A Feasibility Study of Condensate Fractionation Unit (CFU) Process Using Aspen HYSYS Simulation Software in the Petroleum Refinery Industry of Bangladesh

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Ryshur Rahman Turin, Md. Moniruzzaman, Anisul Islam Suva, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6495903/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The design of distillation columns presents significant challenges due to the complexity of various refinery configurations, such as tray architecture, and operational issues including flooding and weeping. This study aims to investigate and simulate critical operational parameters of a distillation column utilizing the Aspen HYSYS process simulation software. A steady-state model of the Condensate Fractionation Unit (CFU) was developed and analyzed using the Peng-Robinson thermodynamic equation of state in conjunction with a stage-by-stage calculation method. The feedstock utilized in this simulation was a secondary condensate stream derived from natural gas processing, consisting of hydrocarbons ranging from propane (C₃) to octadecane (C₁₈). Notably, the volumetric flow rates for light naphtha, heavy naphtha, kerosene, JBO, and diesel products were estimated through simulation as 4350, 15600, 3000, 2200, and 1950 in kg/h (unit), respectively. However, actual plant measurements indicated values of 4300, 15900, 4300, 1600, and 1000 in kg/h (unit), respectively. These findings highlight the effectiveness of simulation tools in improving process insight, facilitating engineering decisions, and enhancing the congruence between conceptual design and operational performance. Aspen HYSYS CFU Petroleum Refinery Simulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 I. INTRODUCTION Petroleum refining is defined as the segregation of natural gas condensate into distinct fractions, followed by the processing of these fractions to produce market-ready products. That means A refinery is a network of industrial units, the number and configuration of which are determined by the diversity of products being synthesized. To ensure a balanced and efficient operation, appropriate refinery processes must be selected, and product outputs must be aligned with market demand. During the production of materials from the lower-boiling fractions of petroleum, a certain quantity of higher-boiling components is inevitably generated. If these heavier fractions cannot be marketed—such as in the form of residual fuel oil—they will eventually accumulate, leading to the saturation of refinery storage capacity [ 1 ]. To prevent such operational bottlenecks, a refinery must maintain a high degree of operational flexibility, enabling it to adapt its processes as required. This often necessitates the integration of additional technologies, such as a hydrotreating process to remove sulphur, oxygen, nitrogen, etc., or a catalytic reforming unit for the conversion of low-octane naphtha into high-octane reformates. Owing to the significant interdependence between heat and mass transfer phenomena during the distillation of natural gas condensate, along with variations in the thermodynamic characteristics of both liquid and vapor streams— which are influenced by pressure and temperature— it becomes highly challenging to determine product composition and rates from the unit using simplified models or conventional personal computing tools [ 2 ]. At present, process simulation is regarded as one of the most effective tools available, despite computationally intensive tools being available. It can be employed for a wide range of applications, including system design, performance optimization, process control, and the prediction of system behavior in response to changes in operating parameters. This analytical task can be performed efficiently through simulation, rather than attempting to understand the operational dynamics of a unit or plant solely through extensive experimental trials. However, it has been clearly demonstrated that recent progress in computer-based simulation platforms and sensor systems is contributing to the elimination of the previously identified limitations. As a result, new opportunities for dependable process optimization are being created. Modelling platforms such as Aspen HYSYS, Simu Solv, SPEEDUP, Aspen PLUS, and gPROMS have significantly minimized the duration and labor required to develop accurate and reliable process models. The challenges encountered in both plant operations and business environments are significantly addressed through the application of modelling and simulation. From an operational perspective, factors such as elevated feed rates, enhanced yields of high-value products, continuous fluctuations in raw material quality, and other disturbances within process units are commonly analyzed using simulation techniques. The quality of returned products and improvements in energy efficiency are largely influenced by plant operations and the effectiveness of process optimization. These tasks are notably facilitated by simulation, which serves as a valuable tool in enhancing operational reliability and performance [ 3 ]. II. METHODOLOGY In refinery technology, the refining processes can be done by either physical segregation or chemical conversion [ 4 ]. Firstly, in the chemistry laboratory, the distillation report was prepared according to the boiling point differences, serving as one of the primary input parameters for the present simulation study. The basis for the condensate feed rate was established at 27100 kg per hour (kg/hr). A fractionation column was utilized to obtain products— light naphtha as the top product, heavy naphtha as the second top product, kerosene and JBO (Jute Batching Oil) from the side stripper column, and diesel as the bottom product. The corresponding input data were replicated within the simulation procedure. In this simulation, the pseudo-component generation and plate-by-plate calculation approach were employed based on ASTM D86 laboratory input data. For this study, ASTM D86 data, acquired from the refinery laboratory, were available and used as input. The Aspen HYSYS version 10.0 adopts several numerical iterations and equations for simulation, as it is generally suitable for a wide range of process conditions. During property generation, two exclusive databases are accessible within Aspen HYSYS: HYSYS Properties and Aspen Properties. For this simulation, the HYSYS Properties database was chosen to generate the thermophysical properties of the process streams. In this simulation process, the selected thermodynamic fluid package was Peng-Robinson, primarily due to its widespread applicability in refinery simulations and its ability to accommodate hypothetical pseudo-components effectively [ 5 ]. The Aspen HYSYS selection criteria was mentioned here under: Table 1 Simulation Model Selection Criteria Aspen HYSYS Model Version 10.0 Fluid Package Selection Peng-Robinson Selection of Properties HYSYS based Properties Type of Calculation Plate by plate calculation Simulation category Pseudo component generation III. ASSAY ANALYSIS AND OIL CHARACTERIZATION The fractional distillation method operates on the fundamental concept that various compounds possess True Boiling Points (TBP). For instance, natural gas condensate (NGC) comprises valuable fractions such as kerosene and naphtha—where naphtha is converted into gasoline, and kerosene is utilized in jet fuel production. Upon vaporization of a blend containing these components, followed by controlled cooling, kerosene undergoes condensation at a comparatively higher temperature than naphtha. As the temperature decreases, kerosene is condensed initially, whereas naphtha condenses subsequently [6]. Figure 1 illustrates the configuration interface of the oil manager within Aspen HYSYS v10.0, specifically focused on the Input Assay section where raw condensate characterization data are provided using the distillation method. In this simulation, the ASTM D86 distillation was done to find out the assay data type for defining the condensate properties. The assay input is provided based on boiling point differences, with corresponding temperatures ranging from 50°C to 284°C at an increment of 10% volume. An assay refers to a dataset obtained through laboratory analysis of crude oil, utilized to characterize its distillation profile and various physical and chemical attributes. This dataset includes fractions, commonly categorized by specific boiling point intervals, along with associated properties for each fraction. Within refinery planning operations, a crude evaluation is frequently conducted to determine the breakeven cost of a particular crude type relative to a standard basket used by the facility. Traditional assay datasets generally comprise limited boiling point data, density values, and other key property measurements, either for selected fractions or for the crude oil as a whole [7]. Figure 2 shows the distillation curve generated from ASTM D86 data to analyze the boiling range distribution of the raw condensate feed. The horizontal axis represents the cumulative volume percent distilled, ranging from 0% to 100%, while the vertical axis displays the corresponding boiling temperatures in degrees Celsius. The plotted line, marked with red diamond symbols, shows a gradual increase in temperature as distillation progresses. This indicates that the lighter components, like naphtha, evaporate at lower temperatures, and heavier hydrocarbons, e.g., diesel, require significantly higher temperatures to vaporize. The curve begins at approximately 50°C, where the lightest fraction starts to distil, and gradually rises, reaching temperatures above 280°C near the 100% distillation point. This profile confirms the presence of a wide boiling range in the feed, with both volatile and less-volatile components. The sharp rise towards the end indicates a desired quantity of heavy products like JBO and diesel, emphasizing the need for appropriate cut points in the column design to optimize separation efficiency. Figure 2 was generated directly from the simulation using input data collected from the refinery laboratory. This distillation profile was used to define pseudo-components and highlights the modelling of the feed behavior in the fractionation column within the simulation study. Figure 3 also explains the cut distribution of the simulated feedstock. The distribution was performed based on the liquid volume fraction of the total oil, and it displays how the raw condensate was divided into multiple boiling range fractions (or “cuts”) through various temperature ranges. The cut input information on the left side of the figure shows the temperature endpoints for each defined fraction: Light naphtha up to 80°C Heavy naphtha up to 180°C Kerosene up to 240°C JBO (Jute Batching Oil) up to 350°C Heavy diesel up to 380°C Residue extending to 1200°C On the right side of Figure 3, a bar chart presents the relative volume distribution of each cut. The horizontal axis represents the boiling point in degrees Celsius, while the vertical axis indicates the liquid volume fraction of the total oil. From the bar chart, it is evident that the heavy naphtha cut (green bar) constitutes the largest volume fraction of the total blend. The light naphtha (red), kerosene (blue), and JBO (magenta) follow in decreasing volume contribution. The heavy diesel (cyan) and residue make up smaller portions, indicating that the feedstock contains a certain amount of lighter to middle distillates and a comparatively lower proportion of heavy components. This graphical distribution shows an understanding of the compositional contents of the feed and is very important for column design and product yield optimization. The data was illustrated after processing the assay input and applying simulation calculations to determine the product yields based on true boiling point (TBP) behavior. IV. SIMULATION OF CONDENSATE FRACTIONATION UNIT The process flow diagram (PFD) represents the Condensate Fractionation Unit (CFU), primarily focusing on the distillation and side stripper systems used for the separation of petroleum fractions. The central unit in the diagram is labelled as the distillation column (C-101). It is designed to separate natural gas condensate into several refinery products based on their true boiling point (TBP) ranges. The column consists of multiple stages, indicated by the numbered trays ranging from 1 to 37, with the feed entering the column at tray 11. Lighter components rise to the top of the column, while heavier components proceed towards the bottom. At the top of the distillation column, overhead vapors are condensed in a condenser unit, where the condensed liquid is partially recycled back to the column as reflux to enhance liquid-vapor separation, while the non-condensable gases are discharged as off-gas. Off-gases are discharged through flaring. The condensed overhead liquid product is collected as light naphtha. From various intermediate draw stages of the fractionation column, heavy naphtha is collected from tray 3, and side streams are withdrawn for further separation. One such stream is directed to the kerosene stripper column, where lighter portion separation is achieved using reboiling. The bottom product from this stripper is collected as kerosene, and the top is returned to the distillation column as kerosene stripper return. Similarly, another side stream is routed to the JBO (Jute Batching Oil) stripper column, where a similar process occurs. The segregated bottom product is collected as JBO, and a return stream is recycled back to the C-101. Both side strippers are equipped with reboilers that provide the necessary heat energy to enhance component separation. These reboilers receive a portion of the side draw streams, heat them to provide the required vaporization, and return the vapor to the strippers. This operation is key to improving product purity and ensuring efficient separation. At the bottom of the fractionation column, the heavier portion of feed is drawn and sent to a reboiler (H-101) to raise the temperature of the bottom product before it is further processed or sent to the storage tank. This stream is identified as diesel, which is one of the main products of the distillation process. So, this process flow diagram (PFD) illustrates a typical distillation section of the CFU process, where condensate feed is separated into multiple fractions, including light naphtha, heavy naphtha, kerosene, JBO, and diesel, using a combination of main distillation and side stripping columns, supported by condensers and reboilers to maintain heat balance and product purity. The Heat and Material Balance (HMB) for all process streams within the CFU operation has been illustrated in Fig. 5, as obtained from the simulation results. Overall, this heat and material balance diagram provides a comprehensive quantitative overview of the system's operating conditions and energy distribution. It is essential in validating process design, identifying thermal efficiencies, and optimizing product recovery in distillation column operations. Figure 6 presents the column specification and performance summary for the distillation column C-101, simulated using Aspen HYSYS with the Peng-Robinson thermodynamic package. Column performance indicators can be reviewed under the "Performance" section within the column section, where graphical outputs such as "Temperature vs. Tray Position from Top" provide insight into the internal temperature distribution across trays. For tray design within HYSYS, the tray sizing feature is enabled through the utility panel (accessed via Tools → Utilities → Tray Sizing). Upon selection of bubble cap trays with a standard vertical spacing of 24 inches (2 feet) and retention of all default configuration parameters, the calculated column diameter was approximately 4.921 feet. Output data concerning tray count, column dimensions, and duties of the reboiler and condenser can be extracted from the simulation results. These parameters are typically integrated into economic evaluation tools, such as cost estimation models or spreadsheets, to facilitate optimization of the total annual production cost. At the top of the figure, a temperature profile plot is shown as a function of tray position, with tray numbers increasing from top to bottom. This profile reveals a typical ascending temperature gradient from the top of the column (around 60 to 100°C) to the bottom (approximately 340°C), characteristic of atmospheric fractional distillation. Such a profile ensures that lighter components vaporize and condense near the top, while heavier components are separated near the bottom due to their higher boiling points. Below the plot, the specifications table lists critical design parameters and target setpoints for the simulation. The reflux ratio is set to 6.0, which is a typical value for refinery columns to ensure effective rectification of the overhead product (light naphtha). The distillate rate is set to 4350 kg/h, representing the flowrate of condensed top product withdrawn. Other key parameters include heavy naphtha flowrate (15,600 kg/h), kerosene stripper product flowrate (3000 kg/h), and JBO stripper product flowrate (2200 kg/h). Degrees of freedom show zero error, indicating the model has converged successfully with consistent mass and energy balances. The green bar at the bottom confirms convergence, validating that the simulation meets all design constraints. This setup is representative of a stable and operationally viable refinery distillation process, ensuring accurate prediction of product flows, compositions, and energy requirements essential for process optimization and scale-up. Figure 7 presents the tray-wise stage efficiency settings for the atmospheric distillation column (C-101) in Aspen HYSYS. In the simulated configuration, a total of 37 bubble cap trays were incorporated within the main distillation column to establish vapor–liquid equilibrium. The tray efficiency was uniformly assigned a value of 0.75 (or 75%) across all stages, with the condenser operating at full efficiency. The design and calculations related to the bubble cap trays were performed in accordance with the methodology outlined in Design of Equilibrium Stage Processes by Bufford D. Smith [8]. Thermodynamic and flow conditions, including pressure, temperature, as well as vapor and liquid flow rates, were determined individually for each tray. These values are automatically computed by the simulator and used to define the internal column behavior, as shown in the corresponding simulation output. V. COLUMN HYDRAULICS In atmospheric distillation systems, the phenomenon referred to as "weeping" arises when liquid leaks through the perforations of sieve trays due to inadequate upward vapor flow. Such a condition is considered unfavorable, as the intended path of the liquid is across the tray surface and subsequently through the downcomer. This phenomenon defines the minimum threshold for vapor velocity in sieve-tray column operations. On the opposite end, "flooding" occurs when excessive accumulation of liquid within the column obstructs downward liquid movement, caused by excessively high vapor flow rates. This condition establishes the maximum permissible vapor velocity for the effective functioning of sieve-tray columns [9]. “Foaming” observed in a distillation column is characterized by the volumetric expansion of liquid, which enhances the interfacial contact between the liquid and vapor phases [10]. Figure 8 illustrates a detailed hydraulic analysis of the main distillation column's internal behavior, focusing on tray 1. It provides insights into the vapor-liquid separation through the column trays and how closely the operating conditions approach mechanical limits, such as flooding, weeping, entrainment, and puking. The operating point on the weir load plot for tray 1 lies within the stable hydraulic envelope. Overall, the “Stages view” displays a blue shading pattern, indicating that the distillation column, designed with 37 bubble cap trays, is operating under stable conditions. Explanation of Key Hydraulic Phenomena: Flooding: This is focused by the "100% Downcomer Backup" line and the vapor-liquid envelope . In the chart, the operating point (black dot) is well below the flooding boundary, indicating safe operation. Weeping: Defined by the 0% weep line (lower pink line). The operating point is significantly above this line, confirming that weeping is not a concern in this stage. Entrainment: Implied by operation near the upper limits of vapor velocity. High vapor velocities can carry liquid droplets upward, reducing tray efficiency. Figure 8 indicates proximity to flooding where entrainment typically begins. The hydraulic envelope's upper edge often correlates with entrainment risk. This simulation suggests entrainment could occur if the vapor load increases further . Puking: A severe form of flooding where frothy liquid and vapor are violently pushed up through the column. The risk of puking increases when operating near or above 100% flood , especially with foaming or highly turbulent conditions. The chart implies that Stage 1 is on the edge , and any operational upsets could initiate puking. VI. OUTPUT OF THE SIMULATION The simulation process in HYSYS is executed using a concurrent modular methodology. A flowsheet model is constructed as an assembly of sub-flowsheets (SFS or modular blocks), which are interconnected via process streams. Each sub-flowsheet comprises a set of unit operations and associated streams that are suitable for simultaneous resolution [11]. Throughout the simulation process, each sub-flowsheet is resolved utilizing one of the standard solvers available in HYSYS, specifically the sequential quadratic programming (SQP) optimizer [12]. The interaction between the simulator and the flowsheet is maintained until the specified convergence criteria are satisfied [13]. Figure 9 presents the streamwise tabulated output of a simulated condensate fractionation unit. The table summarizes thermodynamic and flow parameters of various process streams, including reflux, return and draw streams from light naphtha, kerosene and JBO (Jute Batching Oil) strippers, feed and product outlets, and streams directed to heaters and condensers. The temperature of the stream ranges from 50°C at the condenser to 328.3°C at the bottom of the distillation column, reflecting the continuous heating and fractionation of the natural gas condensate. The pressure of the condensate feed is relatively stable, ranging almost 3 bar. The highest values are seen in the heater and diesel streams at 1.05 bar, supporting consistent vapor-liquid equilibrium conditions in the column. The condensate feed has a mass flow of 27100 kg/h, indicating a moderate feed throughput. The Kerosene Stripper Draw stream exits the column at 167.2°C and 0.7833 bar, with a mass flow of 6411 kg/h, typical for kerosene product recovery. The JBO Draw stream exits at 265.9°C and 1.032 bar, with a mass flow of 15,650 kg/h, making it a major middle distillate product in this case. All streams shown are in liquid phase. Figure 10 represents the final simulation output for the main distillation column, summarizing key process stream data across all primary product and intermediate draws. It displays thermodynamic and flow parameters for streams, including the condensate feed, off-gas, light and heavy naphtha, diesel, kerosene, and JBO products. The condensate feed enters at 80°C and 3.0 bar with a mass flow of 27,100 kg/h. The off-gas stream is fully vaporized at 50°C and 0.73 bar, indicating the separation of lighter components at the top of the column. Light and heavy naphtha exit the column at progressively higher temperatures, confirming their separation based on volatility. The diesel stream emerges at 328.3°C and 1.05 bar with a mass flow of 1950 kg/h, showing characteristics of a bottom product. Kerosene and JBO, separated at 201.6°C and 299°C, respectively, indicate efficient recovery of these boiling components. The thermodynamic properties such as molar enthalpy, entropy, and heat flow vary significantly with stream composition and temperature, showing consistent trends across the boiling point range. These final values demonstrate a reliable simulation of the atmospheric distillation process, confirming appropriate separation, product purity, and energy distribution within the column. The convergence status at the bottom confirms that the simulation ran successfully without errors, providing reliable data for analysis and optimization of the distillation unit. VII. RESULT AND DISCUSSION Table 2: Comparison between Simulation Data and Real Plant Data Parameters Units Simulation Data Plant Data Column Top Pressure Bar 0.73 0.73 Column top temperature °C 93.27 88.5 Column Bottom Pressure Bar 1.05 1.05 Column bottom temperature °C 328.3 330 Feed flowrate Kg/hr 27100 27100 Feed preheating temperature °C 80 77 Heavy naphtha draw temperature °C 128.7 126 Kerosene product draw temperature °C 201.6 227 JBO product draw temperature °C 299 309 Reboling flowrate Kg/hr 109000 109000 Light naphtha flowrate Kg/hr 4350 4300 Heavy naphtha flowrate Kg/hr 15600 15900 Kerosene flowrate Kg/hr 3000 4300 JBO flowrate Kg/hr 2200 1600 Diesel flowrate Kg/hr 1950 1000 Temperature at Heater (H-101) to Column flow °C 341.1 357 Table 2 provides a comprehensive comparison between simulation results and actual plant data, highlighting the accuracy and reliability of the developed model in representing the real operation of the distillation column. The outcomes derived from the process simulations, illustrated in Figures 5, 6, 9 and 10, were systematically compared against actual operational data, as shown in the accompanying table. A detailed comparison of the key operational parameters from the plant with those obtained through simulation revealed close alignment in many aspects. Notably, the volumetric flow rates and associated temperatures for light naphtha, heavy naphtha, kerosene, JBO, and diesel products were estimated through simulation as 4350, 15600, 3000, 2200, and 1950 in kg/h (unit), respectively. However, actual plant measurements indicated values of 4300, 15900, 4300, 1600, and 1000 in kg/h (unit), respectively. The temperature recorded in the plant data at both the top and bottom of the column compared to the simulated values was nearly similar, while a temperature drop was observed in the kerosene product draw. Pressure measurements at both the top and bottom of the column remained consistent across simulation and plant conditions. Furthermore, the temperature of the stream from the heater (H-101) to the column is 341.1°C in the simulation, while the actual plant operates at 357°C. This variation could be attributed to differences in heat exchanger efficiency or bypass effects in the plant setup not modelled in the simulation. Finally, the comparison demonstrates a close correlation between the simulation and plant data, with most parameters aligning closely, thereby affirming the model's feasibility for operational analysis and optimization purposes. VIII. CONCLUSIONS The conducted research successfully demonstrates the capability of process simulation to replicate real-world operational data of a distillation column with commendable accuracy. This study focused on the simulation of a condensate fractionation unit (CFU), where a comprehensive Process Flow Diagram (PFD) was developed, including detailed heat and mass balance calculations. Specifically, simulated and measured flow rates (in kg/h) for light naphtha, heavy naphtha, kerosene, JBO, and diesel were 4350 vs. 4300, 15600 vs. 15900, 3000 vs. 4300, 2200 vs. 1600, and 1950 vs. 1000, respectively. The close alignment between simulation results and actual plant measurements across key parameters—including pressure, temperature, flowrates, and product draws—confirms the robustness of the developed model and its applicability for performance evaluation and operational planning. Although the simulations were performed under steady-state conditions, the approach holds the potential for expansion into dynamic modelling. This transition could support advanced applications, including operator training simulators, transient process behavior analysis, and real-time optimization. The model proves to be a feasible predictive tool, enabling insight into column behavior under various operating conditions. It also offers a fundamental foundation for further studies: control strategy development and energy efficiency assessments. The overall findings reinforce the value of simulation in enhancing process understanding, supporting decision-making, and ensuring improved alignment between design assumptions and plant realities. Declarations Author Contribution M.R.R.T. wrote the main manuscript text, M.M. prepared the figures, A.I.S. provided validation and J.U.A. supervised the whole research. All authors reviewed the manuscript. References J. G. SPEIGHT, Handbook of Petroleum Refining, New York: CRC Press: Taylor & Francis, 2017, p. 143. A. N. Khalaf, "Steady State Simulation of Basrah Crude Oil Refinery Distillation Unit," Thi_Qar University Journal for Engineering Sciences, p. 29, 2018. Q. A. H. S. K. T. H. a. D. M. S. Mohammad Hasibul Hasan, "Simulation of Crude Distillation Unit of Eastern Refinery Limited (ERL) using ASPEN PLUS," in International Conference on Materials, Electronics & Information Engineering, ICMEIE-2015 . T. A. A. A. MOHAMED A. FAHIM, Fundamentals of Petroleum Refining, Elsevier, 2010. L. A. B. J. M. L. A. R. E. E. Juan Pablo Gutierrez, "Thermodynamic Properties for the Simulation of Crude Oil Primary Refining," Juan Pablo Gutierrez et al. Int. Journal of Engineering Research and Applications, p. 192, 2014. A. Ashraf, "Distillation process of Crude oil," Research Gate, 2012. S. C. D.K. Varvarezos, "Crude Evaluation for Refinery Planning Using a Molecular-Based Assay Characterization," in 13 AIChE Annual Meeting , 2013. B. D. Smith, Design of equilibrium stage processes, New York: McGraw-Hill, 1963. M. A. Minhas, "Entrainment Flooding and Weeping Velocities," Chemical Engineering, p. 24, January 2025. D. W. S. W.M.H.E. WIJESINGHE, "Design of o-xylene Plate/Tray Distillation Column," University of Moratuwa, Moratuwa, 2016. M.J.BOX, "A New Method of Constrained Optimization and a Comparison With Other Methods," The Computer Journal, vol. 8, no. 1, pp. 42-52, 1965. J. M. J.L. Kuesterand, Optimization Techniques with FORTRAN, New York: McGraw-Hill Book Co., 1973, pp. 35-108. A. D. F. A. M. Mohammed Jibril, "Simulation of Kaduna Refining and Petrochemical Company (KRPC) Crude Distillation Unit (CDU I) Using HYSYS," International Journal of Advanced Scintific Research and Technology, vol. 1, no. 2, pp. 1-6, 2012. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-6495903","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":446153101,"identity":"5196bf17-3cdd-479e-acfe-694503c61b11","order_by":0,"name":"Md. 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Moniruzzaman","email":"","orcid":"","institution":"Chittagong University of Engineering \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"","lastName":"Moniruzzaman","suffix":""},{"id":446153103,"identity":"de2554a5-ea4e-4570-a752-b2c902e4a273","order_by":2,"name":"Anisul Islam Suva","email":"","orcid":"","institution":"Institute of Energy Technology, CUET","correspondingAuthor":false,"prefix":"","firstName":"Anisul","middleName":"Islam","lastName":"Suva","suffix":""},{"id":446153104,"identity":"9f4f5055-3089-490b-9b3b-bc7a5445253b","order_by":3,"name":"Jamal Uddin Ahamed","email":"","orcid":"","institution":"Institute of Energy Technology, CUET","correspondingAuthor":false,"prefix":"","firstName":"Jamal","middleName":"Uddin","lastName":"Ahamed","suffix":""}],"badges":[],"createdAt":"2025-04-21 12:08:18","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6495903/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6495903/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81601999,"identity":"1f6f4771-ff20-49b9-be54-e0fa193a2924","added_by":"auto","created_at":"2025-04-29 04:29:39","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":761216,"visible":true,"origin":"","legend":"\u003cp\u003eAssay Data Input in Aspen HYSYS Using ASTM D86 Method\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6495903/v1/9a04095f505f0ebdd1f8cda8.jpeg"},{"id":81601998,"identity":"3804e42b-f49f-4eff-8eef-f6ddbe39909a","added_by":"auto","created_at":"2025-04-29 04:29:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":388585,"visible":true,"origin":"","legend":"\u003cp\u003eDistillation Curve of Input Assay from ASTM D86 Data in Aspen HYSYS\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6495903/v1/d4689404ad61cf04ca9f21d3.png"},{"id":81602317,"identity":"3d22efe1-6bbf-4070-a791-d4b618ac1603","added_by":"auto","created_at":"2025-04-29 04:37:39","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":143686,"visible":true,"origin":"","legend":"\u003cp\u003eCut Distribution Based on Liquid Volume Fraction of Condensate\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6495903/v1/0eaa0c90d5d3f26a3b2f572b.jpeg"},{"id":81602002,"identity":"95c2fc5c-7393-4f24-8388-07b6b98ab2f4","added_by":"auto","created_at":"2025-04-29 04:29:39","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":234412,"visible":true,"origin":"","legend":"\u003cp\u003eProcess Flow Diagram of Atmospheric Distillation Column with Side Strippers in CFU Operation\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6495903/v1/555fe0d1742e0626f4fbc54a.jpeg"},{"id":81602323,"identity":"c7824d11-f6c7-4d35-b0d8-2879203cb375","added_by":"auto","created_at":"2025-04-29 04:37:40","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":949313,"visible":true,"origin":"","legend":"\u003cp\u003eHeat and Material Balance for Process Streams\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6495903/v1/a59363ecee740b5d9293e4ab.jpeg"},{"id":81602855,"identity":"66c117c8-8360-4744-80de-55a530ac3be9","added_by":"auto","created_at":"2025-04-29 04:45:39","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":128466,"visible":true,"origin":"","legend":"\u003cp\u003eColumn Specification and Temperature Profile of Atmospheric Distillation Unit C-101 in Aspen HYSYS\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6495903/v1/ed198462f578eb201ad79f03.jpeg"},{"id":81602010,"identity":"f9a0b89d-7656-49ef-b195-7d6625a2d8a7","added_by":"auto","created_at":"2025-04-29 04:29:40","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":191933,"visible":true,"origin":"","legend":"\u003cp\u003eStage Efficiencies of Each Tray in Atmospheric Distillation Column (C-101)\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6495903/v1/81639ce32104b767af0b8876.jpeg"},{"id":81602856,"identity":"ae4b6bda-3218-46de-9ebd-3677910b79da","added_by":"auto","created_at":"2025-04-29 04:45:39","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":196095,"visible":true,"origin":"","legend":"\u003cp\u003eColumn Hydraulics Analysis of Main Fractionation Column\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6495903/v1/72a1f0232a43ca20d2c0228d.jpeg"},{"id":81602017,"identity":"0fce4260-23ef-424a-99da-84c20f98c8e4","added_by":"auto","created_at":"2025-04-29 04:29:40","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":264717,"visible":true,"origin":"","legend":"\u003cp\u003eThermodynamic parameters of simulated stream properties in CFU operation\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6495903/v1/86a844c2d37f9071f2ce6b7a.jpeg"},{"id":81602320,"identity":"9edf9cf5-a256-44c1-baf6-b3001bfbfd16","added_by":"auto","created_at":"2025-04-29 04:37:39","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":196678,"visible":true,"origin":"","legend":"\u003cp\u003eFinal Simulation Output of Stream Properties in Main Distillation Column\u003c/p\u003e","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6495903/v1/b2a0eccba55b37cc26ea8d6f.jpeg"},{"id":84403799,"identity":"b6f4ed60-6121-4986-8faa-d33b00c869a2","added_by":"auto","created_at":"2025-06-11 14:02:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4334848,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6495903/v1/f2c7b48a-ba41-443d-bed1-b1e050c737d0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Feasibility Study of Condensate Fractionation Unit (CFU) Process Using Aspen HYSYS Simulation Software in the Petroleum Refinery Industry of Bangladesh","fulltext":[{"header":"I. INTRODUCTION","content":"\u003cp\u003ePetroleum refining is defined as the segregation of natural gas condensate into distinct fractions, followed by the processing of these fractions to produce market-ready products. That means A refinery is a network of industrial units, the number and configuration of which are determined by the diversity of products being synthesized. To ensure a balanced and efficient operation, appropriate refinery processes must be selected, and product outputs must be aligned with market demand. During the production of materials from the lower-boiling fractions of petroleum, a certain quantity of higher-boiling components is inevitably generated. If these heavier fractions cannot be marketed\u0026mdash;such as in the form of residual fuel oil\u0026mdash;they will eventually accumulate, leading to the saturation of refinery storage capacity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo prevent such operational bottlenecks, a refinery must maintain a high degree of operational flexibility, enabling it to adapt its processes as required. This often necessitates the integration of additional technologies, such as a hydrotreating process to remove sulphur, oxygen, nitrogen, etc., or a catalytic reforming unit for the conversion of low-octane naphtha into high-octane reformates.\u003c/p\u003e \u003cp\u003eOwing to the significant interdependence between heat and mass transfer phenomena during the distillation of natural gas condensate, along with variations in the thermodynamic characteristics of both liquid and vapor streams\u0026mdash; which are influenced by pressure and temperature\u0026mdash; it becomes highly challenging to determine product composition and rates from the unit using simplified models or conventional personal computing tools [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt present, process simulation is regarded as one of the most effective tools available, despite computationally intensive tools being available. It can be employed for a wide range of applications, including system design, performance optimization, process control, and the prediction of system behavior in response to changes in operating parameters. This analytical task can be performed efficiently through simulation, rather than attempting to understand the operational dynamics of a unit or plant solely through extensive experimental trials.\u003c/p\u003e \u003cp\u003eHowever, it has been clearly demonstrated that recent progress in computer-based simulation platforms and sensor systems is contributing to the elimination of the previously identified limitations. As a result, new opportunities for dependable process optimization are being created. Modelling platforms such as Aspen HYSYS, Simu Solv, SPEEDUP, Aspen PLUS, and gPROMS have significantly minimized the duration and labor required to develop accurate and reliable process models.\u003c/p\u003e \u003cp\u003eThe challenges encountered in both plant operations and business environments are significantly addressed through the application of modelling and simulation. From an operational perspective, factors such as elevated feed rates, enhanced yields of high-value products, continuous fluctuations in raw material quality, and other disturbances within process units are commonly analyzed using simulation techniques. The quality of returned products and improvements in energy efficiency are largely influenced by plant operations and the effectiveness of process optimization. These tasks are notably facilitated by simulation, which serves as a valuable tool in enhancing operational reliability and performance [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e"},{"header":"II. METHODOLOGY","content":"\u003cp\u003eIn refinery technology, the refining processes can be done by either physical segregation or chemical conversion [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Firstly, in the chemistry laboratory, the distillation report was prepared according to the boiling point differences, serving as one of the primary input parameters for the present simulation study. The basis for the condensate feed rate was established at 27100 kg per hour (kg/hr). A fractionation column was utilized to obtain products\u0026mdash; light naphtha as the top product, heavy naphtha as the second top product, kerosene and JBO (Jute Batching Oil) from the side stripper column, and diesel as the bottom product.\u003c/p\u003e \u003cp\u003eThe corresponding input data were replicated within the simulation procedure. In this simulation, the pseudo-component generation and plate-by-plate calculation approach were employed based on ASTM D86 laboratory input data. For this study, ASTM D86 data, acquired from the refinery laboratory, were available and used as input.\u003c/p\u003e \u003cp\u003eThe Aspen HYSYS version 10.0 adopts several numerical iterations and equations for simulation, as it is generally suitable for a wide range of process conditions. During property generation, two exclusive databases are accessible within Aspen HYSYS: HYSYS Properties and Aspen Properties. For this simulation, the HYSYS Properties database was chosen to generate the thermophysical properties of the process streams.\u003c/p\u003e \u003cp\u003eIn this simulation process, the selected thermodynamic fluid package was Peng-Robinson, primarily due to its widespread applicability in refinery simulations and its ability to accommodate hypothetical pseudo-components effectively [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The Aspen HYSYS selection criteria was mentioned here under:\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\u003eSimulation Model Selection Criteria\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\u003eAspen HYSYS Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVersion 10.0\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFluid Package Selection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeng-Robinson\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelection of Properties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHYSYS based Properties\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of Calculation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlate by plate calculation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSimulation category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudo component generation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"III. ASSAY ANALYSIS AND OIL CHARACTERIZATION","content":"\u003cp\u003eThe fractional distillation method operates on the fundamental concept that various compounds possess True Boiling Points (TBP). For instance, natural gas condensate (NGC) comprises valuable fractions such as kerosene and naphtha\u0026mdash;where naphtha is converted into gasoline, and kerosene is utilized in jet fuel production. Upon vaporization of a blend containing these components, followed by controlled cooling, kerosene undergoes condensation at a comparatively higher temperature than naphtha. As the temperature decreases, kerosene is condensed initially, whereas naphtha condenses subsequently [6].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 1 illustrates the configuration interface of the oil manager within Aspen HYSYS v10.0, specifically focused on the Input Assay section where raw condensate characterization data are provided using the distillation method. In this simulation, the ASTM D86 distillation was done to find out the assay data type for defining the condensate properties. The assay input is provided based on boiling point differences, with corresponding temperatures ranging from 50\u0026deg;C to 284\u0026deg;C at an increment of 10% volume.\u003c/p\u003e\n\u003cp\u003eAn assay refers to a dataset obtained through laboratory analysis of crude oil, utilized to characterize its distillation profile and various physical and chemical attributes. This dataset includes fractions, commonly categorized by specific boiling point intervals, along with associated properties for each fraction. Within refinery planning operations, a crude evaluation is frequently conducted to determine the breakeven cost of a particular crude type relative to a standard basket used by the facility. Traditional assay datasets generally comprise limited boiling point data, density values, and other key property measurements, either for selected fractions or for the crude oil as a whole [7].\u003c/p\u003e\n\u003cp\u003eFigure 2 shows the distillation curve generated from ASTM D86 data to analyze the boiling range distribution of the raw condensate feed. The horizontal axis represents the cumulative volume percent distilled, ranging from 0% to 100%, while the vertical axis displays the corresponding boiling temperatures in degrees Celsius. The plotted line, marked with red diamond symbols, shows a gradual increase in temperature as distillation progresses. This indicates that the lighter components, like naphtha, evaporate at lower temperatures, and heavier hydrocarbons, e.g., diesel, require significantly higher temperatures to vaporize.\u003c/p\u003e\n\u003cp\u003eThe curve begins at approximately 50\u0026deg;C, where the lightest fraction starts to distil, and gradually rises, reaching temperatures above 280\u0026deg;C near the 100% distillation point. This profile confirms the presence of a wide boiling range in the feed, with both volatile and less-volatile components. The sharp rise towards the end indicates a desired quantity of heavy products like JBO and diesel, emphasizing the need for appropriate cut points in the column design to optimize separation efficiency.\u003c/p\u003e\n\u003cp\u003eFigure 2 was generated directly from the simulation using input data collected from the refinery laboratory. This distillation profile was used to define pseudo-components and highlights the modelling of the feed behavior in the fractionation column within the simulation study.\u003c/p\u003e\n\u003cp\u003eFigure 3 also explains the cut distribution of the simulated feedstock. The distribution was performed based on the liquid volume fraction of the total oil, and it displays how the raw condensate was divided into multiple boiling range fractions (or \u0026ldquo;cuts\u0026rdquo;) through various temperature ranges.\u003c/p\u003e\n\u003cp\u003eThe cut input information on the left side of the figure shows the temperature endpoints for each defined fraction:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eLight naphtha\u003c/strong\u003e up to 80\u0026deg;C\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHeavy naphtha\u003c/strong\u003e up to 180\u0026deg;C\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eKerosene\u003c/strong\u003e up to 240\u0026deg;C\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eJBO (Jute Batching Oil)\u003c/strong\u003e up to 350\u0026deg;C\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHeavy diesel\u003c/strong\u003e up to 380\u0026deg;C\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eResidue\u003c/strong\u003e extending to 1200\u0026deg;C\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOn the right side of Figure 3, a bar chart presents the relative volume distribution of each cut. The horizontal axis represents the boiling point in degrees Celsius, while the vertical axis indicates the liquid volume fraction of the total oil.\u003c/p\u003e\n\u003cp\u003eFrom the bar chart, it is evident that the heavy naphtha cut (green bar) constitutes the largest volume fraction of the total blend. The light naphtha (red), kerosene (blue), and JBO (magenta) follow in decreasing volume contribution. The heavy diesel (cyan) and residue make up smaller portions, indicating that the feedstock contains a certain amount of lighter to middle distillates and a comparatively lower proportion of heavy components.\u003c/p\u003e\n\u003cp\u003eThis graphical distribution shows an understanding of the compositional contents of the feed and is very important for column design and product yield optimization. The data was illustrated after processing the assay input and applying simulation calculations to determine the product yields based on true boiling point (TBP) behavior.\u003c/p\u003e"},{"header":"IV. SIMULATION OF CONDENSATE FRACTIONATION UNIT","content":"\u003cp\u003eThe process flow diagram (PFD) represents the Condensate Fractionation Unit (CFU), primarily focusing on the distillation and side stripper systems used for the separation of petroleum fractions. The central unit in the diagram is labelled as the distillation column (C-101). It is designed to separate natural gas condensate into several refinery products based on their true boiling point (TBP) ranges. The column consists of multiple stages, indicated by the numbered trays ranging from 1 to 37, with the feed entering the column at tray 11. Lighter components rise to the top of the column, while heavier components proceed towards the bottom.\u003c/p\u003e\n\u003cp\u003eAt the top of the distillation column, overhead vapors are condensed in a condenser unit, where the condensed liquid is partially recycled back to the column as reflux to enhance liquid-vapor separation, while the non-condensable gases are discharged as off-gas. Off-gases are discharged through flaring. The condensed overhead liquid product is collected as light naphtha.\u003c/p\u003e\n\u003cp\u003eFrom various intermediate draw stages of the fractionation column, heavy naphtha is collected from tray 3, and side streams are withdrawn for further separation. One such stream is directed to the kerosene stripper column, where lighter portion separation is achieved using reboiling. The bottom product from this stripper is collected as kerosene, and the top is returned to the distillation column as kerosene stripper return. Similarly, another side stream is routed to the JBO (Jute Batching Oil) stripper column, where a similar process occurs. The segregated bottom product is collected as JBO, and a return stream is recycled back to the C-101.\u003c/p\u003e\n\u003cp\u003eBoth side strippers are equipped with reboilers that provide the necessary heat energy to enhance component separation. These reboilers receive a portion of the side draw streams, heat them to provide the required vaporization, and return the vapor to the strippers. This operation is key to improving product purity and ensuring efficient separation.\u003c/p\u003e\n\u003cp\u003eAt the bottom of the fractionation column, the heavier portion of feed is drawn and sent to a reboiler (H-101) to raise the temperature of the bottom product before it is further processed or sent to the storage tank. This stream is identified as diesel, which is one of the main products of the distillation process.\u003c/p\u003e\n\u003cp\u003eSo, this process flow diagram (PFD) illustrates a typical distillation section of the CFU process, where condensate feed is separated into multiple fractions, including light naphtha, heavy naphtha, kerosene, JBO, and diesel, using a combination of main distillation and side stripping columns, supported by condensers and reboilers to maintain heat balance and product purity.\u003c/p\u003e\n\u003cp\u003eThe Heat and Material Balance (HMB) for all process streams within the CFU operation has been illustrated in Fig. 5, as obtained from the simulation results. Overall, this heat and material balance diagram provides a comprehensive quantitative overview of the system\u0026apos;s operating conditions and energy distribution. It is essential in validating process design, identifying thermal efficiencies, and optimizing product recovery in distillation column operations.\u003c/p\u003e\n\u003cp\u003eFigure 6 presents the column specification and performance summary for the distillation column C-101, simulated using Aspen HYSYS with the Peng-Robinson thermodynamic package. Column performance indicators can be reviewed under the \u0026quot;Performance\u0026quot; section within the column section, where graphical outputs such as \u0026quot;Temperature vs. Tray Position from Top\u0026quot; provide insight into the internal temperature distribution across trays.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor tray design within HYSYS, the tray sizing feature is enabled through the utility panel (accessed via Tools \u0026rarr; Utilities \u0026rarr; Tray Sizing). Upon selection of bubble cap trays with a standard vertical spacing of 24 inches (2 feet) and retention of all default configuration parameters, the calculated column diameter was approximately 4.921 feet. Output data concerning tray count, column dimensions, and duties of the reboiler and condenser can be extracted from the simulation results. These parameters are typically integrated into economic evaluation tools, such as cost estimation models or spreadsheets, to facilitate optimization of the total annual production cost.\u003c/p\u003e\n\u003cp\u003eAt the top of the figure, a temperature profile plot is shown as a function of tray position, with tray numbers increasing from top to bottom. This profile reveals a typical ascending temperature gradient from the top of the column (around 60 to 100\u0026deg;C) to the bottom (approximately 340\u0026deg;C), characteristic of atmospheric fractional distillation. Such a profile ensures that lighter components vaporize and condense near the top, while heavier components are separated near the bottom due to their higher boiling points.\u003c/p\u003e\n\u003cp\u003eBelow the plot, the specifications table lists critical design parameters and target setpoints for the simulation. The reflux ratio is set to 6.0, which is a typical value for refinery columns to ensure effective rectification of the overhead product (light naphtha). The distillate rate is set to 4350 kg/h, representing the flowrate of condensed top product withdrawn. Other key parameters include heavy naphtha flowrate (15,600 kg/h), kerosene stripper product flowrate (3000 kg/h), and JBO stripper product flowrate (2200 kg/h).\u003c/p\u003e\n\u003cp\u003eDegrees of freedom show zero error, indicating the model has converged successfully with consistent mass and energy balances. The green bar at the bottom confirms convergence, validating that the simulation meets all design constraints. This setup is representative of a stable and operationally viable refinery distillation process, ensuring accurate prediction of product flows, compositions, and energy requirements essential for process optimization and scale-up.\u003c/p\u003e\n\u003cp\u003eFigure 7 presents the tray-wise stage efficiency settings for the atmospheric distillation column (C-101) in Aspen HYSYS. In the simulated configuration, a total of 37 bubble cap trays were incorporated within the main distillation column to establish vapor\u0026ndash;liquid equilibrium. The tray efficiency was uniformly assigned a value of 0.75 (or 75%) across all stages, with the condenser operating at full efficiency. The design and calculations related to the bubble cap trays were performed in accordance with the methodology outlined in Design of Equilibrium Stage Processes by Bufford D. Smith [8].\u003c/p\u003e\n\u003cp\u003eThermodynamic and flow conditions, including pressure, temperature, as well as vapor and liquid flow rates, were determined individually for each tray. These values are automatically computed by the simulator and used to define the internal column behavior, as shown in the corresponding simulation output.\u003c/p\u003e"},{"header":"V. COLUMN HYDRAULICS","content":"\u003cp\u003eIn atmospheric distillation systems, the phenomenon referred to as \u0026quot;weeping\u0026quot; arises when liquid leaks through the perforations of sieve trays due to inadequate upward vapor flow. Such a condition is considered unfavorable, as the intended path of the liquid is across the tray surface and subsequently through the downcomer. This phenomenon defines the minimum threshold for vapor velocity in sieve-tray column operations. On the opposite end, \u0026quot;flooding\u0026quot; occurs when excessive accumulation of liquid within the column obstructs downward liquid movement, caused by excessively high vapor flow rates. This condition establishes the maximum permissible vapor velocity for the effective functioning of sieve-tray columns [9]. \u0026ldquo;Foaming\u0026rdquo; observed in a distillation column is characterized by the volumetric expansion of liquid, which enhances the interfacial contact between the liquid and vapor phases [10].\u003c/p\u003e\n\u003cp\u003eFigure 8 illustrates a detailed hydraulic analysis of the main distillation column\u0026apos;s internal behavior, focusing on tray 1. It provides insights into the vapor-liquid separation through the column trays and how closely the operating conditions approach mechanical limits, such as flooding, weeping, entrainment, and puking. The operating point on the weir load plot for tray 1 lies within the stable hydraulic envelope. Overall, the \u0026ldquo;Stages view\u0026rdquo; displays a blue shading pattern, indicating that the distillation column, designed with 37 bubble cap trays, is operating under stable conditions.\u003c/p\u003e\n\u003cp\u003eExplanation of Key Hydraulic Phenomena:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eFlooding:\u003c/strong\u003e This is focused by the \u003cstrong\u003e\u0026quot;100% Downcomer Backup\u0026quot;\u003c/strong\u003e line and the \u003cstrong\u003evapor-liquid envelope\u003c/strong\u003e. In the chart, the \u003cstrong\u003eoperating point\u003c/strong\u003e (black dot) is well below the flooding boundary, indicating safe operation.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWeeping:\u003c/strong\u003e Defined by the \u003cstrong\u003e0% weep line\u003c/strong\u003e (lower pink line). The operating point is significantly above this line, confirming that \u003cstrong\u003eweeping is not a concern\u003c/strong\u003e in this stage.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEntrainment:\u003c/strong\u003e Implied by operation near the upper limits of vapor velocity. High vapor velocities can carry liquid droplets upward, reducing tray efficiency. Figure 8 indicates proximity to flooding where entrainment typically begins. The \u003cstrong\u003ehydraulic envelope\u0026apos;s upper edge\u003c/strong\u003e often correlates with entrainment risk. This simulation suggests \u003cstrong\u003eentrainment could occur if the vapor load increases further\u003c/strong\u003e.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePuking:\u003c/strong\u003e A severe form of flooding where frothy liquid and vapor are violently pushed up through the column. The \u003cstrong\u003erisk of puking increases when operating near or above 100% flood\u003c/strong\u003e, especially with foaming or highly turbulent conditions. The chart implies that \u003cstrong\u003eStage 1 is on the edge\u003c/strong\u003e, and any operational upsets could initiate puking.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"VI. OUTPUT OF THE SIMULATION","content":"\u003cp\u003eThe simulation process in HYSYS is executed using a concurrent modular methodology. A flowsheet model is constructed as an assembly of sub-flowsheets (SFS or modular blocks), which are interconnected via process streams. Each sub-flowsheet comprises a set of unit operations and associated streams that are suitable for simultaneous resolution [11]. Throughout the simulation process, each sub-flowsheet is resolved utilizing one of the standard solvers available in HYSYS, specifically the sequential quadratic programming (SQP) optimizer [12]. The interaction between the simulator and the flowsheet is maintained until the specified convergence criteria are satisfied [13].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 9 presents the streamwise tabulated output of a simulated condensate fractionation unit. The table summarizes thermodynamic and flow parameters of various process streams, including reflux, return and draw streams from light naphtha, kerosene and JBO (Jute Batching Oil) strippers, feed and product outlets, and streams directed to heaters and condensers. The temperature of the stream ranges from 50\u0026deg;C at the condenser to 328.3\u0026deg;C at the bottom of the distillation column, reflecting the continuous heating and fractionation of the natural gas condensate. The pressure of the condensate feed is relatively stable, ranging almost 3 bar. The highest values are seen in the heater and diesel streams at 1.05 bar, supporting consistent vapor-liquid equilibrium conditions in the column.\u003c/p\u003e\n\u003cp\u003eThe condensate feed has a mass flow of 27100 kg/h, indicating a moderate feed throughput. The Kerosene Stripper Draw stream exits the column at 167.2\u0026deg;C and 0.7833 bar, with a mass flow of 6411 kg/h, typical for kerosene product recovery. The JBO Draw stream exits at 265.9\u0026deg;C and 1.032 bar, with a mass flow of 15,650 kg/h, making it a major middle distillate product in this case. All streams shown are in liquid phase.\u003c/p\u003e\n\u003cp\u003eFigure 10 represents the final simulation output for the main distillation column, summarizing key process stream data across all primary product and intermediate draws. It displays thermodynamic and flow parameters for streams, including the condensate feed, off-gas, light and heavy naphtha, diesel, kerosene, and JBO products.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe condensate feed enters at 80\u0026deg;C and 3.0 bar with a mass flow of 27,100 kg/h. The off-gas stream is fully vaporized at 50\u0026deg;C and 0.73 bar, indicating the separation of lighter components at the top of the column. Light and heavy naphtha exit the column at progressively higher temperatures, confirming their separation based on volatility. The diesel stream emerges at 328.3\u0026deg;C and 1.05 bar with a mass flow of 1950 kg/h, showing characteristics of a bottom product. Kerosene and JBO, separated at 201.6\u0026deg;C and 299\u0026deg;C, respectively, indicate efficient recovery of these boiling components.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe thermodynamic properties such as molar enthalpy, entropy, and heat flow vary significantly with stream composition and temperature, showing consistent trends across the boiling point range. These final values demonstrate a reliable simulation of the atmospheric distillation process, confirming appropriate separation, product purity, and energy distribution within the column. The convergence status at the bottom confirms that the simulation ran successfully without errors, providing reliable data for analysis and optimization of the distillation unit.\u0026nbsp;\u003c/p\u003e"},{"header":"VII. RESULT AND DISCUSSION","content":"\u003cp\u003e\u003cstrong\u003eTable 2:\u0026nbsp;\u003c/strong\u003eComparison between Simulation Data and Real Plant Data\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50.641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eParameters\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3397%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eUnits\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eSimulation Data\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003ePlant Data\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50.641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eColumn Top Pressure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3397%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBar\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.73\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.73\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50.641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eColumn top temperature\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3397%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026deg;C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e93.27\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e88.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50.641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eColumn Bottom Pressure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3397%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBar\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50.641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eColumn bottom temperature\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3397%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026deg;C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e328.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e330\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50.641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFeed flowrate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3397%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKg/hr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e27100\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e27100\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50.641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFeed preheating temperature\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3397%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026deg;C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e80\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e77\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50.641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeavy naphtha draw temperature\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3397%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026deg;C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e128.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e126\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50.641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKerosene product draw temperature\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3397%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026deg;C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e201.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e227\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50.641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJBO product draw temperature\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3397%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026deg;C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e299\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e309\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50.641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReboling flowrate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3397%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKg/hr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e109000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e109000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50.641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLight naphtha flowrate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3397%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKg/hr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4350\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4300\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50.641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeavy naphtha flowrate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3397%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKg/hr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15600\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15900\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50.641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKerosene flowrate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3397%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKg/hr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4300\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50.641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJBO flowrate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3397%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKg/hr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2200\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1600\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50.641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiesel flowrate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3397%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKg/hr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1950\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50.641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTemperature at Heater (H-101) to Column flow\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.3397%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026deg;C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e341.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0641%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e357\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2 provides a comprehensive comparison between simulation results and actual plant data, highlighting the accuracy and reliability of the developed model in representing the real operation of the distillation column. The outcomes derived from the process simulations, illustrated in Figures 5, 6, 9 and 10, were systematically compared against actual operational data, as shown in the accompanying table. A detailed comparison of the key operational parameters from the plant with those obtained through simulation revealed close alignment in many aspects. Notably, the volumetric flow rates and associated temperatures for light naphtha, heavy naphtha, kerosene, JBO, and diesel products were estimated through simulation as 4350, 15600, 3000, 2200, and 1950 in kg/h (unit), respectively. However, actual plant measurements indicated values of 4300, 15900, 4300, 1600, and 1000 in kg/h (unit), respectively.\u003c/p\u003e\n\u003cp\u003eThe temperature recorded in the plant data at both the top and bottom of the column compared to the simulated values was nearly similar, while a temperature drop was observed in the kerosene product draw. Pressure measurements at both the top and bottom of the column remained consistent across simulation and plant conditions. Furthermore, the temperature of the stream from the heater (H-101) to the column is 341.1\u0026deg;C in the simulation, while the actual plant operates at 357\u0026deg;C. This variation could be attributed to differences in heat exchanger efficiency or bypass effects in the plant setup not modelled in the simulation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, the comparison demonstrates a close correlation between the simulation and plant data, with most parameters aligning closely, thereby affirming the model\u0026apos;s feasibility for operational analysis and optimization purposes.\u003c/p\u003e"},{"header":"VIII. CONCLUSIONS","content":"\u003cp\u003eThe conducted research successfully demonstrates the capability of process simulation to replicate real-world operational data of a distillation column with commendable accuracy. This study focused on the simulation of a condensate fractionation unit (CFU), where a comprehensive Process Flow Diagram (PFD) was developed, including detailed heat and mass balance calculations. Specifically, simulated and measured flow rates (in kg/h) for light naphtha, heavy naphtha, kerosene, JBO, and diesel were 4350 vs. 4300, 15600 vs. 15900, 3000 vs. 4300, 2200 vs. 1600, and 1950 vs. 1000, respectively. The close alignment between simulation results and actual plant measurements across key parameters\u0026mdash;including pressure, temperature, flowrates, and product draws\u0026mdash;confirms the robustness of the developed model and its applicability for performance evaluation and operational planning. Although the simulations were performed under steady-state conditions, the approach holds the potential for expansion into dynamic modelling. This transition could support advanced applications, including operator training simulators, transient process behavior analysis, and real-time optimization. The model proves to be a feasible predictive tool, enabling insight into column behavior under various operating conditions. It also offers a fundamental foundation for further studies: control strategy development and energy efficiency assessments. The overall findings reinforce the value of simulation in enhancing process understanding, supporting decision-making, and ensuring improved alignment between design assumptions and plant realities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.R.R.T. wrote the main manuscript text, M.M. prepared the figures, A.I.S. provided validation and J.U.A. supervised the whole research. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJ. G. SPEIGHT, Handbook of Petroleum Refining, New York: CRC Press: Taylor \u0026amp; Francis, 2017, p. 143.\u003c/li\u003e\n\u003cli\u003eA. N. Khalaf, \u0026quot;Steady State Simulation of Basrah Crude Oil Refinery Distillation Unit,\u0026quot; \u003cem\u003eThi_Qar University Journal for Engineering Sciences, \u003c/em\u003ep. 29, 2018. \u003c/li\u003e\n\u003cli\u003eQ. A. H. S. K. T. H. a. D. M. S. Mohammad Hasibul Hasan, \u0026quot;Simulation of Crude Distillation Unit of Eastern Refinery Limited (ERL) using ASPEN PLUS,\u0026quot; in \u003cem\u003eInternational Conference on Materials, Electronics \u0026amp; Information Engineering, ICMEIE-2015\u003c/em\u003e. \u003c/li\u003e\n\u003cli\u003eT. A. A. A. MOHAMED A. FAHIM, Fundamentals of Petroleum Refining, Elsevier, 2010. \u003c/li\u003e\n\u003cli\u003eL. A. B. J. M. L. A. R. E. E. Juan Pablo Gutierrez, \u0026quot;Thermodynamic Properties for the Simulation of Crude Oil Primary Refining,\u0026quot; \u003cem\u003eJuan Pablo Gutierrez et al. Int. Journal of Engineering Research and Applications, \u003c/em\u003ep. 192, 2014. \u003c/li\u003e\n\u003cli\u003eA. Ashraf, \u0026quot;Distillation process of Crude oil,\u0026quot; \u003cem\u003eResearch Gate, \u003c/em\u003e2012. \u003c/li\u003e\n\u003cli\u003eS. C. D.K. Varvarezos, \u0026quot;Crude Evaluation for Refinery Planning Using a Molecular-Based Assay Characterization,\u0026quot; in \u003cem\u003e13 AIChE Annual Meeting\u003c/em\u003e, 2013. \u003c/li\u003e\n\u003cli\u003eB. D. Smith, Design of equilibrium stage processes, New York: McGraw-Hill, 1963. \u003c/li\u003e\n\u003cli\u003eM. A. Minhas, \u0026quot;Entrainment Flooding and Weeping Velocities,\u0026quot; \u003cem\u003eChemical Engineering, \u003c/em\u003ep. 24, January 2025. \u003c/li\u003e\n\u003cli\u003eD. W. S. W.M.H.E. WIJESINGHE, \u0026quot;Design of o-xylene Plate/Tray Distillation Column,\u0026quot; University of Moratuwa, Moratuwa, 2016.\u003c/li\u003e\n\u003cli\u003eM.J.BOX, \u0026quot;A New Method of Constrained Optimization and a Comparison With Other Methods,\u0026quot; \u003cem\u003eThe Computer Journal, \u003c/em\u003evol. 8, no. 1, pp. 42-52, 1965. \u003c/li\u003e\n\u003cli\u003eJ. M. J.L. Kuesterand, Optimization Techniques with FORTRAN, New York: McGraw-Hill Book Co., 1973, pp. 35-108.\u003c/li\u003e\n\u003cli\u003eA. D. F. A. M. Mohammed Jibril, \u0026quot;Simulation of Kaduna Refining and Petrochemical Company (KRPC) Crude Distillation Unit (CDU I) Using HYSYS,\u0026quot; \u003cem\u003eInternational Journal of Advanced Scintific Research and Technology, \u003c/em\u003evol. 1, no. 2, pp. 1-6, 2012. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Aspen HYSYS, CFU, Petroleum Refinery, Simulation","lastPublishedDoi":"10.21203/rs.3.rs-6495903/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6495903/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe design of distillation columns presents significant challenges due to the complexity of various refinery configurations, such as tray architecture, and operational issues including flooding and weeping. This study aims to investigate and simulate critical operational parameters of a distillation column utilizing the Aspen HYSYS process simulation software. A steady-state model of the Condensate Fractionation Unit (CFU) was developed and analyzed using the Peng-Robinson thermodynamic equation of state in conjunction with a stage-by-stage calculation method. The feedstock utilized in this simulation was a secondary condensate stream derived from natural gas processing, consisting of hydrocarbons ranging from propane (C₃) to octadecane (C₁₈). Notably, the volumetric flow rates for light naphtha, heavy naphtha, kerosene, JBO, and diesel products were estimated through simulation as 4350, 15600, 3000, 2200, and 1950 in kg/h (unit), respectively. However, actual plant measurements indicated values of 4300, 15900, 4300, 1600, and 1000 in kg/h (unit), respectively. These findings highlight the effectiveness of simulation tools in improving process insight, facilitating engineering decisions, and enhancing the congruence between conceptual design and operational performance.\u003c/p\u003e","manuscriptTitle":"A Feasibility Study of Condensate Fractionation Unit (CFU) Process Using Aspen HYSYS Simulation Software in the Petroleum Refinery Industry of Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-29 04:29:35","doi":"10.21203/rs.3.rs-6495903/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2e3da569-5fc9-4608-84df-9988ca75f533","owner":[],"postedDate":"April 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-11T13:53:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-29 04:29:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6495903","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6495903","identity":"rs-6495903","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}