Advanced Amine Solvent Strategies for Efficient CO2 Capture in Post-Combustion Systems

Advanced Amine Solvent Strategies for Efficient CO2 Capture in Post-Combustion Systems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Advanced Amine Solvent Strategies for Efficient CO2 Capture in Post-Combustion Systems Fourie Rochelle, Hamed Hashemi, Paramespri Naidoo, Deresh Ramjugernath This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5385962/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Sep, 2025 Read the published version in International Journal of Environmental Research → Version 1 posted 5 You are reading this latest preprint version Abstract Process simulations serve as an efficient and cost-effective method for evaluating the performance of various amine solvents and their blends in CO₂ capture technologies. This study utilized a performance indicator model to assess the effectiveness of n-methyldiethanolamine (MDEA) and 2-amino-2-methyl-1-propanol (AMP) blends, supplemented with the activator piperazine (PZ). Key parameters such as solvent flow rates, equipment heat duties, and associated costs including energy consumption and carbon taxes were incorporated into the analysis. Among the tested formulations, a binary blend consisting of 25 wt.% AMP and 5 wt.% PZ demonstrated superior performance, achieving a 35% improvement over the baseline 30 wt.% AMP solvent. Further enhancement was realized through the implementation of absorber intercooling (ICA) within the simulation design, which elevated the performance rating by an additional 9%. These results highlight the significant potential of optimizing solvent compositions and incorporating innovative process configurations to improve the efficiency of post-combustion CO₂ capture systems. The findings provide valuable insights for developing more effective and sustainable approaches to reducing carbon emissions in large-scale industrial applications. CO2 capture Performance Post-combustion Amines Asorption Process simulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Highlights Performance indicator model is used for CO 2 capture solvent screening Assessment of the amine-based solutions for CO 2 capture is performed MDEA and AMP in different blends along with the activator PZ are used 25 wt.% AMP + 5 wt.% PZ + 70 wt.% H 2 O is the best performing solvent ICA process configuration improves the rating of conventional configuration 1. Introduction Carbon capture, storage, utilization, and sequestration (CCUS) are being pursued aggressively globally to meet the COP26 goals of carbon neutrality by 2050. This is a short-term solution to keep global warming to below 1.5 o C (Hansen et al., 2008; Zhang et al., 2017). With power stations and refineries being the biggest emitter of CO 2 , the capture thereof, especially from power stations, is a major concern since power generation traditionally relies on the burning of carbonaceous fuels. With much attention on the green economy, in the transition to greener and renewable fuels the capture of CO 2 is necessary. Furthermore, in hard to abate sectors, carbon capture technologies will be a requirement. Therefore it is not possible to replace power requirements with alternate renewable electricity generation immediately (Vortmeyer et al., 2013). A diversified energy eco-system is necessary however CCUS is important during the transition away from fossil fuel use. A growing interest exists in the use of carbon dioxide as a resource in this energy mix. A large body of information exists in literature on the modifications and optimization of existing capture materials for carbon dioxide from flue gas/stack gas. Such materials include absorbents, adsorbents, such as alkanolamines, zeolites, ionic liquids, amine-grafted silicas, carbonaceous adsorbents, and metal organic frameworks. Applications covering gas hydrates, membranes, and biofixation approaches are discussed in the literature for CO 2 capture. Despite the significant volume of research work, industries still seek solution strategies and techniques for the upgrading of current methods (D'Alessandro et al., 2010; Yang et al., 2017). Absorption using alkanolamines and its blends are the focus of this study via process simulations. Three main CO 2 capture methods have been proposed and implemented in various industries to date, namely pre combustion, post-combustion and oxy fuel combustion (Nwaoha et al., 2017). In this study, post-combustion capture (PCC) was applied since this would be the most cost-effective method for retrofitting to current power plants, as opposed to pre-combustion and oxy-fuel combustion technologies which do not possess this benefit (Kanniche et al., 2017). Furthermore, due to the comparatively low CO 2 content in the gas stream, this can be removed more efficiency via chemical than physical methods, as reported by various authors when comparing carbon capture technologies (Kanniche et al., 2010; Kanniche et al., 2017; Mondal et al., 2012). This investigation considered a fossil fueled power plant, where the PCC plant is positioned after the main boiler. Therefore, the power production is not affected in the event of changes or malfunctioning in the carbon capture section of the plant. The inclusion of a PCC plant necessitates considerable capital investment with its operation consuming approximately 20% of the power plant’s energy output (Gammer, 2016). This high energy demand is due to inevitable energy losses during process operations, especially at larger scale. These findings support the need to improve PCC technology (Gammer, 2016). A range of processing methods are adopted in carbon capture operations. These include absorption (generally applied in post- and pre-combustion), adsorption, cryogenic distillation, membranes, gas hydrates and chemical looping, with the latter applicable to pre- and oxyfuel-combustion) (D'Alessandro et al., 2010; Yang et al., 2017). Absorption, more specifically chemical absorption via amine-based solvents, is central to this study. Chemical absorption studies covering a vast range of amine solvents have been reported for the capture of CO 2 . Popular amines include 2-amino-2-methyl-1-propanol (AMP), diethanolamine (DEA), N-methyldiethanolamine (MDEA), monoethanolamine (MEA) and piperazine (PZ), whose structures are also shown in Fig. 1 . There are a number of studies in the literature which have considered blends of either MDEA or AMP with PZ in different concentrations (Ali and Aroua, 2004; Brúder et al., 2011; Dash and Bandyopadhyay, 2016; Dash et al., 2011; Dash et al., 2012; Derks, 2006; Li et al., 2013; Liu et al., 1999; Tong et al., 2013; Wong et al., 2014; Yang et al., 2010). These amine-based solvents, and blends thereof, were selected for the simulation studies. In addition, many CO 2 capture simulation studies report the use of MEA as a solvent, with the sole objective being process optimization and process modifications (Freguia and Rochelle (2003), Fisher et al. (2005), Abu-Zahra et al. (2007), Han et al. (2011), and Arachchige and Melaaen (2012)). In other studies, by Fisher et al. (2007), Kothandaraman et al. (2009), Lee et al. (2009), Chavarro Montenegro (2011), Molina and Bouallou (2013), Naskar et al. (2013), Yakub et al. (2014) and Erfani et al. (2015) the performance of MEA was compared to solvents such as diethanolamine (DEA), methyldiethanolamine (MDEA), AMP, piperazine (PZ) and various combinations of blends using these amines. In using pilot plant data with MEA as the solvent, the results by (Øi, 2007) demonstrated the efficiency in the addition of a capture plant to a parent power plant. Others have compared the simulation software data results to the pilot plant results (Aliabad and Mirzaei, 2009; Luo et al., 2009; Mirzaei et al., 2009), including model development for improved CO 2 capture representation (Abu-Zahra et al., 2012; Ahmadi, 2012; Lim et al., 2013; Lin et al., 2016; Øi, 2012; Zhang et al., 2009) and a techno economic analysis of a CO 2 capture plant (Li and Liang, 2012; Razi et al., 2013). Process simulation studies by Adeosun and Abu-Zahra (2013), Daya (2017), Erfani et al., (2015), Fisher et al., (2007), Molina and Bouallou (2013), and Padurean et al., (2011), compared the performance of solvent blends to pure aqueous solvents or solvent blends. Jones et al. (2013) evaluated an aqueous tri-amine blend of MEA, MDEA and AMP, which to the best of our knowledge appears to be a novel study via process simulations involving an aqueous tri-amine blend. Using the basis of a PCC plant, process simulations were performed using Aspen Plus® V8.8 software, to evaluate and benchmark aqueous amine solvents and their blends against a 30 wt. % AMP solution to achieve a rate of 90% carbon capture. This is a continuation of the work in applying a Performance Indicator Model (PIM), which was developed by Daya (2017), who had selected MEA as the solvent basis. For reasons explained later in the article, AMP was selected as the basis based on literature findings. The PIM is used to evaluate the performance of aqueous amine blends consisting of binary combinations of MDEA or AMP with PZ as an activator, in different concentrations. To enable a comprehensive evaluation tool, the inputs to this performance indicator model included the key parameters such as primarily solvent flow rates and equipment heat duties, calculated using the ASPEN Plus® process simulation designs. Other significant inputs to the model involved costs based on energy requirements, make-up flows, and carbon taxes. The usefulness of such a model provides insight into the viability of a large-scale process and enables the selection of promising solvent or blends without expensive experimental efforts. 2. Post-combustion CO capture simulations and data In the Aspen Plus simulations, the Electrolyte Non-Random Two-Liquid (eNRTL) model was employed to represent the aqueous mixed electrolyte system, whilst the Peng-Robinson equation of state (PREoS) and Boston-Mathias alpha function was used to represent the gas phase. 2.1. System chemistry and kinetics The reactions between amines with CO 2 takes place in the absorber resulting in the formation of the intermediate compounds. The absorption reaction is then reversed in the stripper to release the absorbed CO 2 . The reactions mentioned below were considered for the absorption process using amines (AspenTech., 2015b): CO 2 + 2H 2 O ↔H𝐶𝑂 3 − +𝐻 3 𝑂 + (1) 2𝐻𝑂 ↔𝐻𝑂+𝑂𝐻 (2) 𝐻𝐶𝑂 3 − +𝐻 2 𝑂↔𝐶𝑂 3 2− +𝐻3𝑂 + (3) 𝑀𝐸𝐴 + +𝐻 2 𝑂↔𝑀𝐸𝐴+𝐻 3 𝑂 + (4) 𝑀𝐸𝐴𝐶𝑂𝑂 − +𝐻 2 𝑂↔𝑀𝐸𝐴+ 𝐻𝐶𝑂 3 − (5) 𝑀𝐷𝐸𝐴 + +𝐻 2 𝑂↔𝑀𝐷𝐸𝐴+𝐻 3 𝑂 + (6) 𝐴𝑀𝑃 + +𝐻 2 𝑂↔𝐴𝑀𝑃+𝐻 3 𝑂 + (7) 𝑃𝑍𝐻 + +𝐻 2 𝑂↔𝑃𝑍+𝐻 3 𝑂 + (8) 𝑃𝑍+𝐶𝑂 2 +𝐻 2 𝑂↔𝑃𝑍𝐶𝑂𝑂 − +𝐻 3 𝑂 + (9) 𝐻𝑃𝑍𝐶𝑂𝑂+𝐻 2 𝑂↔𝑃𝑍𝐶𝑂𝑂 − +𝐻 3 𝑂 + (10) 𝑃𝑍𝐶𝑂𝑂 − +𝐻𝐶𝑂 3 − ↔PZ(𝐶𝑂𝑂 − ) 2 +𝐻 2 𝑂 (11) In order to define the equations describing the equilibrium constants of the abovementioned reactions in Aspen Plus ® the following equation was used (temperature is in Kelvin): ln(𝐾 𝑒𝑞 )=𝐴+𝐵/𝑇+𝐶ln(𝑇)+𝐷𝑇 (12) in which the equilibrium constants A , B , C and D are represented in Table 1 . Table 1 Equilibrium constants used in the simulation. Reaction A B C D Ref 1 231.465 -12092.1 -36.7816 0 AspenTech. (2015a) 2 132.899 -13445.9 -22.4773 0 Austgen et al. (1991) 3 216.049 -12431.7 -35.4819 0 Austgen et al. (1991) 4 -3.03833 -7008.36 0 -0.00313489 Austgen et al. (1991) 5 -0.52135 -2545.53 0 0 Austgen et al. (1991) 6 -9.4165 -4234.98 0 0 AspenTech. (2015a); 7 -3.68672 -6754.69 0 0 Dash et al. (2011) 8 -62.28 -2564 6.787 0 AspenTech. (2015a); 9 466.497 1614.5 -97.54 0.2471 Dash et al. (2012) 10 6.822 -6066.9 -2.29 0.0036 Dash et al. (2011) 11 -11.563 1769.4 -1.467 0.0024 Dash et al. (2011) 2.2. Flowsheet setup The CO 2 capture system has inherent convergence issues due to its ionic nature and the presence of chemical reactions within the absorber and stripper. It was thus decided to model the closed-loop system with an open loop (where the recycle stream is not connected to the absorber), to simplify the simulation calculations. To achieve the desired CO 2 capture rate of 90% whilst maintaining commercial sizes for the separation columns, four parallel trains were used in the capture section of the flowsheet. Figure 2 . shows the process flow diagram for post-combustion CO 2 capture using absorption. This consists of three main units, including cooling and compression of the flue gas, CO 2 absorption and solvent regeneration, and CO 2 compression which are explained in detail in the next sections (Kothandaraman, 2010). 2.2.1. Flue gas compression and cooling section Figure 2 shows a direct contact cooler (DCC) which operates at the temperature of the absorber (40°C), a blower for the flue gas compression and cooling section to adjust the flue gas temperature to the operating conditions prior it enters the absorber. It is well established that DCC has great advantages over indirect coolers. For example, it requires less cooling water, lower capital and operating costs, and it has a lower pressure drop (Kothandaraman, 2010). Table 2 presents the specifications of the flue gas fed to the blower. The same composition of the flue gas as used by Khalil and Gerbino (2007) was applied in this study. Insignificant impurities such as sulphur trioxide (SO 3 ), hydrochloric acid (HCl) and nitrogen dioxide (NO 2 ) with neglectable concentrations were ignored and the compositions of the remaining compounds were normalized. Table 2 Properties of flue gas from a coal-fired power plant, used in this study. Flow Rate (ton/hr) 2516 Temperature (°C) 125 Pressure (kPa) 101.33 Composition (mole fraction) Nitrogen (N 2 ) 0.73470 Oxygen (O 2 ) 0.05512 Water vapour (H 2 O) 0.07975 Carbon Dioxide (CO 2 ) 0.12010 Argon (Ar) 0.00877 Nitrous Oxide (NO) 0.00030 Sulphur Dioxide (SO 2 ) 0.00126 A RadFrac column was utilized within the simulation of the DCC in Aspen Plus® for which the column specifications are presented in Table 3 . In the simulation, the blower provided a pressure equal to 111.25 kPa. Table 3 Specifications of the direct contact cooler. Calculation type Equilibrium No. of stages 10 Process stream inlet temperature (°C) 134 Process stream outlet temperature (°C) 42 Height (m) 5 Condenser None Reboiler None 2.2.2. CO 2 capture section This vital part of the CO 2 capture plant is comprised mainly of two pieces of equipment for CO 2 capture i.e., the absorber and stripper columns. Typically, a packed column is selected for the absorber in which the flue gas and the lean amine solvent enter from the bottom and the top, respectively. The addition of a water wash section at the top of the column is recommended by Li et al. (2016) in order to cool and clean the vent gas (Li et al., 2016a). The simulation of the absorber was performed using a RadFrac column. The specifications of the four identical absorbers, connected in parallel are given in Table 4 . Table 4 Specifications of the absorber. Section 1 Section 2 Wash water section Capture Section Calculation type Equilibrium Equilibrium No. of stages 2 20 Top Pressure (kPa) 105 - Height (m) 2 20 Diameter (m) 12 12 Condenser None - Reboiler - None Similar to the absorber, a packed column is also used for the stripper which is mainly responsible for the separation of the captured CO 2 from the rich amine solvent. The operational pressure of the stripper was set at 1.8 atm. Basically, the stripper utilizes the heat provided by the steam in the kettle reboiler to reverse whatever reaction occurs in the absorber resulting in the production of the lean solvent at the bottom of the column and a stream mainly containing CO 2 and H 2 O at the top. The regenerated solvent exits from the bottom and is recycled to the absorber (Fisher et al., 2007; Kothandaraman, 2010). A RadFrac column was also employed to simulate four identical strippers, connected in parallel for which the column specifications are given in Table 5 . Table 5 Specifications of the stripper Calculation type Equilibrium No. of stages 21 Top Pressure (kPa) 200 Height (m) 17 Diameter (m) 14 Condenser Partial Reboiler Kettle In order to provide a higher operational pressure in the stripper, a pump is included after the absorber. Furthermore, the rich amine solvent with elevated pressure prevents corrosive acid gas release which can cause fatal damage to the vital equipment and piping (Fisher et al., 2005). Similarly, for the abovementioned reasons a pump is also placed after the stripper to recycle the lean amine solvent to the absorber. In an energy-conservation approach, prior to the regeneration of the amine solvent in the stripper the rich amine from the absorber is pre-heated using the hot lean amine from the stripper reboiler. The temperature of the rich amine is increased to approximately 110 ˚C, concerning a 10˚C temperature approach on the hot side of the heat exchanger (Fisher et al., 2005). Further cooling of the lean amine is achieved by placing a cooler in the recycle loop in order to bring the lean amine temperature to the absorber operating temperature (± 40°C) (Fisher et al., 2005). 2.2.3. CO 2 compression train In order to compress the CO 2 product stream prior to it being sent for storage, a 4-stage reciprocating compressor, with inter-stage cooling and maximum compression of about 9 MPa, is usually utilized. The resulting supercritical liquid CO 2 is subsequently pumped to the necessary discharge pressure of about 13 MPa (Kothandaraman, 2010). The compression of the captured CO 2 in the simulation in this study is performed using a sequence of three compressors with inter-stage cooling and CO 2 separation from the condensed H 2 O. The resultant pressures achieved from each compressor are 430 kPa, 1.9 MPa and 8 MPa, respectively. The supercritical liquid CO 2 is then pumped to the pressure of 11 MPa. 2.3. Performance indicator model The evaluation of the performance of the amine solvents considered in this study for CO 2 capture is carried out by entering the simulation results into a performance indicator model (PIM). In this model, the parameters including solvent make-up, cooling water and make-up water, steam, corrosion inhibitor, amine reclaim and disposal, and carbon taxes, were selected as inputs to the model on a cost basis. As indicated in a previous study by Daya (2017) it is more suitable to base the PIM model on the cost of CO 2 avoided, instead of the cost of CO 2 captured since in some cases capturing CO 2 increases the CO 2 emissions. For example, in power plants since the power required for the capture process usually comes from the plant itself the efficiency of the plant decreases resulting in more fuel combusted by the plant to meet the electrical load. A conceptual illustration of CO 2 captured vs CO 2 avoided is represented in Fig. 3 . Since solvent regeneration is an energy-intensive approach that contributes nearly two-thirds of the operating cost of the CO 2 capture plant, for evaluation of the solvent performance many researchers merely focus on the energy consideration (Khalil and Gerbino, 2007). However, in the model used in this study, the many other factors that influence the operating costs were considered and are discussed in the following sections. Further details of how the costs for all of these factors were calculated can be found in Fourie (2018). 2.3.1. Amine type The influence of amine type on the model (besides variation in costs between different amines) is due to the variation of the solvent flow rate necessary to absorb the required amount of CO 2 when amine type is varied. The prices for the amines investigated in this study are given in Table 6 . The prices from different sources were averaged to obtain a single representative cost. In the case of outdated values, in order to get more updated prices, a ratio of the chemical consumer index of the source year and the current year was utilized. Table 6 Prices of the amines studied. Amine Price (R/ton)* Reference(s) AMP 88374 Eachus and Bollmeier (2000); Zauba Technologies & Data (2016); Daya (2017) MDEA 58556 Kohl and Nielsen (1997); Zauba Technologies & Data (2016) MEA 39271 Kohl and Nielsen (1997); Sinnott (2005) PZ 75880 Sridhar and Carter (2000); Sigma-Aldrich (2016); Zauba Technologies & Data (2016) *The prices cited are for 2016 2.3.2. Amine degradation rates Amine losses due to amine degradation have a direct effect on the operating cost of the capture plant. However, the diversity of the factors and parameters involved in the degradation reactions makes it too complicated to be used in the simulations (Fytianos et al., 2016). Hence, to take the amine degradation into consideration a general degradation model was used in this study, which was incorporated outside the simulation work. Further details on the inclusion of the reclamation into the model are explained in Fourie (2018). As illustrated in the literature the contributors to degradation which directly influence the process streams are oxidative and thermal degradation rates. Hence, in this study these rates were utilized to represent the amines degradation (Shao and Stangeland, 2009). For MEA, MDEA and AMP the degradation rates were selected from the data published by Lepaumier et al. (2009a), Lepaumier et al. (2009b) and Lepaumier et al. (2009c) which are reported in Table 7 . The values reported are based on the fraction of amine degraded per hour and are the summation of oxidative and thermal degradation contributions in the presence of both O 2 and CO 2 . In the case of PZ, as illustrated by Freeman et al. (2010), this does not degrade at temperatures below 140°C, and since the equipment in which PZ exists, operate at lower temperatures, the degradation of PZ was ignored in the model. Table 7 Degradation rates of the amines studied * . * (Lepaumier et al., 2009a) (Lepaumier et al., 2009b) (Lepaumier et al., 2009c) Amine Combined degradation rate (%/hr) MEA 0.3137 AMP 0.0739 MDEA 0.0970 PZ Assumed negligible Another important cause of degradation is the presence of a trace amount of impurities in the flue gas stream such as NO x , SO 2 , fly ash and NH 3 . In order to purify the system from these degradation products as well as any other undesirable impurities such as sludge, a reclaimer unit is placed after the reboiler of the stripper (Rochelle et al., 2011). The reclaimer unit was not included in the simulations due to the complexity of the degradation reactions mechanism and diversity of the degradation products. Instead, the contributions of these reclamation costs, estimated as a fractional loss of the recirculating solvent rate, as well as the disposal costs due to amine degradation were calculated manually using an excel spreadsheet along with the other PIM calculations (Fourie 2018). 2.3.3. Corrosion Usually, the absorption reaction of CO 2 with amines leads to the production of several highly corrosive chemicals which can cause fatal damage to the process equipment. One solution to this problem is the application of corrosion inhibitors such as sodium metavanadate and copper carbonate (Veawab et al., 2001). In this study, the same corrosion inhibitors were employed, and the cost was calculated from a fraction of the recirculating solvent rate (Fourie 2018). 2.3.4. Energy consumption The energy consumption in a CO 2 capture plant can be divided into three major uses: electrical power consumption, steam usage and water usage. The electrical consumption is mainly due to the flue gas blower, CO 2 compressors and all pumps in the process. In this study the energy required in the capture plant is provided by the power plant, consequently, the developed performance model is based on the “cost of CO 2 avoided”. In the capture plant, the only equipment that consumes steam is the stripper reboiler and the compressors which were modeled to be electrically driven. The amount of steam consumed by the stripper is directly proportional to the value of the heat duty calculated using the simulations and it was estimated by dividing the heat duty by the steam heat of vaporization. Direct contact cooler (DCC), stripper condenser, CO 2 compression intercoolers and the lean amine cooler are the major equipment that consume cooling water. For stripper condenser and CO 2 compression intercoolers the amount of cooling water required was obtained using the duties in the simulations while, the flow of cooling water through the DCC was directly obtained from the simulation. In order to preserve the composition of aqueous solvent the addition of make-up water to the system is also necessary. 2.3.5. Carbon tax The levy imposed on the release of CO 2 by the industrial facilities is referred to as carbon tax which is a form of pollution tax. This levy serves to encourage companies to reduce their CO 2 emissions. The price of carbon tax has been set to $ 10/ton with a possible 10% yearly increase between 2016 and 2019 (The World Bank, 2014). In this study the rate of carbon capture was fixed, therefore the value of carbon tax allocated for each solvent blend stays constant. 2.4. Model inputs and outputs The PIM developed in this study requires two types of inputs, viz. user-defined inputs and result inputs. User-defined inputs can be changed by user. In Table 8 the user-defined inputs are divided into two groups “inputs into Aspen” and “external inputs”. The result inputs, which are basically the outputs from the Aspen simulation, are identified as “inputs into PIM from Aspen”. The ultimate output achieved from the performance model is the rating of a specific amine solvent or blend relative to an identified baseline solvent. Table 8 The various inputs required to use the performance indicator model (PIM). Inputs into Aspen Inputs into PIM from Aspen External inputs into PIM Flue gas flow rate Cooler duties Amine price(s) Flue gas composition Stripper condenser duty Make-up water price Solvent composition Direct cooler water flow Cooling water price CO 2 capture rate Stripper reboiler duty Steam price Lean solvent flow from stripper Corrosion inhibitor price Amine flow(s) into absorber Amine reclaim cost Compressor power required Amine disposal cost Pumping power required Carbon tax rate Power required for blower Amine degradation rate(s) Amine make-up flow(s) Power plant efficiency Water make-up flow CO 2 flow into process 2.5. Model implementation In this section the determination of the rating with the PIM is explained in detail. For the evaluation of the overall cost for CO 2 captured ( C T,j,captured ) the summation of each factor i for each case j is used. Generally, the cost factors are equivalent to the product of price of the process chemicals or utilities and their corresponding flow rates. $$\:{C}_{T,j,captured}={\sum\:}_{k=0}^{n}{C}_{ij}$$ 13 The total cost of CO 2 avoided is obtained using the following equation: $$\:{{C}_{T,j,avoided}=C}_{T,j,captured}\times\:\frac{{\epsilon\:}_{OP}}{{\epsilon\:}_{j}}$$ 14 in which ε OP represents the original operating efficiency of the power plant without the capture plant and ( ε j ) is the reduced efficiency of the power plant in the presence of the capture plant which is dependent on the solvent used in case j . The following equation is then used to obtain the rating for each case j , R, in which C i,b,avoided and C i,j,avoided represent the avoided costs of the base case and case j , for each factor i respectively. $$\:R={\sum\:}_{i}^{n}{x}_{i,j}\times\:\frac{{C}_{i,b,avoided}}{{C}_{i,j,avoided}}$$ 15 In Eq. ( 15 ), x ij , is the factor fraction which is obtained by dividing the cost of the factor i for case j by the total cost of CO 2 avoided for case j . $$\:{x}_{ij}=\frac{{C}_{i,j,avoided}}{{C}_{T,i,j,avoided}}$$ 16 In Eq. ( 16 ), x ij can be used as an indicator to compare each case to the benchmark case. If a lower cost avoided calculated for case j than that of the benchmark case, it suggests a superior performance of the solvent in case j in the simulation of post-combustion CO 2 capture. For the solvents that are more cost-effective as determined by equations 15 and 16 , a greater rating value (than the benchmark with R is equal to 1) would be obtained if multiplying the ratio of cost avoided calculated for case j and the benchmark by x ij . Consequently, for the less cost-effective solvents than the benchmark, a rating value of less than one would be obtained. All the factors included in the PIM are associated to the operating cost of a CO 2 capture plant. Since the capital costs are not included in the model, in the solvent assessment it is required that equipment sizes remain unchanged during all the simulation cases being evaluated. In a practical condition, this would be equivalent to varying the solvent of an existing capture installation to establish whether it enhances the performance of the plant. 3. Results and discussion 3.1. Test systems In this study, an aqueous amine solvent of MEA with a concentration of 30 wt.% was used in a simulation as the benchmark case for which the main inputs to the PIM are given in Table 9 . Table 9 Main inputs to the performance model for the base case, 30% MEA Parameter Unit Value Amine Price R/ton 39271 a Make-up Water Price R/ton 11.52 b Cooling Tower Water Price R/ton 0.54 b Steam Price R/ton 150 b Corrosion Inhibitor Price R/ton 3784 b Amine Reclaim Cost R/ton 10078 b Amine Disposal Cost R/ton 3008 b Carbon Tax Rate R/ton 120 c Amine Degradation Rates %/hr 0.3137 d Power Plant Efficiency % 42.4 e a Kohl and Nielsen (1997); Sinnott (2005) b Daya (2017) c The World Bank (2014) d Lepaumier et al. (2009a); Lepaumier et al. (2009b); Lepaumier et al. (2009c) e Gammer (2016) When using the PIM to analyze the results, the optimum operating point (the point at which the different costs combine to form the lowest total CO 2 capture cost and thus the highest rating) must be found. To determine the optimum operating cost, the simulation was performed for a range of lean solvent loadings and the loading which resulted in the highest overall rating as determined by the PIM is then the optimum point for that solvent. This method was used for all solvent blends considered. The validation of the performance indicator model is achieved by comparing the main results of the benchmark using the 30% MEA process simulation to those in the literature. Table 10 shows this comparison. For this purpose, the energy requirements were noted and the results obtained in this study as well as those from the literature indicate that the energy required is affected directly by the number of employed trains (hence number of absorbers and strippers). For example the energy required for CO 2 capture process using MEA is between 3 to 4.5 GJ/(ton CO 2 ) (Zahra, 2009). While, as shown in Table 10 the energy requirement reported by researchers who used multiple equipment trains is similar to the amount in this range multiplied, though by the number of trains. Table 10 Comparative analysis between the 30% MEA benchmarks established in this study and those reported in the literature. This study (80% capture) This study (90% capture) Fisher et al. (2007) Kothandaraman (2010) Padurean et al. (2011) Daya (2017) Open/ closed Process Open Open Closed Open Closed Open a Solvent Lean Loading (mol CO 2 /mol amine) 0.21 0.14 b n/s 0.22 b n/s 0.18 a Solvent Flowrate (ton/hr) 1484 1786 24237 b n/s 3500 2073 Flue Gas CO 2 Content (mol %) 12 12 12.38 b n/s 8.4 12 Flue Gas Flow Rate (ton/hr) 2283 2283 2448 b n/s 2928 2516 CO 2 capture rate (%) 80 90 90 85 90.3 80 Total Energy Requirement (GJ/t CO 2 ) 12.4 19.6 16.82 16.61 3.29 13.00 Stripper diameter (m) 14 14 7.9 7 b n/s 14 No. of trains 4 4 4 4 1 4 Energy Required per Stripper (GJ/t CO 2 ) 3.09 4.9 4.21 4.15 3.29 3.25 a All sources referenced in this table use 30 wt.% MEA as a solvent b not specified by source As shown in Table 10 , for the benchmark system (30% MEA, 80% CO 2 capture), the energy required per reboiler unit is equal to 3.09 GJ/ ton CO 2 . This is 5% less than the value obtained by Daya (2017), using very similar conditions. This was deemed as an acceptable difference. By adjusting the capture rate (30% MEA, 90% capture) and comparing the result to the AMP system to the literature the energy required per reboiler unit is 4.9 GJ/ ton CO 2 . This is 16% higher than the value obtained by Fisher et al. (2007), with conditions and inputs being very quite similar, but still deemed as an acceptable difference. Not all simulation conditions can be identical, especially when there exists a great difference in the column diameters used in different simulations. It is worth mentioning that in this study open-loop simulations were used while Fisher et al. (2007) used closed-loop simulations. However, the discrepancy in the results of open-loop and closed-loop processes are not always high. For example, as indicated in Table 10 , the percentage difference between the energy requirements obtained in the work of Fisher et al. (2007), with closed-loop, and Kothandaraman (2010), with open-loop, is less than 2%. This and a study by Ahmadi (2012) confirm that the results obtained in this study which are based on open-loop process simulations are equivalent to the results achieved using simulation of closed-loop processes. The output of the simulation using 30% AMP as the solvent, with the benchmark as 30% MEA alongside the results obtained by Daya (2017) and Padurean et al. (2011) are depicted in Fig. 4 . The preliminary rating obtained in this study was not exactly the same as the ratings calculated by the other two references. After performing a sensitivity analysis, the range of (0.932–1.151) was obtained as a probable range of ratings for which the upper limit is placed within the range of the literature values (refer to error bars in Fig. 4 ). Hence, it is confirmed that the rating results for the 30% AMP simulation were satisfactory, and this case was utilized as the benchmark for the investigations of the study. Table 11 shows a comparison between 30% AMP benchmark of this study to simulations for 30% AMP obtained in the literature. Table 11 Comparative analysis between the AMP benchmark adopted in this investigation and similar AMP simulation studies documented in existing literature This work Padurean et al. (2011) Lee et al. (2009) Chavarro Montenegro (2011) Li et al. (2016b) Solvent Lean Loading (mol CO 2 /mol amine) 0.135 n/s a n/s n/s 0.33 Solvent Flowrate (ton/hr) 2383 n/s 0.974 n/s n/s Flue Gas CO 2 Content (mol %) 12 8.4 8.34 12.57 13.3 Flue Gas Flow Rate (ton/hr) 2283 2928 0.563 324 3122 CO 2 capture rate (%) 90 93.8 95 90 90 Total Energy Requirement (GJ/t CO 2 ) 11.6 2.82 8.76 8 2.295 Stripper diameter (m) 14 n/s n/s 7 n/s No. of trains b 4 1 1 1 1 a not specified by source b If number of trains were not specified, it was assumed to be 1 It can be interpreted from the solvent and flue gas flow rates in Table 11 that the work carried out by Lee et al. (2009) and Chavarro Montenegro (2011) was performed on a smaller scale than this work. The comparatively large energy requirements obtained in both studies confirm that the results are highly dependent on the scale at which the simulations performed. Excluding these two sources, a maximum deviation of about 26% between this work and literature is obtained. (considering 2.9 GJ/ ton CO 2 for the energy requirement per reboiler in this study) with an average percentage deviation of 14.5%. The deviations obtained are satisfactory since not all the conditions are the same as used in literature. 3.2. New systems In this study several blends of the amines, i.e., AMP, MDEA and PZ were selected as the new systems in the CO 2 capture simulations. In order to examine the effect of increasing the amine concentration on the rating, binary solvent blends with total amine concentrations of 30% and 40% were simulated. Within each scenario, the lean loading of the solvent (mol CO 2 / mol amine) was varied to find the optimum operating point (where the rating was highest). Subsequently, the resultant conditions became the demonstrative case for that particular amine blend, as shown in Fig. 5 .The rating for the benchmark case is set to 1, based on the output from the PIM. As indicated previously, in the PIM calculations the ratings above one are considered as a superior performance to the benchmark whilst the ratings below one are considered inferior to the benchmark. The 30% AMP aqueous solution was applied as the benchmark for all cases. Table 12 shows results for all the systems considered in this study. One can observe from the results shown in Table 12 that even though the reboiler duty and solvent flow rate encompass a great portion of the ratings obtained by the PIM, the best rating is not always obtained when the reboiler duty and solvent flow rate are minimum. Experimental investigations show that the solubility of CO 2 in the solvents considered in this study increases in the order MDEA < AMP < PZ. A similar trend was also observed in the flowrates reported for the different amine blends in which the flowrate decreased with decreasing solvents capacity for CO 2 capture. For example, the blends of MDEA + PZ show flow rates considerably higher than that of the AMP + PZ blends. In the case of the tri-amine blends since all three components exist, flow rates lie in between. Solvent flow rate and the reaction rate of amine blend with CO 2 (which also follows the trend PZ > AMP > MDEA) are also directly related. Consequently, it is expected that for a fixed capture rate the solvents with a higher rate of reaction with CO 2 show a lower required flow rate as presented in Table 13 . Table 12 An overview of the outcomes achieved for the analyzed configurations in this study. System Lean Loading (mol CO 2 / mol Amine) Rating Reboiler Duty (MW) Solvent Flow Rate (ton/hr) 30% AMP 0.135 1.000 296 2323 25% MDEA + 5% PZ 0.150 1.030 122 3778 22% MDEA + 8% PZ 0.215 1.004 148 3893 35% MDEA + 5% PZ 0.110 1.080 205 3345 32% MDEA + 8% PZ 0.160 1.052 147 3446 28% AMP + 2% PZ 0.170 1.270 164 1874 25% AMP + 5% PZ 0.210 1.359 136 1859 38% AMP + 2% PZ 0.170 1.120 130 1863 30% AMP + 10% PZ 0.250 1.212 91 1936 25% MDEA + 5% AMP + 5% PZ 0.125 1.220 116 2858 25% MDEA + 10% AMP + 5% PZ 0.110 1.269 107 2473 25% MDEA + 10% AMP + 10% PZ 0.177 1.228 95 2641 Table 13 A summary of the simulations conducted for the intercooled absorber under various operating conditions. *Convergence errors Split Fraction Entering stage Exiting Stage Stripper Reboiler Duty (MW) Intercooler Duty (MW) Solvent Flow Rate (ton/hr) Temperature Difference in Intercooler (°C) Estimated Rating Case 1 0.20 20 12 142.6 -6.255 1716 19.09 1.431 Case 2 0.40 20 12 139.9 -10.48 1684 16.31 1.487 Case 3 0.55 20 12 139.3 -12.60 1679 13.66 1.509 Case 4 0.60 20 12 139.4 -13.12 1680 13.66 1.509 Case 5 0.20 17 9 142.1 -6.977 1709 21.36 1.463 Case 6 0.40 17 9 138.8 -11.51 1668 18.07 1.546 Case 7 0.55 17 9 137.8 -13.72 1656 15.79 1.577 Case 8 0.60 17 9 137.7 -14.26 1655 15.06 1.578 Case 9 0.20 17 17 141.3 -3.783 1694 11.76 1.413 Case 10 0.20 15 15 142.9 -4.581 1720 14.00 1.429 Case 11 0.30 15 15 141.2 -5.953 1700 12.28 1.453 Case 12 0.40 15 15 139.9 -6.977 1684 10.90 1.471 Amongst the amine blends considered in this study, MDEA + PZ blends did not show very promising ratings, which is mostly due to the ternary structure of MDEA which hinders its reaction with CO 2 resulting in a higher solvent flowrate. CO 2 solubility data confirms that the solubility of CO 2 increases with increasing the PZ concentration in the blend (Dash and Bandyopadhyay, 2016) which is also observed in the solvent flow rate trends (see Table 13 ). The reclaim and disposal costs of the amines are estimated in separate calculations based on the flowrates of the solvent. Consequently, a higher flowrate also results in a surge in these costs. Hence, although the energy consumption for the MDEA + PZ blend is lower than the benchmark, by considering the additional factors mentioned in the calculations, a lower rating for AMP + PZ blends is observed. The combination of the low reboiler duty and low solvent flow rate resulted in the highest ratings overall achieved for the AMP + PZ blends. A relatively low reboiler duty was achieved for the tri-amine blends, which is possibly due to the combination of AMP and MDEA knowing that both have lower heats of reaction with CO 2 (resulting in a reduction in the regeneration duty). Nevertheless, since a large portion of the solvent’s amine concentration is MDEA, a high solvent flowrate leads to the higher costs of solvent make-up and reclaim, resulting in a lower PIM rating. Hence, even though the tri-amine blends showed an outstanding performance with respect to the energy consumption, when including other parameters, these blends were inferior in performance compared to AMP + PZ binary blends (see Table 12 ). 3.3. Modified configuration The ratings reported in Table 12 are obtained based on the conventional process configuration. In order to investigate the effect of the process configuration on the simulation results, a modified process configuration, namely, intercooled absorber (ICA) was simulated in which the best-performing solvent blend (i.e., 25% AMP + 5% PZ + 70% H 2 O wt.% obtained in conventional configuration studies), was used. In the ICA process configuration, a fraction of the solvent in the absorber is withdrawn, cooled, and then fed to the absorber (Le Moullec et al., 2014). Figure 6 depicts an ICA process configuration used in the simulation in which one cooling stage was used. A 22-stage colums (where stages are numbered from top to bottom) was utilized in the simulation amongst which stage 17 was primarily attempted as the cooling stage. After changing the stage number of the cooling stage it was found that stage 15 resulted in an optimal performance of the process. Table 14 shows the results obtained using stage number 15 as well as those obtained using stage number 17 as the cooling stage. Similar results can be observed in both scenarios however, more stable simulations were achieved using stage 15 as the cooling stage. It can be inferred that using the stages below 15 may result in unstable simulation runs which can cause convergence errors. As indicated in Table 13 , different exiting and entering stages were chosen to assess the column performance (cases 1–8). Only one cooling stage was utilized to perform cases 9–12. Even though there are few cases from 1–8 with higher ratings than one cooling stage cases, it can be concluded that implementing one cooling stage would be more practical, therefore the results of cases 1–8 were ignored and presented here merely for illustration purposes. The results from the PIM showed that amongst the cases utilizing stage 17 and 15 as the cooling stage, case 12 (which uses stage 15 as cooling stage) yielded the best rating. Table 14 Additional exploration of employing stage 15 as a cooling stage through an expanded range of split fractions. Split Fraction Cooling Stage Stripper Reboiler Duty (MW) Intercooler Duty (MW) Solvent Flow Rate (ton/hr) Temperature Difference in Intercooler (°C) Estimated Rating 0.2 15 142.9 -4.581 1720 14.00 1.429 0.3 15 141.2 -5.953 1700 12.28 1.453 0.4 15 139.9 -6.977 1684 10.90 1.471 0.5 15 138.8 -7.774 1671 9.80 1.483 0.6 15 138.0 -8.412 1660 8.90 1.483 *Convergence errors In an attempt to obtain higher ratings, the split fraction, as well as the solvent lean loading were increased. For this purpose, as shown in Table 14 firstly the split fraction range was extended to see whether a maximum could be achieved at some point. The results in Table 14 show that the split fractions 0.5 and 0.6 produce a rating of 1.483 which is the highest obtained. Since the split fraction 0.6 did not improve the results significantly, it is decided to select the split fraction 0.5 for further investigations. Ultimately, the results obtained confirm that when using stage 15 as cooling stage, with a split fraction of 0.5 and a solvent lean loading equal to 0.2 mol CO 2 / mol amine, the ICA configuration produces a maximum rating of 1.483 which is an improvement of 9% comparing to that of the conventional configuration (1.359). The simultaneous effect of mitigated energy requirements as well as reduced solvent supplies improved the rating in the ICA configuration which also can affect the cost of other parameters, like waste disposal. This study focused on selected amines and its blends. It is possible to extend the work to other promising amines and blends, and apply the same simulations to assess the performance against those investigated in this work. In addition, alternate process configurations and modifications to the capture unit design are possible. Using a rate-based model instead of an equilibrium model for the simulations could also be considered for future work, especially where equipment design is concerned. These are recommendations for continuation of this work in assessing solvent blends and process modifications for improved carbon capture techniques with energy reductions. Conclusions The performance of the mixture of various aqueous amine solvents for post-combustion CO 2 capture from a coal-fired power plant was examined by means of a performance indicator model. A solvent of 30 wt. % AMP was used as the baseline instead of the preliminary 30 wt. % MEA, since previous investigations carried out have already proven that AMP is a more promising agent for CO 2 capture than MEA. Different blends of aqueous solvent of AMP or MDEA with piperazine (PZ) were assessed, as well as ternary amine aqueous blends with AMP, MDEA, and PZ. In the case of binary amine mixtures, the overall amine concentrations of 30 wt.% and 40 wt.% were assessed, whereas the overall amine concentration of the ternary amine blends were 45 wt.%. It was found that 25% AMP + 5% PZ (wt.%), is the best-performing blend with the highest rating of 1.359. The worst-performing blend was found to be 22% MDEA + 8% PZ, with a rating of 1.004, which was very close to the benchmark solvent of AMP. Up to 5% improvement in ratings was obtained for the MDEA + PZ blends, when increasing the total amine concentration, while for the AMP + PZ blends, it showed a reverse effect on the rating (up to 21% decrease). The ratings for all the binary amines increased with increasing PZ concentration. For the binary blends of AMP + PZ, an increase of 7% and 8% in ratings were obtained for the overall amine concentrations of 30 wt. % and 40 wt. %, respectively. For MDEA + PZ blends, ratings increased by 2.5% and 2.7% for total amine concentrations of 30 wt. % and 40 wt. %, respectively. Higher ratings were obtained for the tri-amine blends (1.220–1.269) comparing to the ratings obtained for MDEA + PZ blends (1.004–1.080) but it is lower than that of AMP + PZ blends with a total amine concentration of 30 wt. % (1.270–1.359). Finally, a modification in the process configuration was made (modified intercooled absorber configuration) in order to improve further the rating obtained for the best-performing blend (i.e. 25% AMP + 5% PZ). In this case, the rating was increased to 1.483 (9% improvement of conventional configuration), when using stage 15 (of a 22 stage RADFRAC column) as cooling stage, with a split fraction of 0.5 and a solvent lean loading equal to 0.2 mol CO 2 / mol amine. 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Wong, M.K., Murshid, G., Bustam, M.A., Tyutyu, S., Shariff, A.M., 2014. Solubility of Carbon Dioxide in Piperazine-activated Methyldiethanolamine and 2-Amino-2-Methyl-1-Propanol. Journal of Applied Sciences 14, 3114–3117. Yakub, M.I., Samah, M., Danladi, S.U., 2014. Technical and Economic Considerations of Post-Combustion Carbon Capture in a Coal Fired Power Plant. International Journal of Advances in Engineering & Technology 7, 1549–1581. Yang, X., Rees, R.J., Conway, W., Puxty, G., Yang, Q., Winkler, D.A., 2017. Computational Modeling and Simulation of CO 2 Capture by Aqueous Amines. Chemical Reviews 117, 9524–9593. Yang, Z.-Y., Soriano, A.N., Caparanga, A.R., Li, M.-H., 2010. Equilibrium solubility of carbon dioxide in (2-amino-2-methyl-1-propanol + piperazine + water). The Journal of Chemical Thermodynamics 42, 659–665. Zahra, M.R.A., 2009. Carbon dioxide capture from flue gas: Development and evaluation of existing and novel process concepts. Technische Universiteit Delft. Zauba Technologies & Data, 2016. Search Import Export Data of India. Zhang, W., Chen, J., Luo, X., Wang, M., 2017. Modelling and process analysis of post-combustion carbon capture with the blend of 2-amino-2-methyl-1-propanol and piperazine. International Journal of Greenhouse Gas Control 63, 37–46. Zhang, Y., Chen, H., Chen, C.-C., Plaza, J.M., Dugas, R., Rochelle, G.T., 2009. Rate-Based Process Modeling Study of CO 2 Capture with Aqueous Monoethanolamine Solution. Industrial & Engineering Chemistry Research 48, 9233–9246. Cite Share Download PDF Status: Published Journal Publication published 05 Sep, 2025 Read the published version in International Journal of Environmental Research → Version 1 posted Editorial decision: Major revisions 30 Apr, 2025 Reviewers agreed at journal 05 Nov, 2024 Reviewers invited by journal 05 Nov, 2024 Editor assigned by journal 05 Nov, 2024 First submitted to journal 03 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5385962","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":374396215,"identity":"5a46ce08-6bf6-4d95-9daf-241f76b5150a","order_by":0,"name":"Fourie Rochelle","email":"","orcid":"","institution":"University of KwaZulu-Natal School of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Fourie","middleName":"","lastName":"Rochelle","suffix":""},{"id":374396216,"identity":"37bb1de4-7c7c-4d57-932d-46c7499a3524","order_by":1,"name":"Hamed Hashemi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYBACAxDBw3CAgUECyEioALJJ1HKGgYegHlQtjG0MhK0xZz+d+OANwx15+dnNRzc8nHdYxp7/AOOHHwx18ri0WPbkbjacw/DMcMOdY2k3Ercd5uGRSGCW7GFgM2zA5bADudukeRgOM26QyDEDarkN1MLAIA10HyNOLeffbv8N1GI/f0b+txuJc4Ba+A8w/wZ6zR6nlhu525iBWhIbbuSw3UhsAGphSGAD2mKQiFvL282ScwwOJ2+4kWZ2I+HYfx6eG4ltlj0GCcm4HZa78cObisO282ckP7v5oybNnr3/8OEbPyrqbHFpgWpE4YE8boBd4SgYBaNgFIwC4gAAB1BbWUDqzzgAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-1986-6923","institution":"University of the Witwatersrand Johannesburg","correspondingAuthor":true,"prefix":"","firstName":"Hamed","middleName":"","lastName":"Hashemi","suffix":""},{"id":374396217,"identity":"257231d9-34b6-45be-8d9e-7201b4b28ab2","order_by":2,"name":"Paramespri Naidoo","email":"","orcid":"","institution":"Stellenbosch University","correspondingAuthor":false,"prefix":"","firstName":"Paramespri","middleName":"","lastName":"Naidoo","suffix":""},{"id":374396218,"identity":"2d5f0a7d-39ca-438f-8c5b-99ac99812ce2","order_by":3,"name":"Deresh Ramjugernath","email":"","orcid":"","institution":"Stellenbosch University","correspondingAuthor":false,"prefix":"","firstName":"Deresh","middleName":"","lastName":"Ramjugernath","suffix":""}],"badges":[],"createdAt":"2024-11-04 07:54:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5385962/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5385962/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s41742-025-00877-6","type":"published","date":"2025-09-05T15:56:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69335691,"identity":"ebe48f18-a9dc-471f-a945-85f4cea4b82f","added_by":"auto","created_at":"2024-11-19 09:54:40","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":95342,"visible":true,"origin":"","legend":"\u003cp\u003eStructures of the most common amines investigated in the literature.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5385962/v1/24c2ec656bd6ac6487522800.jpeg"},{"id":69335692,"identity":"9f9c9085-9541-488c-aaf5-33544d88b313","added_by":"auto","created_at":"2024-11-19 09:54:40","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":328992,"visible":true,"origin":"","legend":"\u003cp\u003eFlowsheet of the Aspen simulation for CO\u003csub\u003e2\u003c/sub\u003e capture by amine absorption.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003eIn an open loop system, the rich/lean heat exchanger present in conventional configurations of a post-combustion CO\u003csub\u003e2\u003c/sub\u003e capture plant must be represented by a separate heater (HEATER) and cooler (COOLER 1). The heat duties of this heater and cooler are equal in magnitude since in reality this is one heat exchanger. A second cooler (COOLER 2) is needed to further cool the recycled lean solvent to the desired temperature of the absorber.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5385962/v1/6a253c4eb036f9031f84e848.jpeg"},{"id":69336196,"identity":"91aee0ca-25f6-418e-b571-41f4837a5037","added_by":"auto","created_at":"2024-11-19 10:02:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":335381,"visible":true,"origin":"","legend":"\u003cp\u003eIllustrative diagram of the CO\u003csub\u003e2\u003c/sub\u003e captured vs CO\u003csub\u003e2\u003c/sub\u003e avoided concept (\u003ca href=\"#_ENREF_12\" title=\"Canadian Clean Power Coalition, 2013 #87\"\u003eCanadian Clean Power Coalition, 2013\u003c/a\u003e)\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5385962/v1/885d84acb29c5d108b7a3614.png"},{"id":69335693,"identity":"9bdf40b7-468d-4b1b-b03d-75d109e590f1","added_by":"auto","created_at":"2024-11-19 09:54:40","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":112535,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the 30% AMP simulation results with the literature.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5385962/v1/6ca6f91805324c002c4ce09a.jpeg"},{"id":69335696,"identity":"b377c61c-bbb0-42fc-8018-c74668e2fccf","added_by":"auto","created_at":"2024-11-19 09:54:40","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":234793,"visible":true,"origin":"","legend":"\u003cp\u003eRatings for the new systems simulated in this work.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5385962/v1/f93bf3ff67e5e67eea3a55c1.jpeg"},{"id":69335695,"identity":"ad2122a7-6df8-4f8e-a721-f5fea614f51e","added_by":"auto","created_at":"2024-11-19 09:54:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":91724,"visible":true,"origin":"","legend":"\u003cp\u003eThe intercooled absorber configuration as applied to the capture section of the flowsheet.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5385962/v1/af86f6c5caea7ad51c42f194.png"},{"id":90827821,"identity":"d5081e4b-d03c-4097-afb1-dfb5901d898e","added_by":"auto","created_at":"2025-09-08 15:59:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2754938,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5385962/v1/0406c760-394a-4a46-be31-eec6a953b9c4.pdf"}],"financialInterests":"","formattedTitle":"Advanced Amine Solvent Strategies for Efficient CO2 Capture in Post-Combustion Systems","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003ePerformance indicator model is used for CO\u003csub\u003e2\u003c/sub\u003e capture solvent screening\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAssessment of the amine-based solutions for CO\u003csub\u003e2\u003c/sub\u003e capture is performed\u003c/li\u003e\n \u003cli\u003eMDEA and AMP in different blends along with the activator PZ are used\u003c/li\u003e\n \u003cli\u003e25 wt.% AMP + 5 wt.% PZ + 70 wt.% H\u003csub\u003e2\u003c/sub\u003eO is the best performing solvent\u003c/li\u003e\n \u003cli\u003eICA process configuration improves the rating of conventional configuration\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eCarbon capture, storage, utilization, and sequestration (CCUS) are being pursued aggressively globally to meet the COP26 goals of carbon neutrality by 2050. This is a short-term solution to keep global warming to below 1.5 \u003csup\u003eo\u003c/sup\u003eC (Hansen et al., 2008; Zhang et al., 2017). With power stations and refineries being the biggest emitter of CO\u003csub\u003e2\u003c/sub\u003e, the capture thereof, especially from power stations, is a major concern since power generation traditionally relies on the burning of carbonaceous fuels. With much attention on the green economy, in the transition to greener and renewable fuels the capture of CO\u003csub\u003e2\u003c/sub\u003e is necessary. Furthermore, in hard to abate sectors, carbon capture technologies will be a requirement. Therefore it is not possible to replace power requirements with alternate renewable electricity generation immediately (Vortmeyer et al., 2013). A diversified energy eco-system is necessary however CCUS is important during the transition away from fossil fuel use. A growing interest exists in the use of carbon dioxide as a resource in this energy mix. A large body of information exists in literature on the modifications and optimization of existing capture materials for carbon dioxide from flue gas/stack gas. Such materials include absorbents, adsorbents, such as alkanolamines, zeolites, ionic liquids, amine-grafted silicas, carbonaceous adsorbents, and metal organic frameworks. Applications covering gas hydrates, membranes, and biofixation approaches are discussed in the literature for CO\u003csub\u003e2\u003c/sub\u003e capture. Despite the significant volume of research work, industries still seek solution strategies and techniques for the upgrading of current methods (D'Alessandro et al., 2010; Yang et al., 2017). Absorption using alkanolamines and its blends are the focus of this study via process simulations.\u003c/p\u003e \u003cp\u003eThree main CO\u003csub\u003e2\u003c/sub\u003e capture methods have been proposed and implemented in various industries to date, namely pre combustion, post-combustion and oxy fuel combustion (Nwaoha et al., 2017). In this study, post-combustion capture (PCC) was applied since this would be the most cost-effective method for retrofitting to current power plants, as opposed to pre-combustion and oxy-fuel combustion technologies which do not possess this benefit (Kanniche et al., 2017). Furthermore, due to the comparatively low CO\u003csub\u003e2\u003c/sub\u003e content in the gas stream, this can be removed more efficiency via chemical than physical methods, as reported by various authors when comparing carbon capture technologies (Kanniche et al., 2010; Kanniche et al., 2017; Mondal et al., 2012).\u003c/p\u003e \u003cp\u003eThis investigation considered a fossil fueled power plant, where the PCC plant is positioned after the main boiler. Therefore, the power production is not affected in the event of changes or malfunctioning in the carbon capture section of the plant. The inclusion of a PCC plant necessitates considerable capital investment with its operation consuming approximately 20% of the power plant\u0026rsquo;s energy output (Gammer, 2016). This high energy demand is due to inevitable energy losses during process operations, especially at larger scale. These findings support the need to improve PCC technology (Gammer, 2016).\u003c/p\u003e \u003cp\u003eA range of processing methods are adopted in carbon capture operations. These include absorption (generally applied in post- and pre-combustion), adsorption, cryogenic distillation, membranes, gas hydrates and chemical looping, with the latter applicable to pre- and oxyfuel-combustion) (D'Alessandro et al., 2010; Yang et al., 2017). Absorption, more specifically chemical absorption via amine-based solvents, is central to this study.\u003c/p\u003e \u003cp\u003eChemical absorption studies covering a vast range of amine solvents have been reported for the capture of CO\u003csub\u003e2\u003c/sub\u003e. Popular amines include 2-amino-2-methyl-1-propanol (AMP), diethanolamine (DEA), N-methyldiethanolamine (MDEA), monoethanolamine (MEA) and piperazine (PZ), whose structures are also shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There are a number of studies in the literature which have considered blends of either MDEA or AMP with PZ in different concentrations (Ali and Aroua, 2004; Br\u0026uacute;der et al., 2011; Dash and Bandyopadhyay, 2016; Dash et al., 2011; Dash et al., 2012; Derks, 2006; Li et al., 2013; Liu et al., 1999; Tong et al., 2013; Wong et al., 2014; Yang et al., 2010). These amine-based solvents, and blends thereof, were selected for the simulation studies.\u003c/p\u003e \u003cp\u003eIn addition, many CO\u003csub\u003e2\u003c/sub\u003e capture simulation studies report the use of MEA as a solvent, with the sole objective being process optimization and process modifications (Freguia and Rochelle (2003), Fisher et al. (2005), Abu-Zahra et al. (2007), Han et al. (2011), and Arachchige and Melaaen (2012)). In other studies, by Fisher et al. (2007), Kothandaraman et al. (2009), Lee et al. (2009), Chavarro Montenegro (2011), Molina and Bouallou (2013), Naskar et al. (2013), Yakub et al. (2014) and Erfani et al. (2015) the performance of MEA was compared to solvents such as diethanolamine (DEA), methyldiethanolamine (MDEA), AMP, piperazine (PZ) and various combinations of blends using these amines. In using pilot plant data with MEA as the solvent, the results by (\u0026Oslash;i, 2007) demonstrated the efficiency in the addition of a capture plant to a parent power plant. Others have compared the simulation software data results to the pilot plant results (Aliabad and Mirzaei, 2009; Luo et al., 2009; Mirzaei et al., 2009), including model development for improved CO\u003csub\u003e2\u003c/sub\u003e capture representation (Abu-Zahra et al., 2012; Ahmadi, 2012; Lim et al., 2013; Lin et al., 2016; \u0026Oslash;i, 2012; Zhang et al., 2009) and a techno economic analysis of a CO\u003csub\u003e2\u003c/sub\u003e capture plant (Li and Liang, 2012; Razi et al., 2013).\u003c/p\u003e \u003cp\u003eProcess simulation studies by Adeosun and Abu-Zahra (2013), Daya (2017), Erfani et al., (2015), Fisher et al., (2007), Molina and Bouallou (2013), and Padurean et al., (2011), compared the performance of solvent blends to pure aqueous solvents or solvent blends. Jones et al. (2013) evaluated an aqueous tri-amine blend of MEA, MDEA and AMP, which to the best of our knowledge appears to be a novel study via process simulations involving an aqueous tri-amine blend.\u003c/p\u003e \u003cp\u003eUsing the basis of a PCC plant, process simulations were performed using Aspen Plus\u0026reg; V8.8 software, to evaluate and benchmark aqueous amine solvents and their blends against a 30 wt. % AMP solution to achieve a rate of 90% carbon capture. This is a continuation of the work in applying a Performance Indicator Model (PIM), which was developed by Daya (2017), who had selected MEA as the solvent basis. For reasons explained later in the article, AMP was selected as the basis based on literature findings. The PIM is used to evaluate the performance of aqueous amine blends consisting of binary combinations of MDEA or AMP with PZ as an activator, in different concentrations. To enable a comprehensive evaluation tool, the inputs to this performance indicator model included the key parameters such as primarily solvent flow rates and equipment heat duties, calculated using the ASPEN Plus\u0026reg; process simulation designs. Other significant inputs to the model involved costs based on energy requirements, make-up flows, and carbon taxes. The usefulness of such a model provides insight into the viability of a large-scale process and enables the selection of promising solvent or blends without expensive experimental efforts.\u003c/p\u003e"},{"header":"2. Post-combustion CO capture simulations and data","content":"\u003cp\u003eIn the Aspen Plus simulations, the Electrolyte Non-Random Two-Liquid (eNRTL) model was employed to represent the aqueous mixed electrolyte system, whilst the Peng-Robinson equation of state (PREoS) and Boston-Mathias alpha function was used to represent the gas phase.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. System chemistry and kinetics\u003c/h2\u003e \u003cp\u003eThe reactions between amines with CO\u003csub\u003e2\u003c/sub\u003e takes place in the absorber resulting in the formation of the intermediate compounds. The absorption reaction is then reversed in the stripper to release the absorbed CO\u003csub\u003e2\u003c/sub\u003e. The reactions mentioned below were considered for the absorption process using amines (AspenTech., 2015b):\u003c/p\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;2H\u003csub\u003e2\u003c/sub\u003eO \u0026harr;H\u0026#119862;\u0026#119874;\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e+\u0026#119867;\u003csub\u003e3\u003c/sub\u003e\u0026#119874;\u003csup\u003e+\u003c/sup\u003e (1)\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2𝐻𝑂 ↔𝐻𝑂+𝑂𝐻 (2)\u003c/h3\u003e\n\u003cp\u003e\u0026#119867;\u0026#119862;\u0026#119874;\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e+\u0026#119867;\u003csub\u003e2\u003c/sub\u003e\u0026#119874;\u0026harr;\u0026#119862;\u0026#119874;\u003csub\u003e3\u003c/sub\u003e \u003csup\u003e2\u0026minus;\u003c/sup\u003e+\u0026#119867;3\u0026#119874;\u003csup\u003e+\u003c/sup\u003e (3)\u003c/p\u003e \u003cp\u003e\u0026#119872;\u0026#119864;\u0026#119860;\u003csup\u003e+\u003c/sup\u003e+\u0026#119867;\u003csub\u003e2\u003c/sub\u003e\u0026#119874;\u0026harr;\u0026#119872;\u0026#119864;\u0026#119860;+\u0026#119867;\u003csub\u003e3\u003c/sub\u003e\u0026#119874;\u003csup\u003e+\u003c/sup\u003e (4)\u003c/p\u003e \u003cp\u003e\u0026#119872;\u0026#119864;\u0026#119860;\u0026#119862;\u0026#119874;\u0026#119874;\u003csup\u003e\u0026minus;\u003c/sup\u003e+\u0026#119867;\u003csub\u003e2\u003c/sub\u003e\u0026#119874;\u0026harr;\u0026#119872;\u0026#119864;\u0026#119860;+ \u0026#119867;\u0026#119862;\u0026#119874;\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e (5)\u003c/p\u003e \u003cp\u003e\u0026#119872;\u0026#119863;\u0026#119864;\u0026#119860;\u003csup\u003e+\u003c/sup\u003e+\u0026#119867;\u003csub\u003e2\u003c/sub\u003e\u0026#119874;\u0026harr;\u0026#119872;\u0026#119863;\u0026#119864;\u0026#119860;+\u0026#119867;\u003csub\u003e3\u003c/sub\u003e\u0026#119874;\u003csup\u003e+\u003c/sup\u003e (6)\u003c/p\u003e \u003cp\u003e\u0026#119860;\u0026#119872;\u0026#119875;\u003csup\u003e+\u003c/sup\u003e+\u0026#119867;\u003csub\u003e2\u003c/sub\u003e\u0026#119874;\u0026harr;\u0026#119860;\u0026#119872;\u0026#119875;+\u0026#119867;\u003csub\u003e3\u003c/sub\u003e\u0026#119874;\u003csup\u003e+\u003c/sup\u003e (7)\u003c/p\u003e \u003cp\u003e\u0026#119875;\u0026#119885;\u0026#119867;\u003csup\u003e+\u003c/sup\u003e+\u0026#119867;\u003csub\u003e2\u003c/sub\u003e\u0026#119874;\u0026harr;\u0026#119875;\u0026#119885;+\u0026#119867;\u003csub\u003e3\u003c/sub\u003e\u0026#119874;\u003csup\u003e+\u003c/sup\u003e (8)\u003c/p\u003e \u003cp\u003e\u0026#119875;\u0026#119885;+\u0026#119862;\u0026#119874;\u003csub\u003e2\u003c/sub\u003e+\u0026#119867;\u003csub\u003e2\u003c/sub\u003e\u0026#119874;\u0026harr;\u0026#119875;\u0026#119885;\u0026#119862;\u0026#119874;\u0026#119874;\u003csup\u003e\u0026minus;\u003c/sup\u003e+\u0026#119867;\u003csub\u003e3\u003c/sub\u003e\u0026#119874;\u003csup\u003e+\u003c/sup\u003e (9)\u003c/p\u003e \u003cp\u003e\u0026#119867;\u0026#119875;\u0026#119885;\u0026#119862;\u0026#119874;\u0026#119874;+\u0026#119867;\u003csub\u003e2\u003c/sub\u003e\u0026#119874;\u0026harr;\u0026#119875;\u0026#119885;\u0026#119862;\u0026#119874;\u0026#119874;\u003csup\u003e\u0026minus;\u003c/sup\u003e+\u0026#119867;\u003csub\u003e3\u003c/sub\u003e\u0026#119874;\u003csup\u003e+\u003c/sup\u003e (10)\u003c/p\u003e \u003cp\u003e\u0026#119875;\u0026#119885;\u0026#119862;\u0026#119874;\u0026#119874;\u003csup\u003e\u0026minus;\u003c/sup\u003e+\u0026#119867;\u0026#119862;\u0026#119874;\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026harr;PZ(\u0026#119862;\u0026#119874;\u0026#119874;\u003csup\u003e\u0026minus;\u003c/sup\u003e)\u003csub\u003e2\u003c/sub\u003e+\u0026#119867;\u003csub\u003e2\u003c/sub\u003e\u0026#119874; (11)\u003c/p\u003e \u003cp\u003eIn order to define the equations describing the equilibrium constants of the abovementioned reactions in Aspen Plus\u003csup\u003e\u0026reg;\u003c/sup\u003e the following equation was used (temperature is in Kelvin):\u003c/p\u003e \u003cp\u003eln(\u0026#119870;\u003csub\u003e\u0026#119890;\u0026#119902;\u003c/sub\u003e)=\u0026#119860;+\u0026#119861;/\u0026#119879;+\u0026#119862;ln(\u0026#119879;)+\u0026#119863;\u0026#119879; (12)\u003c/p\u003e \u003cp\u003ein which the equilibrium constants \u003cem\u003eA\u003c/em\u003e, \u003cem\u003eB\u003c/em\u003e, \u003cem\u003eC\u003c/em\u003e and \u003cem\u003eD\u003c/em\u003e are represented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEquilibrium constants used in the simulation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReaction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e231.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-12092.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-36.7816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAspenTech. (2015a)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e132.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-13445.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-22.4773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAustgen et al. (1991)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e216.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-12431.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-35.4819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAustgen et al. (1991)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.03833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7008.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.00313489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAustgen et al. (1991)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.52135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2545.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAustgen et al. (1991)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-9.4165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4234.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAspenTech. (2015a);\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.68672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6754.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDash et al. (2011)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-62.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAspenTech. (2015a);\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e466.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1614.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-97.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDash et al. (2012)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6066.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDash et al. (2011)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-11.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1769.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDash et al. (2011)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Flowsheet setup\u003c/h2\u003e \u003cp\u003eThe CO\u003csub\u003e2\u003c/sub\u003e capture system has inherent convergence issues due to its ionic nature and the presence of chemical reactions within the absorber and stripper. It was thus decided to model the closed-loop system with an open loop (where the recycle stream is not connected to the absorber), to simplify the simulation calculations. To achieve the desired CO\u003csub\u003e2\u003c/sub\u003e capture rate of 90% whilst maintaining commercial sizes for the separation columns, four parallel trains were used in the capture section of the flowsheet.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. shows the process flow diagram for post-combustion CO\u003csub\u003e2\u003c/sub\u003e capture using absorption. This consists of three main units, including cooling and compression of the flue gas, CO\u003csub\u003e2\u003c/sub\u003e absorption and solvent regeneration, and CO\u003csub\u003e2\u003c/sub\u003e compression which are explained in detail in the next sections (Kothandaraman, 2010).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Flue gas compression and cooling section\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows a direct contact cooler (DCC) which operates at the temperature of the absorber (40\u0026deg;C), a blower for the flue gas compression and cooling section to adjust the flue gas temperature to the operating conditions prior it enters the absorber. It is well established that DCC has great advantages over indirect coolers. For example, it requires less cooling water, lower capital and operating costs, and it has a lower pressure drop (Kothandaraman, 2010). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the specifications of the flue gas fed to the blower. The same composition of the flue gas as used by Khalil and Gerbino (2007) was applied in this study. Insignificant impurities such as sulphur trioxide (SO\u003csub\u003e3\u003c/sub\u003e), hydrochloric acid (HCl) and nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e) with neglectable concentrations were ignored and the compositions of the remaining compounds were normalized.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProperties of flue gas from a coal-fired power plant, used in this study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlow Rate (ton/hr)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2516\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePressure (kPa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eComposition\u003c/p\u003e \u003cp\u003e(mole fraction)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrogen (N\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73470\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxygen (O\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater vapour (H\u003csub\u003e2\u003c/sub\u003eO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07975\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbon Dioxide (CO\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArgon (Ar)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrous Oxide (NO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSulphur Dioxide (SO\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA RadFrac column was utilized within the simulation of the DCC in Aspen Plus\u0026reg; for which the column specifications are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In the simulation, the blower provided a pressure equal to 111.25 kPa.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpecifications of the direct contact cooler.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalculation type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquilibrium\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of stages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcess stream inlet temperature (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcess stream outlet temperature (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCondenser\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReboiler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. CO\u003csub\u003e2\u003c/sub\u003e capture section\u003c/h2\u003e \u003cp\u003eThis vital part of the CO\u003csub\u003e2\u003c/sub\u003e capture plant is comprised mainly of two pieces of equipment for CO\u003csub\u003e2\u003c/sub\u003e capture i.e., the absorber and stripper columns.\u003c/p\u003e \u003cp\u003eTypically, a packed column is selected for the absorber in which the flue gas and the lean amine solvent enter from the bottom and the top, respectively. The addition of a water wash section at the top of the column is recommended by Li et al. (2016) in order to cool and clean the vent gas (Li et al., 2016a). The simulation of the absorber was performed using a RadFrac column. The specifications of the four identical absorbers, connected in parallel are given in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpecifications of the absorber.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSection 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSection \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWash water section\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCapture Section\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalculation type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquilibrium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEquilibrium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of stages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTop Pressure (kPa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiameter (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCondenser\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReboiler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSimilar to the absorber, a packed column is also used for the stripper which is mainly responsible for the separation of the captured CO\u003csub\u003e2\u003c/sub\u003e from the rich amine solvent. The operational pressure of the stripper was set at 1.8 atm. Basically, the stripper utilizes the heat provided by the steam in the kettle reboiler to reverse whatever reaction occurs in the absorber resulting in the production of the lean solvent at the bottom of the column and a stream mainly containing CO\u003csub\u003e2\u003c/sub\u003e and H\u003csub\u003e2\u003c/sub\u003eO at the top. The regenerated solvent exits from the bottom and is recycled to the absorber (Fisher et al., 2007; Kothandaraman, 2010).\u003c/p\u003e \u003cp\u003eA RadFrac column was also employed to simulate four identical strippers, connected in parallel for which the column specifications are given in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpecifications of the stripper\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalculation type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquilibrium\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of stages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTop Pressure (kPa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiameter (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCondenser\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePartial\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReboiler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKettle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn order to provide a higher operational pressure in the stripper, a pump is included after the absorber. Furthermore, the rich amine solvent with elevated pressure prevents corrosive acid gas release which can cause fatal damage to the vital equipment and piping (Fisher et al., 2005). Similarly, for the abovementioned reasons a pump is also placed after the stripper to recycle the lean amine solvent to the absorber.\u003c/p\u003e \u003cp\u003eIn an energy-conservation approach, prior to the regeneration of the amine solvent in the stripper the rich amine from the absorber is pre-heated using the hot lean amine from the stripper reboiler. The temperature of the rich amine is increased to approximately 110 ˚C, concerning a 10˚C temperature approach on the hot side of the heat exchanger (Fisher et al., 2005).\u003c/p\u003e \u003cp\u003eFurther cooling of the lean amine is achieved by placing a cooler in the recycle loop in order to bring the lean amine temperature to the absorber operating temperature (\u0026plusmn;\u0026thinsp;40\u0026deg;C) (Fisher et al., 2005).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3. CO\u003csub\u003e2\u003c/sub\u003e compression train\u003c/h2\u003e \u003cp\u003eIn order to compress the CO\u003csub\u003e2\u003c/sub\u003e product stream prior to it being sent for storage, a 4-stage reciprocating compressor, with inter-stage cooling and maximum compression of about 9 MPa, is usually utilized. The resulting supercritical liquid CO\u003csub\u003e2\u003c/sub\u003e is subsequently pumped to the necessary discharge pressure of about 13 MPa (Kothandaraman, 2010).\u003c/p\u003e \u003cp\u003eThe compression of the captured CO\u003csub\u003e2\u003c/sub\u003e in the simulation in this study is performed using a sequence of three compressors with inter-stage cooling and CO\u003csub\u003e2\u003c/sub\u003e separation from the condensed H\u003csub\u003e2\u003c/sub\u003eO. The resultant pressures achieved from each compressor are 430 kPa, 1.9 MPa and 8 MPa, respectively. The supercritical liquid CO\u003csub\u003e2\u003c/sub\u003e is then pumped to the pressure of 11 MPa.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Performance indicator model\u003c/h2\u003e \u003cp\u003eThe evaluation of the performance of the amine solvents considered in this study for CO\u003csub\u003e2\u003c/sub\u003e capture is carried out by entering the simulation results into a performance indicator model (PIM). In this model, the parameters including solvent make-up, cooling water and make-up water, steam, corrosion inhibitor, amine reclaim and disposal, and carbon taxes, were selected as inputs to the model on a cost basis. As indicated in a previous study by Daya (2017) it is more suitable to base the PIM model on the cost of CO\u003csub\u003e2\u003c/sub\u003e avoided, instead of the cost of CO\u003csub\u003e2\u003c/sub\u003e captured since in some cases capturing CO\u003csub\u003e2\u003c/sub\u003e increases the CO\u003csub\u003e2\u003c/sub\u003e emissions. For example, in power plants since the power required for the capture process usually comes from the plant itself the efficiency of the plant decreases resulting in more fuel combusted by the plant to meet the electrical load. A conceptual illustration of CO\u003csub\u003e2\u003c/sub\u003e captured vs CO\u003csub\u003e2\u003c/sub\u003e avoided is represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eSince solvent regeneration is an energy-intensive approach that contributes nearly two-thirds of the operating cost of the CO\u003csub\u003e2\u003c/sub\u003e capture plant, for evaluation of the solvent performance many researchers merely focus on the energy consideration (Khalil and Gerbino, 2007). However, in the model used in this study, the many other factors that influence the operating costs were considered and are discussed in the following sections. Further details of how the costs for all of these factors were calculated can be found in Fourie (2018).\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Amine type\u003c/h2\u003e \u003cp\u003eThe influence of amine type on the model (besides variation in costs between different amines) is due to the variation of the solvent flow rate necessary to absorb the required amount of CO\u003csub\u003e2\u003c/sub\u003e when amine type is varied. The prices for the amines investigated in this study are given in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The prices from different sources were averaged to obtain a single representative cost. In the case of outdated values, in order to get more updated prices, a ratio of the chemical consumer index of the source year and the current year was utilized.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrices of the amines studied.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmine\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrice (R/ton)*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference(s)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEachus and Bollmeier (2000); Zauba Technologies \u0026amp; Data (2016); Daya (2017)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMDEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKohl and Nielsen (1997); Zauba Technologies \u0026amp; Data (2016)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKohl and Nielsen (1997); Sinnott (2005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSridhar and Carter (2000); Sigma-Aldrich (2016); Zauba Technologies \u0026amp; Data (2016)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003e*The prices cited are for 2016\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Amine degradation rates\u003c/h2\u003e \u003cp\u003eAmine losses due to amine degradation have a direct effect on the operating cost of the capture plant. However, the diversity of the factors and parameters involved in the degradation reactions makes it too complicated to be used in the simulations (Fytianos et al., 2016). Hence, to take the amine degradation into consideration a general degradation model was used in this study, which was incorporated outside the simulation work. Further details on the inclusion of the reclamation into the model are explained in Fourie (2018).\u003c/p\u003e \u003cp\u003eAs illustrated in the literature the contributors to degradation which directly influence the process streams are oxidative and thermal degradation rates. Hence, in this study these rates were utilized to represent the amines degradation (Shao and Stangeland, 2009). For MEA, MDEA and AMP the degradation rates were selected from the data published by Lepaumier et al. (2009a), Lepaumier et al. (2009b) and Lepaumier et al. (2009c) which are reported in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The values reported are based on the fraction of amine degraded per hour and are the summation of oxidative and thermal degradation contributions in the presence of both O\u003csub\u003e2\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003e. In the case of PZ, as illustrated by Freeman et al. (2010), this does not degrade at temperatures below 140\u0026deg;C, and since the equipment in which PZ exists, operate at lower temperatures, the degradation of PZ was ignored in the model.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDegradation rates of the amines studied\u003csup\u003e*\u003c/sup\u003e. \u003csup\u003e*\u003c/sup\u003e(Lepaumier et al., 2009a) (Lepaumier et al., 2009b) (Lepaumier et al., 2009c)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmine\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCombined degradation rate (%/hr)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0739\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMDEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0970\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssumed negligible\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAnother important cause of degradation is the presence of a trace amount of impurities in the flue gas stream such as NO\u003csub\u003ex\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, fly ash and NH\u003csub\u003e3\u003c/sub\u003e. In order to purify the system from these degradation products as well as any other undesirable impurities such as sludge, a reclaimer unit is placed after the reboiler of the stripper (Rochelle et al., 2011). The reclaimer unit was not included in the simulations due to the complexity of the degradation reactions mechanism and diversity of the degradation products. Instead, the contributions of these reclamation costs, estimated as a fractional loss of the recirculating solvent rate, as well as the disposal costs due to amine degradation were calculated manually using an excel spreadsheet along with the other PIM calculations (Fourie 2018).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3. Corrosion\u003c/h2\u003e \u003cp\u003eUsually, the absorption reaction of CO\u003csub\u003e2\u003c/sub\u003e with amines leads to the production of several highly corrosive chemicals which can cause fatal damage to the process equipment. One solution to this problem is the application of corrosion inhibitors such as sodium metavanadate and copper carbonate (Veawab et al., 2001). In this study, the same corrosion inhibitors were employed, and the cost was calculated from a fraction of the recirculating solvent rate (Fourie 2018).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4. \u003cem\u003eEnergy consumption\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe energy consumption in a CO\u003csub\u003e2\u003c/sub\u003e capture plant can be divided into three major uses: electrical power consumption, steam usage and water usage. The electrical consumption is mainly due to the flue gas blower, CO\u003csub\u003e2\u003c/sub\u003e compressors and all pumps in the process. In this study the energy required in the capture plant is provided by the power plant, consequently, the developed performance model is based on the \u0026ldquo;cost of CO\u003csub\u003e2\u003c/sub\u003e avoided\u0026rdquo;.\u003c/p\u003e \u003cp\u003eIn the capture plant, the only equipment that consumes steam is the stripper reboiler and the compressors which were modeled to be electrically driven. The amount of steam consumed by the stripper is directly proportional to the value of the heat duty calculated using the simulations and it was estimated by dividing the heat duty by the steam heat of vaporization.\u003c/p\u003e \u003cp\u003eDirect contact cooler (DCC), stripper condenser, CO\u003csub\u003e2\u003c/sub\u003e compression intercoolers and the lean amine cooler are the major equipment that consume cooling water. For stripper condenser and CO\u003csub\u003e2\u003c/sub\u003e compression intercoolers the amount of cooling water required was obtained using the duties in the simulations while, the flow of cooling water through the DCC was directly obtained from the simulation. In order to preserve the composition of aqueous solvent the addition of make-up water to the system is also necessary.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.3.5. \u003cem\u003eCarbon tax\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe levy imposed on the release of CO\u003csub\u003e2\u003c/sub\u003e by the industrial facilities is referred to as carbon tax which is a form of pollution tax. This levy serves to encourage companies to reduce their CO\u003csub\u003e2\u003c/sub\u003e emissions. The price of carbon tax has been set to \u003cspan\u003e$\u003c/span\u003e10/ton with a possible 10% yearly increase between 2016 and 2019 (The World Bank, 2014). In this study the rate of carbon capture was fixed, therefore the value of carbon tax allocated for each solvent blend stays constant.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Model inputs and outputs\u003c/h2\u003e \u003cp\u003eThe PIM developed in this study requires two types of inputs, viz. user-defined inputs and result inputs. User-defined inputs can be changed by user. In Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e the user-defined inputs are divided into two groups \u0026ldquo;inputs into Aspen\u0026rdquo; and \u0026ldquo;external inputs\u0026rdquo;. The result inputs, which are basically the outputs from the Aspen simulation, are identified as \u0026ldquo;inputs into PIM from Aspen\u0026rdquo;. The ultimate output achieved from the performance model is the rating of a specific amine solvent or blend relative to an identified baseline solvent.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe various inputs required to use the performance indicator model (PIM).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInputs into Aspen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInputs into PIM from Aspen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExternal inputs into PIM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlue gas flow rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCooler duties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmine price(s)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlue gas composition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStripper condenser duty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMake-up water price\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolvent composition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirect cooler water flow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCooling water price\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e capture rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStripper reboiler duty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSteam price\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLean solvent flow from stripper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCorrosion inhibitor price\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmine flow(s) into absorber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmine reclaim cost\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompressor power required\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmine disposal cost\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePumping power required\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCarbon tax rate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePower required for blower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmine degradation rate(s)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmine make-up flow(s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePower plant efficiency\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater make-up flow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e flow into process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Model implementation\u003c/h2\u003e \u003cp\u003eIn this section the determination of the rating with the PIM is explained in detail.\u003c/p\u003e \u003cp\u003eFor the evaluation of the overall cost for CO\u003csub\u003e2\u003c/sub\u003e captured (\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003eT,j,captured\u003c/em\u003e\u003c/sub\u003e) the summation of each factor \u003cem\u003ei\u003c/em\u003e for each case \u003cem\u003ej\u003c/em\u003e is used. Generally, the cost factors are equivalent to the product of price of the process chemicals or utilities and their corresponding flow rates.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{C}_{T,j,captured}={\\sum\\:}_{k=0}^{n}{C}_{ij}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e13\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe total cost of CO\u003csub\u003e2\u003c/sub\u003e avoided is obtained using the following equation:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{{C}_{T,j,avoided}=C}_{T,j,captured}\\times\\:\\frac{{\\epsilon\\:}_{OP}}{{\\epsilon\\:}_{j}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e14\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ein which \u003cem\u003eε\u003c/em\u003e\u003csub\u003e\u003cem\u003eOP\u003c/em\u003e\u003c/sub\u003e represents the original operating efficiency of the power plant without the capture plant and (\u003cem\u003eε\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e) is the reduced efficiency of the power plant in the presence of the capture plant which is dependent on the solvent used in case \u003cem\u003ej\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe following equation is then used to obtain the rating for each case \u003cem\u003ej\u003c/em\u003e, R, in which \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,b,avoided\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,j,avoided\u003c/em\u003e\u003c/sub\u003e represent the avoided costs of the base case and case \u003cem\u003ej\u003c/em\u003e, for each factor \u003cem\u003ei\u003c/em\u003e respectively.\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:R={\\sum\\:}_{i}^{n}{x}_{i,j}\\times\\:\\frac{{C}_{i,b,avoided}}{{C}_{i,j,avoided}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e15\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e15\u003c/span\u003e), \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e, is the factor fraction which is obtained by dividing the cost of the factor \u003cem\u003ei\u003c/em\u003e for case \u003cem\u003ej\u003c/em\u003e by the total cost of CO\u003csub\u003e2\u003c/sub\u003e avoided for case \u003cem\u003ej\u003c/em\u003e.\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{x}_{ij}=\\frac{{C}_{i,j,avoided}}{{C}_{T,i,j,avoided}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e16\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn Eq.\u0026nbsp;(\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e16\u003c/span\u003e), \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e can be used as an indicator to compare each case to the benchmark case. If a lower cost avoided calculated for case \u003cem\u003ej\u003c/em\u003e than that of the benchmark case, it suggests a superior performance of the solvent in case \u003cem\u003ej\u003c/em\u003e in the simulation of post-combustion CO\u003csub\u003e2\u003c/sub\u003e capture. For the solvents that are more cost-effective as determined by equations \u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e15\u003c/span\u003e and \u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e16\u003c/span\u003e, a greater rating value (than the benchmark with \u003cem\u003eR\u003c/em\u003e is equal to 1) would be obtained if multiplying the ratio of cost avoided calculated for case \u003cem\u003ej\u003c/em\u003e and the benchmark by \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e. Consequently, for the less cost-effective solvents than the benchmark, a rating value of less than one would be obtained.\u003c/p\u003e \u003cp\u003eAll the factors included in the PIM are associated to the operating cost of a CO\u003csub\u003e2\u003c/sub\u003e capture plant. Since the capital costs are not included in the model, in the solvent assessment it is required that equipment sizes remain unchanged during all the simulation cases being evaluated. In a practical condition, this would be equivalent to varying the solvent of an existing capture installation to establish whether it enhances the performance of the plant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Test systems\u003c/h2\u003e \u003cp\u003eIn this study, an aqueous amine solvent of MEA with a concentration of 30 wt.% was used in a simulation as the benchmark case for which the main inputs to the PIM are given in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMain inputs to the performance model for the base case, 30% MEA\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmine Price\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR/ton\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39271\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMake-up Water Price\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR/ton\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.52\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCooling Tower Water Price\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR/ton\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.54\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSteam Price\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR/ton\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorrosion Inhibitor Price\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR/ton\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3784\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmine Reclaim Cost\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR/ton\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10078\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmine Disposal Cost\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR/ton\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3008\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbon Tax Rate\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR/ton\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120\u003csup\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmine Degradation Rates\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%/hr\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3137\u003csup\u003e\u003cem\u003ed\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePower Plant Efficiency\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.4\u003csup\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e Kohl and Nielsen (1997); Sinnott (2005)\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e Daya (2017)\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sup\u003e The World Bank (2014)\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003e\u003cem\u003ed\u003c/em\u003e\u003c/sup\u003e Lepaumier et al. (2009a); Lepaumier et al. (2009b); Lepaumier et al. (2009c)\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sup\u003e Gammer (2016)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eWhen using the PIM to analyze the results, the optimum operating point (the point at which the different costs combine to form the lowest total CO\u003csub\u003e2\u003c/sub\u003e capture cost and thus the highest rating) must be found. To determine the optimum operating cost, the simulation was performed for a range of lean solvent loadings and the loading which resulted in the highest overall rating as determined by the PIM is then the optimum point for that solvent. This method was used for all solvent blends considered.\u003c/p\u003e \u003cp\u003eThe validation of the performance indicator model is achieved by comparing the main results of the benchmark using the 30% MEA process simulation to those in the literature. Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e shows this comparison. For this purpose, the energy requirements were noted and the results obtained in this study as well as those from the literature indicate that the energy required is affected directly by the number of employed trains (hence number of absorbers and strippers). For example the energy required for CO\u003csub\u003e2\u003c/sub\u003e capture process using MEA is between 3 to 4.5 GJ/(ton CO\u003csub\u003e2\u003c/sub\u003e) (Zahra, 2009). While, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e the energy requirement reported by researchers who used multiple equipment trains is similar to the amount in this range multiplied, though by the number of trains.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparative analysis between the 30% MEA benchmarks established in this study and those reported in the literature.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThis study (80% capture)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThis study (90% capture)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFisher et al. (2007)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKothandaraman (2010)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePadurean et al. (2011)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDaya (2017)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpen/ closed Process\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClosed\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eClosed\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003eSolvent Lean Loading (mol CO\u003csub\u003e2\u003c/sub\u003e/mol amine)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003en/s\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003en/s\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003eSolvent Flowrate (ton/hr)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1484\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1786\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24237\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003en/s\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3500\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2073\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlue Gas CO\u003csub\u003e2\u003c/sub\u003e Content (mol %)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003en/s\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlue Gas Flow Rate (ton/hr)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2283\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2283\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2448\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003en/s\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2928\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2516\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e capture rate (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Energy Requirement (GJ/t CO\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.82\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStripper diameter (m)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003en/s\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of trains\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy Required per Stripper (GJ/t CO\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e All sources referenced in this table use 30 wt.% MEA as a solvent\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003enot specified by source\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, for the benchmark system (30% MEA, 80% CO\u003csub\u003e2\u003c/sub\u003e capture), the energy required per reboiler unit is equal to 3.09 GJ/ ton CO\u003csub\u003e2\u003c/sub\u003e. This is 5% less than the value obtained by Daya (2017), using very similar conditions. This was deemed as an acceptable difference. By adjusting the capture rate (30% MEA, 90% capture) and comparing the result to the AMP system to the literature the energy required per reboiler unit is 4.9 GJ/ ton CO\u003csub\u003e2\u003c/sub\u003e. This is 16% higher than the value obtained by Fisher et al. (2007), with conditions and inputs being very quite similar, but still deemed as an acceptable difference. Not all simulation conditions can be identical, especially when there exists a great difference in the column diameters used in different simulations. It is worth mentioning that in this study open-loop simulations were used while Fisher et al. (2007) used closed-loop simulations. However, the discrepancy in the results of open-loop and closed-loop processes are not always high. For example, as indicated in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, the percentage difference between the energy requirements obtained in the work of Fisher et al. (2007), with closed-loop, and Kothandaraman (2010), with open-loop, is less than 2%. This and a study by Ahmadi (2012) confirm that the results obtained in this study which are based on open-loop process simulations are equivalent to the results achieved using simulation of closed-loop processes.\u003c/p\u003e \u003cp\u003eThe output of the simulation using 30% AMP as the solvent, with the benchmark as 30% MEA alongside the results obtained by Daya (2017) and Padurean et al. (2011) are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The preliminary rating obtained in this study was not exactly the same as the ratings calculated by the other two references. After performing a sensitivity analysis, the range of (0.932–1.151) was obtained as a probable range of ratings for which the upper limit is placed within the range of the literature values (refer to error bars in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Hence, it is confirmed that the rating results for the 30% AMP simulation were satisfactory, and this case was utilized as the benchmark for the investigations of the study. Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e shows a comparison between 30% AMP benchmark of this study to simulations for 30% AMP obtained in the literature.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparative analysis between the AMP benchmark adopted in this investigation and similar AMP simulation studies documented in existing literature\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThis work\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePadurean et al. (2011)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLee et al. (2009)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChavarro Montenegro (2011)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLi et al. (2016b)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolvent Lean Loading\u003c/p\u003e \u003cp\u003e(mol CO\u003csub\u003e2\u003c/sub\u003e/mol amine)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en/s\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en/s\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en/s\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolvent Flowrate (ton/hr)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2383\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en/s\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en/s\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003en/s\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlue Gas CO\u003csub\u003e2\u003c/sub\u003e Content\u003c/p\u003e \u003cp\u003e(mol %)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.57\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlue Gas Flow Rate (ton/hr)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2283\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2928\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3122\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e capture rate (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Energy Requirement (GJ/t CO\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.76\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.295\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStripper diameter (m)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en/s\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en/s\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003en/s\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of trains\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e not specified by source\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e If number of trains were not specified, it was assumed to be 1\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eIt can be interpreted from the solvent and flue gas flow rates in Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e that the work carried out by Lee et al. (2009) and Chavarro Montenegro (2011) was performed on a smaller scale than this work. The comparatively large energy requirements obtained in both studies confirm that the results are highly dependent on the scale at which the simulations performed. Excluding these two sources, a maximum deviation of about 26% between this work and literature is obtained. (considering 2.9 GJ/ ton CO\u003csub\u003e2\u003c/sub\u003e for the energy requirement per reboiler in this study) with an average percentage deviation of 14.5%. The deviations obtained are satisfactory since not all the conditions are the same as used in literature.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.2. \u003cem\u003eNew systems\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eIn this study several blends of the amines, i.e., AMP, MDEA and PZ were selected as the new systems in the CO\u003csub\u003e2\u003c/sub\u003e capture simulations. In order to examine the effect of increasing the amine concentration on the rating, binary solvent blends with total amine concentrations of 30% and 40% were simulated. Within each scenario, the lean loading of the solvent (mol CO\u003csub\u003e2\u003c/sub\u003e/ mol amine) was varied to find the optimum operating point (where the rating was highest). Subsequently, the resultant conditions became the demonstrative case for that particular amine blend, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.The rating for the benchmark case is set to 1, based on the output from the PIM. As indicated previously, in the PIM calculations the ratings above one are considered as a superior performance to the benchmark whilst the ratings below one are considered inferior to the benchmark. The 30% AMP aqueous solution was applied as the benchmark for all cases.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e shows results for all the systems considered in this study. One can observe from the results shown in Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e that even though the reboiler duty and solvent flow rate encompass a great portion of the ratings obtained by the PIM, the best rating is not always obtained when the reboiler duty and solvent flow rate are minimum. Experimental investigations show that the solubility of CO\u003csub\u003e2\u003c/sub\u003e in the solvents considered in this study increases in the order MDEA \u0026lt; AMP \u0026lt; PZ. A similar trend was also observed in the flowrates reported for the different amine blends in which the flowrate decreased with decreasing solvents capacity for CO\u003csub\u003e2\u003c/sub\u003e capture. For example, the blends of MDEA + PZ show flow rates considerably higher than that of the AMP + PZ blends. In the case of the tri-amine blends since all three components exist, flow rates lie in between. Solvent flow rate and the reaction rate of amine blend with CO\u003csub\u003e2\u003c/sub\u003e (which also follows the trend PZ \u0026gt; AMP \u0026gt; MDEA) are also directly related. Consequently, it is expected that for a fixed capture rate the solvents with a higher rate of reaction with CO\u003csub\u003e2\u003c/sub\u003e show a lower required flow rate as presented in Table\u0026nbsp;\u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003e13\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAn overview of the outcomes achieved for the analyzed configurations in this study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystem\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLean Loading (mol CO\u003csub\u003e2\u003c/sub\u003e/ mol Amine)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRating\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReboiler Duty (MW)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSolvent Flow Rate (ton/hr)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30% AMP\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e296\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2323\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25% MDEA + 5% PZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.030\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3778\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22% MDEA + 8% PZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3893\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35% MDEA + 5% PZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.080\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3345\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32% MDEA + 8% PZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.052\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3446\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28% AMP + 2% PZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.270\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1874\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25% AMP + 5% PZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.359\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1859\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e38% AMP + 2% PZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.120\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1863\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30% AMP + 10% PZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.212\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1936\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25% MDEA + 5% AMP + 5% PZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.220\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2858\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25% MDEA + 10% AMP + 5% PZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.269\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2473\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25% MDEA + 10% AMP + 10% PZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.228\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2641\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab13\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eA summary of the simulations conducted for the intercooled absorber under various operating conditions. *Convergence errors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSplit Fraction\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEntering stage\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExiting Stage\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStripper Reboiler Duty (MW)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIntercooler Duty (MW)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSolvent Flow Rate (ton/hr)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTemperature Difference in Intercooler (°C)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eEstimated Rating\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase 1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e142.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-6.255\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1716\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19.09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.431\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase 2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e139.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-10.48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1684\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16.31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.487\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase 3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e139.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-12.60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1679\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13.66\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.509\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase 4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e139.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-13.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1680\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13.66\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.509\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase 5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e142.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-6.977\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1709\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e21.36\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.463\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase 6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e138.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-11.51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1668\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18.07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.546\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase 7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e137.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-13.72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1656\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15.79\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.577\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase 8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e137.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-14.26\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1655\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.578\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase 9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e141.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.783\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1694\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.76\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.413\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase 10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e142.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-4.581\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1720\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.429\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase 11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e141.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-5.953\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.453\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase 12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e139.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-6.977\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1684\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.471\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eAmongst the amine blends considered in this study, MDEA + PZ blends did not show very promising ratings, which is mostly due to the ternary structure of MDEA which hinders its reaction with CO\u003csub\u003e2\u003c/sub\u003e resulting in a higher solvent flowrate. CO\u003csub\u003e2\u003c/sub\u003e solubility data confirms that the solubility of CO\u003csub\u003e2\u003c/sub\u003e increases with increasing the PZ concentration in the blend (Dash and Bandyopadhyay, 2016) which is also observed in the solvent flow rate trends (see Table\u0026nbsp;\u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003e13\u003c/span\u003e). The reclaim and disposal costs of the amines are estimated in separate calculations based on the flowrates of the solvent. Consequently, a higher flowrate also results in a surge in these costs. Hence, although the energy consumption for the MDEA + PZ blend is lower than the benchmark, by considering the additional factors mentioned in the calculations, a lower rating for AMP + PZ blends is observed.\u003c/p\u003e \u003cp\u003eThe combination of the low reboiler duty and low solvent flow rate resulted in the highest ratings overall achieved for the AMP + PZ blends. A relatively low reboiler duty was achieved for the tri-amine blends, which is possibly due to the combination of AMP and MDEA knowing that both have lower heats of reaction with CO\u003csub\u003e2\u003c/sub\u003e (resulting in a reduction in the regeneration duty). Nevertheless, since a large portion of the solvent’s amine concentration is MDEA, a high solvent flowrate leads to the higher costs of solvent make-up and reclaim, resulting in a lower PIM rating.\u003c/p\u003e \u003cp\u003eHence, even though the tri-amine blends showed an outstanding performance with respect to the energy consumption, when including other parameters, these blends were inferior in performance compared to AMP + PZ binary blends (see Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Modified configuration\u003c/h2\u003e \u003cp\u003eThe ratings reported in Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e are obtained based on the conventional process configuration. In order to investigate the effect of the process configuration on the simulation results, a modified process configuration, namely, intercooled absorber (ICA) was simulated in which the best-performing solvent blend (i.e., 25% AMP + 5% PZ + 70% H\u003csub\u003e2\u003c/sub\u003eO wt.% obtained in conventional configuration studies), was used.\u003c/p\u003e \u003cp\u003eIn the ICA process configuration, a fraction of the solvent in the absorber is withdrawn, cooled, and then fed to the absorber (Le Moullec et al., 2014). Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e depicts an ICA process configuration used in the simulation in which one cooling stage was used. A 22-stage colums (where stages are numbered from top to bottom) was utilized in the simulation amongst which stage 17 was primarily attempted as the cooling stage. After changing the stage number of the cooling stage it was found that stage 15 resulted in an optimal performance of the process. Table\u0026nbsp;\u003cspan refid=\"Tab14\" class=\"InternalRef\"\u003e14\u003c/span\u003e shows the results obtained using stage number 15 as well as those obtained using stage number 17 as the cooling stage. Similar results can be observed in both scenarios however, more stable simulations were achieved using stage 15 as the cooling stage. It can be inferred that using the stages below 15 may result in unstable simulation runs which can cause convergence errors. As indicated in Table\u0026nbsp;\u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003e13\u003c/span\u003e, different exiting and entering stages were chosen to assess the column performance (cases 1–8). Only one cooling stage was utilized to perform cases 9–12. Even though there are few cases from 1–8 with higher ratings than one cooling stage cases, it can be concluded that implementing one cooling stage would be more practical, therefore the results of cases 1–8 were ignored and presented here merely for illustration purposes. The results from the PIM showed that amongst the cases utilizing stage 17 and 15 as the cooling stage, case 12 (which uses stage 15 as cooling stage) yielded the best rating.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab14\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 14\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdditional exploration of employing stage 15 as a cooling stage through an expanded range of split fractions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSplit Fraction\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCooling Stage\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStripper Reboiler Duty (MW)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntercooler Duty (MW)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSolvent Flow Rate (ton/hr)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTemperature Difference in Intercooler (°C)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEstimated Rating\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e142.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.581\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1720\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.429\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.953\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.453\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e139.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.977\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1684\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.471\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e138.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.774\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1671\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.483\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e138.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-8.412\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1660\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.483\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e*Convergence errors\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eIn an attempt to obtain higher ratings, the split fraction, as well as the solvent lean loading were increased. For this purpose, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab14\" class=\"InternalRef\"\u003e14\u003c/span\u003e firstly the split fraction range was extended to see whether a maximum could be achieved at some point. The results in Table\u0026nbsp;\u003cspan refid=\"Tab14\" class=\"InternalRef\"\u003e14\u003c/span\u003e show that the split fractions 0.5 and 0.6 produce a rating of 1.483 which is the highest obtained. Since the split fraction 0.6 did not improve the results significantly, it is decided to select the split fraction 0.5 for further investigations. Ultimately, the results obtained confirm that when using stage 15 as cooling stage, with a split fraction of 0.5 and a solvent lean loading equal to 0.2 mol CO\u003csub\u003e2\u003c/sub\u003e/ mol amine, the ICA configuration produces a maximum rating of 1.483 which is an improvement of 9% comparing to that of the conventional configuration (1.359). The simultaneous effect of mitigated energy requirements as well as reduced solvent supplies improved the rating in the ICA configuration which also can affect the cost of other parameters, like waste disposal.\u003c/p\u003e \u003cp\u003eThis study focused on selected amines and its blends. It is possible to extend the work to other promising amines and blends, and apply the same simulations to assess the performance against those investigated in this work. In addition, alternate process configurations and modifications to the capture unit design are possible. Using a rate-based model instead of an equilibrium model for the simulations could also be considered for future work, especially where equipment design is concerned. These are recommendations for continuation of this work in assessing solvent blends and process modifications for improved carbon capture techniques with energy reductions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe performance of the mixture of various aqueous amine solvents for post-combustion CO\u003csub\u003e2\u003c/sub\u003e capture from a coal-fired power plant was examined by means of a performance indicator model. A solvent of 30 wt. % AMP was used as the baseline instead of the preliminary 30 wt. % MEA, since previous investigations carried out have already proven that AMP is a more promising agent for CO\u003csub\u003e2\u003c/sub\u003e capture than MEA. Different blends of aqueous solvent of AMP or MDEA with piperazine (PZ) were assessed, as well as ternary amine aqueous blends with AMP, MDEA, and PZ. In the case of binary amine mixtures, the overall amine concentrations of 30 wt.% and 40 wt.% were assessed, whereas the overall amine concentration of the ternary amine blends were 45 wt.%. It was found that 25% AMP + 5% PZ (wt.%), is the best-performing blend with the highest rating of 1.359. The worst-performing blend was found to be 22% MDEA + 8% PZ, with a rating of 1.004, which was very close to the benchmark solvent of AMP. Up to 5% improvement in ratings was obtained for the MDEA + PZ blends, when increasing the total amine concentration, while for the AMP + PZ blends, it showed a reverse effect on the rating (up to 21% decrease). The ratings for all the binary amines increased with increasing PZ concentration. For the binary blends of AMP + PZ, an increase of 7% and 8% in ratings were obtained for the overall amine concentrations of 30 wt. % and 40 wt. %, respectively. For MDEA + PZ blends, ratings increased by 2.5% and 2.7% for total amine concentrations of 30 wt. % and 40 wt. %, respectively. Higher ratings were obtained for the tri-amine blends (1.220–1.269) comparing to the ratings obtained for MDEA + PZ blends (1.004–1.080) but it is lower than that of AMP + PZ blends with a total amine concentration of 30 wt. % (1.270–1.359). Finally, a modification in the process configuration was made (modified intercooled absorber configuration) in order to improve further the rating obtained for the best-performing blend (i.e. 25% AMP + 5% PZ). In this case, the rating was increased to 1.483 (9% improvement of conventional configuration), when using stage 15 (of a 22 stage RADFRAC column) as cooling stage, with a split fraction of 0.5 and a solvent lean loading equal to 0.2 mol CO\u003csub\u003e2\u003c/sub\u003e/ mol amine. Further work is recommended to investigate other and new solvents, which were not part of this study, and examine alternatives to the process configurations, in achieving lower operating costs and energy reductions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgment\u003c/h2\u003e \u003cp\u003eThis work is based upon research supported by the South African Research Chairs Initiative of the Department of Science and Technology and National Research Foundation UID 18606.\u003c/p\u003e\u003ch2\u003eData availability statement\u003c/h2\u003e \u003cp\u003eThe authors declare that the data supporting the findings of this study are available within the paper. Should any raw data files be needed in another format they are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbu-Zahra, M.R., Jansens, P.J., Knudsen, J.N., Goetheer, E.L., 2012. Experimental verification of Equilibrium-Stage and Rate-Based Simulations. International journal of enhanced research in science technology and engineering 1, 37\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbu-Zahra, M.R., Schneiders, L.H., Niederer, J.P., Feron, P.H., Versteeg, G.F., 2007. CO\u003csub\u003e2\u003c/sub\u003e capture from power plants: Part I. A parametric study of the technical performance based on monoethanolamine. International Journal of Greenhouse gas control 1, 37\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdeosun, A., Abu-Zahra, M.R.M., 2013. Evaluation of amine-blend solvent systems for CO\u003csub\u003e2\u003c/sub\u003e post-combustion capture applications. Energy Procedia 37, 211\u0026ndash;218.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmadi, F., 2012. Assessing the Performance of Aspen Plus and Promax for the Simulation of CO\u003csub\u003e2\u003c/sub\u003e Capture Plants, Faculty of Graduate Studies and Research. University of Regina.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli, B.S., Aroua, M., 2004. Effect of piperazine on CO\u003csub\u003e2\u003c/sub\u003e loading in aqueous solutions of MDEA at low pressure. International journal of thermophysics 25, 1863\u0026ndash;1870.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAliabad, Z.H., Mirzaei, S., 2009. Removal of CO\u003csub\u003e2\u003c/sub\u003e and H\u003csub\u003e2\u003c/sub\u003eS using Aqueous Alkanolamine Solusions. International Journal of Chemical, Molecular, Nuclear, Materials and Metallurgical Engineering 3, 49\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArachchige, U.S.P., Melaaen, M.C., 2012. Aspen Plus Simulation of CO\u003csub\u003e2\u003c/sub\u003e Removal from Coal and Gas Fired Power Plants. Energy Procedia 23, 391\u0026ndash;399.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAspenTech., 2015a. Aspen Physical Property System, V8.8. Aspen Technology Inc., Cambridge.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAspenTech., 2015b. Electrolyte NRTL Activity Coefficient Model (GMENRTL), Aspen Plus Help. Aspen Technology Inc., Bedford, Massachusetts.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAustgen, D.M., Rochelle, G.T., Chen, C.C., 1991. Model of vapor-liquid equilibria for aqueous acid gas-alkanolamine systems. 2. Representation of hydrogen sulfide and carbon dioxide solubility in aqueous MDEA and carbon dioxide solubility in aqueous mixtures of MDEA with MEA or DEA. Industrial \u0026amp; Engineering Chemistry Research 30, 543\u0026ndash;555.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBr\u0026uacute;der, P., Grimstvedt, A., Mejdell, T., Svendsen, H.F., 2011. CO\u003csub\u003e2\u003c/sub\u003e capture into aqueous solutions of piperazine activated 2-amino-2-methyl-1-propanol. Chemical Engineering Science 66, 6193\u0026ndash;6198.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCanadian Clean Power Coalition, 2013. Capture Cost Definitions Facts Sheet.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChavarro Montenegro, H.J., 2011. Novel Solvents for CO\u003csub\u003e2\u003c/sub\u003e Capture. Flowsheet Analysis, School of Chemical Engineering and Analytical Science, Faculty of Engineering and Physical Sciences. University of Manchester.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD'Alessandro, D.M., Smit, B., Long, J.R., 2010. Carbon dioxide capture: prospects for new materials. Angewandte Chemie International Edition 49, 6058\u0026ndash;6082.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDash, S.K., Bandyopadhyay, S.S., 2016. Studies on the effect of addition of piperazine and sulfolane into aqueous solution of N-methyldiethanolamine for CO\u003csub\u003e2\u003c/sub\u003e capture and VLE modelling using eNRTL equation. International Journal of Greenhouse Gas Control 44, 227\u0026ndash;237.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDash, S.K., Samanta, A., Samanta, A.N., Bandyopadhyay, S.S., 2011. Vapour liquid equilibria of carbon dioxide in dilute and concentrated aqueous solutions of piperazine at low to high pressure. Fluid Phase Equilibria 300, 145\u0026ndash;154.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDash, S.K., Samanta, A.N., Bandyopadhyay, S.S., 2012. Experimental and theoretical investigation of solubility of carbon dioxide in concentrated aqueous solution of 2-amino-2-methyl-1-propanol and piperazine. The Journal of Chemical Thermodynamics 51, 120\u0026ndash;125.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaya, A., 2017. Development of a performance indicator for carbon capture applications. University of Kwa-Zulu Natal.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDerks, P.W.J., 2006. Carbon dioxide absorption in piperazine activated N-methyldiethanolamine. University of Twente, The Netherlands.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEachus, A.C., Bollmeier, A.F., 2000. Alkanolamines from Nitro Alcohols, Kirk-Othmer Encyclopedia of Chemical Technology. John Wiley \u0026amp; Sons, Inc.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErfani, A., Boroojerdi, S., Dehghani, A., 2015. Simulation Of An Operational Amine Based CO\u003csub\u003e2\u003c/sub\u003e Removal Plant As An Example Of CO\u003csub\u003e2\u003c/sub\u003e Capture At Coal-Fired Power Plants. Petroleum \u0026amp; Coal 57, 85\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFisher, K.S., Beitler, C., Rueter, C., Searcy, K., Rochelle, G.T., Jessim, M., 2005. Integrating MEA regeneration with CO\u003csub\u003e2\u003c/sub\u003e compression and peaking to reduce capture costs. US Department of Energy.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFisher, K.S., Searcy, K., Rochelle, G.T., Ziaii, T., Schubert, C., 2007. Advanced Amine Solvent Formulations and Process Integration for Near-Term CO\u003csub\u003e2\u003c/sub\u003e Capture Success. US Department of Energy.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFreeman, S.A., Dugas, R., Van Wagener, D.H., Nguyen, T., Rochelle, G.T., 2010. Carbon dioxide capture with concentrated, aqueous piperazine. International Journal of Greenhouse Gas Control 4, 119\u0026ndash;124.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFreguia, S., Rochelle, G.T., 2003. Modeling of CO\u003csub\u003e2\u003c/sub\u003e capture by aqueous monoethanolamine. 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Industrial \u0026amp; Engineering Chemistry Research 48, 9233\u0026ndash;9246.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-environmental-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"IJER","sideBox":"Learn more about [International Journal of Environmental Research](https://www.springer.com/journal/41742)","snPcode":"41742","submissionUrl":"https://www.editorialmanager.com/ijer/default2.asp...\n","title":"International Journal of Environmental Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"CO2 capture, Performance, Post-combustion, Amines, Asorption, Process simulation","lastPublishedDoi":"10.21203/rs.3.rs-5385962/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5385962/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eProcess simulations serve as an efficient and cost-effective method for evaluating the performance of various amine solvents and their blends in CO₂ capture technologies. This study utilized a performance indicator model to assess the effectiveness of n-methyldiethanolamine (MDEA) and 2-amino-2-methyl-1-propanol (AMP) blends, supplemented with the activator piperazine (PZ). Key parameters such as solvent flow rates, equipment heat duties, and associated costs including energy consumption and carbon taxes were incorporated into the analysis. Among the tested formulations, a binary blend consisting of 25 wt.% AMP and 5 wt.% PZ demonstrated superior performance, achieving a 35% improvement over the baseline 30 wt.% AMP solvent. Further enhancement was realized through the implementation of absorber intercooling (ICA) within the simulation design, which elevated the performance rating by an additional 9%. These results highlight the significant potential of optimizing solvent compositions and incorporating innovative process configurations to improve the efficiency of post-combustion CO₂ capture systems. The findings provide valuable insights for developing more effective and sustainable approaches to reducing carbon emissions in large-scale industrial applications.\u003c/p\u003e","manuscriptTitle":"Advanced Amine Solvent Strategies for Efficient CO2 Capture in Post-Combustion Systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-19 09:54:36","doi":"10.21203/rs.3.rs-5385962/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2025-04-30T14:19:21+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-11-05T14:30:07+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-05T13:18:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-05T06:47:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Environmental Research","date":"2024-11-04T02:52:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-environmental-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"IJER","sideBox":"Learn more about [International Journal of Environmental Research](https://www.springer.com/journal/41742)","snPcode":"41742","submissionUrl":"https://www.editorialmanager.com/ijer/default2.asp...\n","title":"International Journal of Environmental Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"67d8e131-68e6-498b-986b-30776e1c6b69","owner":[],"postedDate":"November 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-08T15:58:11+00:00","versionOfRecord":{"articleIdentity":"rs-5385962","link":"https://doi.org/10.1007/s41742-025-00877-6","journal":{"identity":"international-journal-of-environmental-research","isVorOnly":false,"title":"International Journal of Environmental Research"},"publishedOn":"2025-09-05 15:56:51","publishedOnDateReadable":"September 5th, 2025"},"versionCreatedAt":"2024-11-19 09:54:36","video":"","vorDoi":"10.1007/s41742-025-00877-6","vorDoiUrl":"https://doi.org/10.1007/s41742-025-00877-6","workflowStages":[]},"version":"v1","identity":"rs-5385962","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5385962","identity":"rs-5385962","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}