Techno-Economical Assessment of Kappaphycus alvarezii Carrageenan Extraction Plant

Techno-Economical Assessment of Kappaphycus alvarezii Carrageenan Extraction Plant | 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 Techno-Economical Assessment of Kappaphycus alvarezii Carrageenan Extraction Plant Felix Subakti, Ryozo Noguchi, Andarini Diharmi, Juro Miyasaka, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5405906/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Kappaphycus alvarezii carrageenan extraction was optimized using a software-based process simulation. The study focused on the Indonesian seaweed industry, utilizing the advanced modeling capabilities of “SuperPro™”. The simulation involved comprehensive analysis of the extraction process, from dried seaweed transport to final carrageenan production. Furthermore, an economic sensitivity analysis was conducted, incorporating the seaweed production cost as a critical parameter. This analysis provides valuable insights into the financial viability of Kappaphycus alvarezii carrageenan extraction by considering the variations in the input costs, market prices, and other economic factors. The data show that a factory producing Kappaphycus alvarezii carrageenan is both technically and economically feasible within a 10-year lifespan. The plant processes 11.5 MT of raw Kappaphycus seaweed per batch, amounting to an annual input of 44.045 MT raw materials, and an annual output of 3.14 MTs of carrageenan, totaling the processing of 13,074 MTs of Kappaphycus alvarezii carrageenan annually with a payback period of around 8 year and a return on investment of 11.33%. The results provide information for stakeholders, including seaweed farmers, processors, and policymakers, about the potential financial benefits and challenges associated with scaling-up Kappaphycus alvarezii carrageenan extraction in Indonesia. Biorefinery Carrageenan Process Simulations Process optimizations Kappaphycus alvarezii Waste Treatment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 INTRODUCTION Indonesia, a prominent archipelagic nation, has successfully cultivated diverse seaweed varieties (Sultana et al., 2023), securing its position as the world’s second-largest producer of annual fresh seaweed tonnage, trailing behind China (World Bank, 2023). The harvested seaweeds are primarily exported in their raw state to more developed nations, with limited domestic consumption, primarily within the pharmaceutical sector. The processing of seaweeds to enhance their pharmaceutical value has focused on the extraction of fine chemicals, driven by extensive research into the medicinal and nutritional benefits of seaweeds. One such chemical that has received extensive scrutiny and has been extracted for pharmaceutical applications is Kappaphycus alvarezii carrageenan. The substance is characterized as a linear sulfated polysaccharide. Carrageenan is both flavorless and colorless (Guo et al., 2022). Most types of carrageenans exhibit gel-forming properties at different temperatures, except for λ-carrageenan, which maintains a predominantly rigid structure (Running et al., 2012). Carrageenan is commercially extracted from red algae of varying molecular masses, and is used in capsules, tablet coagulants, and dairy emulsifiers (Bongaerts et al., 1999). Indonesia has several red seaweeds that yield carrageenan, which are currently being mass-cultivated across numerous significant islands. Kappaphycus alvarezii , formerly known as Euchema cottonii , stands out among these species (Guo et al., 2022). Despite not being indigenous to the Indonesian archipelago (Ask & Batibasaga, Aisake & Zertuche, Jose & San, 2003), Kappaphycus alvarezii has successfully adapted to local Indonesian coasts, becoming an invasive species. The primary yield of this seaweed is κ-carrageenan, which justifies its reclassification. Thriving in coastal areas of warmer oceans, Kappaphycus alvarezii can be effectively cultivated using rope anchoring (Rama et al., 2018). To initiate the industrial-scale production of carrageenan from Kappaphycus alvarezii , establishing a pilot-scale production plant is imperative. Considering the inevitable cost-implications, establishing a production plant requires comprehensive calculations. This underscores the significance of employing factory simulation software, which facilitates multiple calculations and minimizes potential errors that may contribute to escalating capital costs (Casavant & Côté, 2004). This study aims to conceptualize a carrageenan extraction plant for treating Kappaphycus alvarezii , with the simulation yielding a feasible scheme from both the engineering and economic perspectives. Envisaging industrial-scale production of red-seaweed carrageenan, this initiative is expected to add value to fresh seaweed cultivated in Indonesia, thereby contributing to the advancement of the blue industrial revolution through the novel exploration of marine resources. 2 METHODOLOGY The simulation was preformed using SuperPro™ as a Software Process Designer (SPD). The tools were obtained from parts of the software library and sized accordingly. 2.1 SuperPro™ Simulation Software SuperPro™, a commercially distributed software from Intelligen Inc., is a bioprocess-oriented production simulation software. This software contains multiple simulated chemical process units and additional supply chain-oriented units. The software version utilized in this assessment is equipped with SchedulePro™, which is available under the SuperPro™ suite, and is integrated within the system. SchedulePro™ is a scheduling software that identifies bottlenecks and provides solutions or overlaps batches to maximize the time efficiency. SuperPro™, in general, works passively off the user’s input within the process and the material parameters. The materials in SuperPro™ are mostly provided through the built-in library; however, if required in a specific case, users can input their own materials through the ‘substance and mixtures’ menu. The software can perform a general economic assessment, technical assessment, and provide a Gantt chart that visually represents the batch schedule in a production plant. SuperPro™ can also be integrated with Microsoft Excel to generate “. xls” reports that have broader appeal for investors and engineers alike. The economic aspects of SuperPro™ are also passively influenced by manual input of the material costs, unit costs, labor, consumables, and utilities. Debt and repayment can also be managed through the investment ratio, taxes, and factory lifetime, which users can input into the software. Revenue, waste, and emissions can be classified per stream rather than per material. 2.2 Mode of Operation The plant simulation was conducted within the framework of a batch system characterized by an annual operational time of 7920 h (Ahmed et al., 2022). Ahmed et al. suggested that 330 d was sufficient for a lean manufacturing simulation. This intentional selection of the operating time was also predicated based on the need to accommodate the intermittent downtimes necessitated by system upgrades or routine equipment maintenance. 2.3 Definition of Components in the Simulation Table 1 lists the components used in the simulations. The components were collected in the SuperPro™ built-in database; in this case, several additional components were inputted into the library due to default collection limitations. Table 1 Components and Definition of the Simulation Components Definition Details Carrageenan Defined by the user Molar mass of 193 kg/mol Seaweed Mixture of Proteins Fats, Ash, Cellulose, Biomass and Carrageenan Cellulose SuperPro™ database, chemical formula Ash SuperPro™ database Molar mass of 200 g/mol Water SuperPro™ database Molar mass of 18.02 g/mol Air SuperPro™ database 76.72% Nitrogen, 23.29% Oxygen Potassium Chloride SuperPro™ database Molar mass of 74.54 g/mol Potassium Oxide SuperPro™ database Molar mass of 56.11 g/mol Proteins SuperPro™ database Defined as CH 1.8 O 0.5 N 0.2 , Molar mass of 147.60 g/mol Fat SuperPro™ database Defined as CH 1.8 O 0.5 N 0.2 , Molar mass of 147.60 g/mol Carbohydrate SuperPro™ database C 6 H 12 O 6 Biomass SuperPro™ database Defined as CH 1.8 O 0.5 N 0.2 2.4 Chemical Modelling of Simulated Seaweed The input material for this factory, namely. Kappaphycus seaweed, has multiple compositions, which were programmed by customizing the SuperPro™ library and are summarized in Table 2 . The baseline ratios were obtained through a literature review and wet-chemical information from the University of Riau, and were rounded to two decimal places (Diharmi et al., 2020). Table 2 Carrageenan Stock Mixture Modelling (Source, Diharmi, et.al, 2020) Ingredient % Ash 12.35 Proteins 3.67 Fats 0.26 Carrageenan 30.77 Biomass 3.84 Water 49.20 Total 100.00 2.5 Carrageenan Modelling Carrageenan as a substrate does not possess any fixed properties; the researcher utilized information provided by the University of Riau to define the baseline properties for Carrageenan modeling. The molar mass and ash content were particularly variable; thus, a number from the literature review was used (Younes et al., 2018). 2.6 Unit Processes Involved in the Plant The unit processes performed herein followed the unit library provided by the SuperPro™ simulator software. Labeling followed the default automated unit labeling system built into the software; the final product may differ in labeling, but the overall functions typically remain the same. The SuperPro™ library was deemed sufficient for unit process simulations in terms of technical feasibility, whereas the scheduling and economic parameters had to be modified according to more modern capabilities. The details of the unit processes are listed in Table 3 . Table 3 Classification of Units Utilized in the Production Plant Unit type Unit Label Function in the process Transport truck P-10 Transport of raw feed to the factory site Cold water washer 1 WSH-101 Partial removal of ash Extractor MSX-101 Removes carrageenan from raw feed with strong acid Cold water washer 2 WSH-102 Partially washes the acid from carrageenan slurry Batch vessel 1 V-101 Sedimentation site Plate and frame filter 1 PFF-101 Filters out sludge from water Batch vessel 2 V-102 Neutralization using a strong base Tray drying 1 TDR-101 Dries the Carrageenan Grinder GR-101 Produces powder form of Carrageenan Plate and frame Filter 2 PFF-102 Removed by-product from water Tray drying 2 TDR-102 Dries the by-product 2.7 Equipment Sizing User-inputted, design-calculated sizing was applied to the equipment operating within the factory, instead of a more customized throughput-calculated sizing, because of the lack of available information regarding the prices of customized chemical process equipment. Table 4 shows a brief breakdown of the units required in the simulation, construction materials, and simulated capacity. The simulation did not use staggered or standby units. Table 4 Unit Processes Utilized in the Plant Name Type No. of Units Size (Capacity) Material of Construction WSH-101 Washer (Bulk Flow) 6 4,000 kg/h CS MSX-101 Mixer-Settler Extractor 2 3,509 L/h SS316 WSH-102 Washer (Bulk Flow) 4 3,886 kg/h CS V-101 Blending Tank 1 27,870 L SS316 PFF-101 Plate & Frame Filter 1 402 m 2 SS316 V-102 Blending Tank 1 6,256 L SS316 TDR-101 Tray Dryer 1 236 m 2 SS316 GR-101 Grinder 1 3,413 kg/h CS PFF-102 Plate & Frame Filter 1 88 m 2 SS316 TDR-102 Tray Dryer 1 322 m 2 SS316 2.8 Power Demands of the Factory Dominated by the electricity consumed by the unit processes established within the factory, the power demands were modeled in watts owing to the electrically driven nature of most processes. Note that the superheated steam was primarily modeled as an economic factor, and power costs were not considered. Most of the process-unit equipment were purchased from a third party and assembled onsite. Table 5 presents a summary of the equipment required in the factory, with the baseline power demand and a reference for the power demand. Table 5 Power Demands Per Unit of Process Unit label Power Demand Reference WSH-101 13.5 kWh Henan Ocean Machinery Equipment Co., Ltd., 2024. MSX-101 15.0 kWh Wenzhou Chinz Machinery Co., Ltd., 2024 WSH-102 13.5 kWh Henan Ocean Machinery Equipment Co., Ltd., 2024 V-101 11.0 kWh Henan Xingyang Mining Machinery Manufactory, 2024. PFF-101 7.50 kWh Woking Environmental Technology Co., Ltd., 2024. V-102 4.00 kWh Weishu Machinery Technology (Shanghai) Co., Ltd., 2024. TDR-101 35.0 kWh Shandong Qike Machinery Equipment Co., Ltd., 2024. GR-101 15.0 kWh Harun et al., 2019. PFF-102 7.50 kWh Woking Environmental Technology Co., Ltd., 2024. TDR-102 35.0 kWh Shandong Qike Machinery Equipment Co., Ltd., 2024). 2.9 Process Parameters Each discrete module within the simulation framework is tailored to align with the specific throughput and compositional characteristics of each sequential phase within the batch input stream (Arias et al., 2022). The implemented software facilitates automated labeling of individual units by incorporating inherent numerical identifiers. Temperature and pressure emerged as the predominant variables that were uniformly employed across all modules (Araque et al., 2020). Notably, the operation environment in the factory did not entail a vacuum, and for simulation purposes, atmospheric air was modeled as a composite mixture within the simulation library. Details of these processes are listed in Table 6 . Table 6 Process Parameters and Conditions Operations Unit label Temp. ( o C) Pressure Conditions Truck P-10 n/a n/a Assumed to be a singular unit Seaweed washing WSH-101 25 Atmospheric 75% ash removal Main extraction MSX-101 80 Atmospheric Strong base extraction using KOH Carrageenan washing WSH-102 25 Atmospheric Ash and KOH removal from the substrate Sedimentation V-101 25 Atmospheric Added water Carrageenan filtration PFF-101 25 Atmospheric Assumed LOD of 10% and 8 cm cake thickness Neutralization V-102 37 Atmospheric Strong acid KCl to counter the strong base Carrageenan drying TDR-101 70 Atmospheric Water removal Product grinding GR-101 25 Atmospheric Assumed no dissipation to heat By product filtration PFF-102 25 Atmospheric Assumed LOD 30% and 8 cm Cake thickness By product drying TDR-102 70 Atmospheric Water removal 2.10 Contents of the Flow The constituent elements of plant apparatus encompass the conveyance of raw materials and intermediate substances in diverse forms. Nevertheless, owing to the influence of the environmental parameters inherent to operational processes and discernible effects on the physical attributes of specific components, coupled with considerations of equipment sizing and constraints, a degree of anticipated incongruity in the volumetric content of the flow became evident. The expectations regarding what should be found in a particular flow for the entire simulation are listed in Table 7 . Table 7 Process Flow Contents and Their Functions Flow label Expected contents Flow purpose Supplier’s stash Raw Seaweed Starts the whole process Fresh chopped seaweed Raw seaweed Main Process flow, feed for the washer First ash removal Ash and water Waste classification Wash water 1 Water Washer input flow KOH Mix Potassium hydroxide and water Additional Process flow, Extraction Solvent Washed seaweed Seaweed components and water with reduced ash Main process flow, feed for the extractor Seaweed sludge Ash, Biomass, Carrageenan, Proteins, Water, KOH Recently extracted seaweeds. Wash Water 2 Water Second ash removal Ash and water Waste classification Washed sludge Ash, Biomass, Carrageenan, Proteins, Water Feed for sedimentation tank KCl Mix Potassium Chloride and water Additional Process flow: Neutralize the basic sludge S-114 Air and Biomass Sediments Carrageenan, Proteins, water Filter Cake 1 Proteins and Water Waste product of filtration Filtered product Carrageenan, proteins, water KCl Input KCl-water mix Neutralizing solution S-117 Air and water Neutralizing vessel exhaust Wash water 3 Water Aiding in sedimentation Raw Carrageenan Solid Carrageenan, water, and proteins Carrageenan sludge, feed for the tray drying Crude Carrageenan Solid Carrageenan and protein Carrageenan powder Solid Carrageenan and protein Final product for the market, the main revenue flow Filter cake 2 Ash, Biomass, Fats, KOH Sediments, Proteins, and water Waste product of filtration S-124 Biomass, Carrageenan, Fats, KOH, Proteins, water Filtered product feed for by-product tray drying. S-126 water Removed water vapor Dried By-Product Biomass, fats, protein, carbohydrates, Cellulose By-product, secondary revenue flow 2.11 Plant Consumables Plant consumables encompass a category of material inputs that influence processes by modulating the environmental parameters, distinct from active involvement in operational processes. This classification arises from the capacity of the plant consumables to directly influence the operational costs, warranting amalgamation with such costs during a rigorous economic assessment. The consumables for the plant are recorded in Table 8 with suitable quantitative units, preferably SI units, along with the respective functions. Table 8 Consumables Required by the Plant Consumables Main dimension Unit of measurement Function Source Superheated steam weight MTs Heat exchanger agent SuperPro™ library Cooling water weight m 3 Heat exchanger agent SuperPro™ Library Electricity Energy KW/h Unit process consumption SuperPro™ Library 3 RESULTS 3.1 Factory Schematics The plant generates two revenue flows and eight waste flows. The revenue flows were split into carrageenan powder as the primary revenue flow and a mixture of waste products that could be used as premium fertilizer as the secondary revenue flow. The wastes comprised ash, fats, used acids, and base- and tray-dried exhaust waste outputs, with ash being deemed the most troublesome because of its low potential value. Figure 1 provides an overview of the process simulation as generated by SuperPro™ 10.3. The simulation contained a washer, grinder, reactor, three filtration units, centrifuge, three distillation towers, four heat exchanger units, and one freeze-drying unit. The flowchart presented in Fig. 2 summarizes the process and its demands. The flowchart includes details of the power demands and step-by-step breakdown of the process flow. 3.2 Time Constraints of the Factory 3.2.1 Plant Operational Scheduling The deviation from the conventional calendrical standards of 365 or 360 days within an operational timeframe of 7,920 h per annum was implemented as a deliberate measure to accurately capture and account for instances of maintenance and upgrade-related downtime. This approach acknowledges the inherent complexities in large-scale production lines, wherein interruptions to regular operations necessitate more comprehensive accounting for time to achieve a deeper understanding of the system's functioning. Table 9 lists the recipe cycle times between batches. The factory operated a batch process model instead of a continuous process (Goršek & Glavič, 1997). Table 9 Factory's Batch Scheduling Scheduling item Annual operating time 7,918.43 h Recipe batch time 5.17 h Recipe cycle time 2.07 h Number of batches per year 3,830 cycles 3.2.2 Plant Batch Scheduling Batch operation of a plant involves the orchestration of multiple interdependent processes within the operational framework. Notably, these processes are sequentially linked, wherein the output of a given process serves as the input for the successive processes. Additionally, specific processes operate concurrently, imparting a semi-continuous character to the overall processing regimen as the output seamlessly transitions into subsequent processing units (Tan et al., 2019). Most processes delineated in the Gantt chart were configured within the parameters stipulated by the software, as illustrated in Fig. 3 . Notably, several processes were assigned a time-span of 1 h to align with the conventional duration of a standard 8 h workday. Accordingly, the graphical representation in Fig. 3 is demarcated into 8 h cycles. As a departure from the default flow or process set, which adhered to throughput-based computation, the pull-in and transfer-in processes within the factory were deliberately set to durations of less than 1 h. This adjustment was aimed at aligning with the principal process while accounting for scaling considerations and enhancing the overall representational fidelity of the simulation. After considering the manner in which a single batch can be executed in a factory, multiple batches must be considered in parallel. In this case, a new batch can be started when the current batch is halfway to completion. Figure 4 shows how such overlapping processes can be conducted within the same plant. This arrangement allows more batches to be run within 24 h, thus increasing profitability and productivity (Joseph & B, 2021). The material balance report can be broken down into a summary and stream details. The summary, as presented in Table 10 , was split into three columns, and consisted of the materials, annual demands, and batch demands. Table 10 Batch and Annual Material Consumptions for the Factory Material MT/yr MT/batch KClMix 7,660 2.0 KOH mix 15,320 4.0 Seaweed 44,045 11.5 Water 103,410 27.0 Total 170,435 44.5 The water input was derived passively through a composite methodology involving manual adjustment of the per-unit input, as well as the computed demand of specific unit processes beyond the researcher's immediate purview. The main aim of the factory simulation was to process the seaweed supply using the amount of seaweed that was first determined by the researcher. Potassium hydroxide, potassium chloride, and water were used to fulfill the processing demands of raw seaweed input. This calculation was extended to encompass nuanced process demands, including those of a more esoteric nature, such as the requirement for a thermal jacket with fluid convection. 3.3 Revenue and Cost Classifications The revenue flow was split into two major components: the main stream of revenue consisted mostly of Kappaphycus alvarezii carrageenan, and the by-product comprised multiple biodegradable components. The Capital costs are split into various elements, such as the establishment price, labor costs, and unit purchases (Al-Sharrah & Marafi, 2023). The pie chart in Fig. 5 shows that the raw materials are the primary contributors to the operating cost. R&D will be conducted chiefly through university collaborations with external funding; thus, the costs in this chart will be lowered by default. Waste disposal has a low contribution to the cost, consisting solely of the costs of disposing ash and biomass. The energy costs of waste disposal are divided into utility and unit costs with depreciation. Land-based truck transportation is the main mode of transportation. 3.4 Materials and Energy The generated income comprised Kappaphycus alvarezii carrageenan output as the main product and the secondary biomass-dominated by-product. The calculated yield for these substances was heavily influenced by the composition of the fresh seaweed, which is the primary feedstock of this factory, assuming that the composition would not change in the long run. The factory produces approximately 3,143 kg of carrageenan powder from 11,000 kg of fresh seaweed. The main contributor to the energy consumption is the electricity required to power process units within the factory. Some additional energy consumption in the model is expected in the transport phase using long-haul trucks that run primarily on diesel fuel. However, the software opted to treat the input fuel consumption directly as an economic burden instead of passively as fuel consumption. This factory has a built-in waste treatment process for separating low-value ash waste from biomass and biodegradable waste with potential economic value. Although built-in waste treatment processes within factories can contribute to a more independently run factory because the presence of any third-party waste treatment service is rendered unnecessary, the upfront capital required to establish this process is much higher and may be a prelude to higher debt or required investments. The factory had two plate and frame filter units. Although this filter is commonplace and deemed necessary by the author, the carrageenan found in the simulated results of the first filtration should be neglected. Carrageenan is the main product; thus, neglecting carrageenan within the by-product load may reduce both the yield and profitability. 3.5 Assessment of the Predicted Final Product The final marketable product of this factory is solid carrageenan in powdered form. The final product contained less than 0.1% impurities, which complies with the FAO standards for carrageenan purity. The calculation showed that the ash was completely removed through multiple separation processes in the plant simulation. Ash within the simulation was considered as a monolith instead of the commonplace dual classification of acid-soluble and acid-insoluble wastes. A minor hurdle in harnessing the final product is the loss of carrageenan due to bulk filtering. Bulk filtering was deemed an appropriate choice for economic reasons; however, from the technical perspective, the carrageenan lost by filtering can cause a loss of potential revenue. 3.6 Economic Breakdown The intent is for the factory to process and transport freshly harvested seaweed instead of establishing an in-house cultivation unit or seaweed farm. This significantly increases the cost required to run the factory, with raw material procurement dominating the cost. The utilities mainly consist of the electricity needed to run the unit processes, whereas ash removal increases the waste-treatment cost. The factory payment system was evaluated through a meticulous cash flow analysis with a protracted 20-year payment horizon. Notably, in typical scenarios, a factory commences operations with an initial phase of nonprofitability, gradually transitioning to capital returns after a minimum of 10 year. The economic framework of the analysis predicts a flat tax rate of 6%. The factory profitability was measured by examining two crucial financial metrics: the internal rate of return (IRR) and net present value (NPV). The IRR represents the discount rate at which a project's NPV attains equilibrium, reaching zero (Zenkovich et al., 2021). Concurrently, the NPV is defined as the sum of all forthcoming cash flows, encompassing both positive and negative flows, throughout the lifespan of an investment and is meticulously discounted to the present value (Naim et al., 2007). This analytical framework ensures a rigorous and comprehensive assessment of the factory payment system, thereby enhancing the investigation's scholarly rigor and depth. The costs of energy and water in the plant were modeled after the national electricity and water prices set by the Indonesian Government, which means that the costs were somewhat subsidized. The classification of this plant fell under ‘Industri skala besar’ (large-scale industries), which warrants a reduced purchase price for both water and energy. Table 11 shows the potential payback time and gross margins of the factory, as generated by SuperPro™ software. Table 11 Basic Economic Breakdown of the Factory Total Capital Investment 1,914,000 $ Capital Investment Charged to This Project 1,914,000 $ Operating Cost 6,950,000 $ /year Revenues 7,109,000 $ /year Gross Margin 2.23 % Return on Investment (ROI) 11.35 % Payback Time 8.81 years IRR (After Taxes) 7.11 % NPV (at 7.0% Interest) 19,000 $ 3.7 Sensitivity Analysis Figure 6 presents a sensitivity analysis for determining which of the many market factors has the strongest impact on the factory’s profitability (represented by the NPV) in the long run. The authors considered three significant factors: the price of the sold product, price of seaweed as factory feed, and cost of labor (Fig. 6 ). The factory was most strongly affected by fluctuations in the carrageenan market selling price, with labor costs affecting it the least. The three graphs meet at the equilibrium point close to 0%, where the prices of these components will cause the factory to barely break-even, even when covering the operations. 4 Conclusions The analysis shows that the upscaling of a Kappaphycus alvarezii carrageenan extraction factory is technically and economically feasible within a 10-year payback period, assuming the products can find considerable demand on the market. Certain considerations must be made regarding the waste treatment and secondary product manufacturing. In the present study, there were no clear prospects for secondary products that could properly supplement the income provided by Kappaphycus alvarezii carrageenan. The waste products within the simulation contained a significant amount of ash, which is considered nonbiodegradable and may inflict both economic and environmental burdens on the factory, regardless of the ash classification. Declarations The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Competing Interests The authors declare no competing economic interests Funding The authors did not receive support from any organization for the submitted work. Author Contribution R.N Conceptualizes the research, A.D provides the wet lab information and aided with building a database. F.S does the experiment and wrote most of the paper including figures and tables, J.M, K.O and A.I are additional reviewers. 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Waste Biorefinery towards a sustainable biotechnological production of pediocin: Synergy Between Process Simulation and Environmental Assessment. Environmental Technology & Innovation , 26 , 102306. Retrieved 9th of July 2024, from: https://doi.org/10.1016/j.eti.2022.102306 Araque, J., Niño, L., & Gelves, G. (2020). Industrial scale bioprocess simulation for Ganoderma lucidum production using SuperPro designer. Journal of Physics: Conference Series, 1655(1), 012077. Retrieved 25th of June 2024, from: https://doi.org/10.1088/1742-6596/1655/1/012077 Goršek, A., & Glavič, P. (1997). Design of batch versus continuous processes. Chemical Engineering Research and Design , 75 (7), 718–723. Retrieved 24th of July 2024, from: https://doi.org/10.1205/026387697524218 Tan, J., Yee Foo, D. C., Kumaresan, S., & Abdul Aziz, R. (2019). Evaluation of Debottlenecking strategies for a liquid medicine production utilizing batch process simulation. 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IOP Conference Series: Earth and Environmental Science, 175, 012219. Retrieved 5th of August 2024, from: https://doi.org/10.1088/1755-1315/175/1/012219 Zenkovich, M. V., Drevs, Y. G., Inozemtseva, V. S., & Shevchenko, N. A. (2021). Industrial Plants Investment Projects Efficiency Estimation based on simulation and Artificial Intelligence Methods. Procedia Computer Science , 190 , 852–862. Retrieved 11th of august 2024, from: https://doi.org/10.1016/j.procs.2021.06.107 Naim, M. M., Wikner, J., & Grubbström, R. W. (2007). A net present value assessment of make-to-order and make-to-stock manufacturing systems. Omega, 35(5), 524–532. Retrieved 11th of august 2024, from: https://doi.org/10.1016/j.omega.2005.09.006 Additional Declarations No competing interests reported. Supplementary Files CarrageenanEconomicGeneratedReport.xls CarrageenanMaterialGeneratedReport.xls Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 01 Dec, 2024 Reviews received at journal 01 Dec, 2024 Reviews received at journal 25 Nov, 2024 Reviews received at journal 15 Nov, 2024 Reviewers agreed at journal 14 Nov, 2024 Reviewers agreed at journal 13 Nov, 2024 Reviewers agreed at journal 13 Nov, 2024 Reviewers invited by journal 10 Nov, 2024 Editor assigned by journal 10 Nov, 2024 Submission checks completed at journal 07 Nov, 2024 First submitted to journal 06 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. 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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-5405906","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":378780220,"identity":"625f3fe8-3981-40d0-b4fe-86ce99b5bb8c","order_by":0,"name":"Felix Subakti","email":"","orcid":"","institution":"Kyoto University","correspondingAuthor":false,"prefix":"","firstName":"Felix","middleName":"","lastName":"Subakti","suffix":""},{"id":378780221,"identity":"32aef97a-23f1-438e-b9bf-84577f7af53a","order_by":1,"name":"Ryozo 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01:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5405906/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5405906/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69822742,"identity":"9c380609-e7b2-4c87-a89b-fcdb783ed14d","added_by":"auto","created_at":"2024-11-25 14:29:52","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60736,"visible":true,"origin":"","legend":"\u003cp\u003eFactory Schematic as Visualized by SuperPro™\u003c/p\u003e","description":"","filename":"Figure1FactorySchematicasVisualizedbySuperProTM.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5405906/v1/e29e3ba98572063addc5bf58.jpg"},{"id":69822746,"identity":"5c8fc26c-a922-406c-9835-8a2f7e0860b4","added_by":"auto","created_at":"2024-11-25 14:29:53","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":109310,"visible":true,"origin":"","legend":"\u003cp\u003eSimplified Process Flowchart of Energy and Material\u003c/p\u003e","description":"","filename":"Figure2SimplifiedProcessFlowchartofEnergyandMaterial.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5405906/v1/8c76ecd001f8301f29155315.jpg"},{"id":69823279,"identity":"b530f76a-9599-4d93-a99c-ff225e7d671b","added_by":"auto","created_at":"2024-11-25 14:37:53","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":237982,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eGantt Chart for a Single Batch\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure3GanttChartsforaSingleBatch.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5405906/v1/59ef50168c127c4094263446.jpg"},{"id":69822745,"identity":"2153034d-31c9-48bb-b820-409da8d2a9a2","added_by":"auto","created_at":"2024-11-25 14:29:53","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":77767,"visible":true,"origin":"","legend":"\u003cp\u003eGantt Chart for Multiple Batches\u003c/p\u003e","description":"","filename":"Figure4GanttChartforMultipleBatches.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5405906/v1/044cedec31e3bc068d599aee.jpg"},{"id":69822743,"identity":"c7dafbe3-0347-4a03-b705-d743d68800c1","added_by":"auto","created_at":"2024-11-25 14:29:53","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":78811,"visible":true,"origin":"","legend":"\u003cp\u003eOperational Cost Breakdown and Classifications\u003c/p\u003e","description":"","filename":"Figure5OperationalCostBreakdownandClassifications.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5405906/v1/c04a049a3a1f80e28e81671e.jpg"},{"id":69823278,"identity":"acb3e157-724d-4939-956e-53429f2f7f8d","added_by":"auto","created_at":"2024-11-25 14:37:53","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":87297,"visible":true,"origin":"","legend":"\u003cp\u003eNPV Sensitivity Analysis Graph of the Production Line, Split Between Product Price, Feed Cost and Labor Costs\u003c/p\u003e","description":"","filename":"Figure6NPVSensitivityAnalysisGraphoftheProductionLineSplitBetweenProductPriceFeedCostandLaborCosts.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5405906/v1/75665b1681650372a36568e5.jpg"},{"id":69824589,"identity":"eb36df48-1cdb-4aa3-a081-5ea71d9663d5","added_by":"auto","created_at":"2024-11-25 14:45:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1487712,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5405906/v1/364228b3-a80a-456a-9d65-577c9973d600.pdf"},{"id":69822748,"identity":"6467f97c-cfa4-4b28-918d-619cb6402822","added_by":"auto","created_at":"2024-11-25 14:29:53","extension":"xls","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":75776,"visible":true,"origin":"","legend":"","description":"","filename":"CarrageenanEconomicGeneratedReport.xls","url":"https://assets-eu.researchsquare.com/files/rs-5405906/v1/c61d5e9a252b5314ea455ab8.xls"},{"id":69822749,"identity":"3d075ecf-88ec-4700-bb78-d3ee3bb7a88e","added_by":"auto","created_at":"2024-11-25 14:29:53","extension":"xls","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":152064,"visible":true,"origin":"","legend":"","description":"","filename":"CarrageenanMaterialGeneratedReport.xls","url":"https://assets-eu.researchsquare.com/files/rs-5405906/v1/316681c418b949fef21b23ed.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Techno-Economical Assessment of Kappaphycus alvarezii Carrageenan Extraction Plant","fulltext":[{"header":"1 INTRODUCTION","content":"\u003cp\u003eIndonesia, a prominent archipelagic nation, has successfully cultivated diverse seaweed varieties (Sultana et al., 2023), securing its position as the world\u0026rsquo;s second-largest producer of annual fresh seaweed tonnage, trailing behind China (World Bank, 2023). The harvested seaweeds are primarily exported in their raw state to more developed nations, with limited domestic consumption, primarily within the pharmaceutical sector. The processing of seaweeds to enhance their pharmaceutical value has focused on the extraction of fine chemicals, driven by extensive research into the medicinal and nutritional benefits of seaweeds.\u003c/p\u003e \u003cp\u003eOne such chemical that has received extensive scrutiny and has been extracted for pharmaceutical applications is \u003cem\u003eKappaphycus alvarezii\u003c/em\u003e carrageenan. The substance is characterized as a linear sulfated polysaccharide. Carrageenan is both flavorless and colorless (Guo et al., 2022). Most types of carrageenans exhibit gel-forming properties at different temperatures, except for λ-carrageenan, which maintains a predominantly rigid structure (Running et al., 2012). Carrageenan is commercially extracted from red algae of varying molecular masses, and is used in capsules, tablet coagulants, and dairy emulsifiers (Bongaerts et al., 1999).\u003c/p\u003e \u003cp\u003eIndonesia has several red seaweeds that yield carrageenan, which are currently being mass-cultivated across numerous significant islands. \u003cem\u003eKappaphycus alvarezii\u003c/em\u003e, formerly known as \u003cem\u003eEuchema cottonii\u003c/em\u003e, stands out among these species (Guo et al., 2022). Despite not being indigenous to the Indonesian archipelago (Ask \u0026amp; Batibasaga, Aisake \u0026amp; Zertuche, Jose \u0026amp; San, 2003), \u003cem\u003eKappaphycus alvarezii\u003c/em\u003e has successfully adapted to local Indonesian coasts, becoming an invasive species. The primary yield of this seaweed is κ-carrageenan, which justifies its reclassification. Thriving in coastal areas of warmer oceans, \u003cem\u003eKappaphycus alvarezii\u003c/em\u003e can be effectively cultivated using rope anchoring (Rama et al., 2018).\u003c/p\u003e \u003cp\u003eTo initiate the industrial-scale production of carrageenan from \u003cem\u003eKappaphycus alvarezii\u003c/em\u003e, establishing a pilot-scale production plant is imperative. Considering the inevitable cost-implications, establishing a production plant requires comprehensive calculations. This underscores the significance of employing factory simulation software, which facilitates multiple calculations and minimizes potential errors that may contribute to escalating capital costs (Casavant \u0026amp; C\u0026ocirc;t\u0026eacute;, 2004). This study aims to conceptualize a carrageenan extraction plant for treating \u003cem\u003eKappaphycus alvarezii\u003c/em\u003e, with the simulation yielding a feasible scheme from both the engineering and economic perspectives. Envisaging industrial-scale production of red-seaweed carrageenan, this initiative is expected to add value to fresh seaweed cultivated in Indonesia, thereby contributing to the advancement of the blue industrial revolution through the novel exploration of marine resources.\u003c/p\u003e"},{"header":"2 METHODOLOGY","content":"\u003cp\u003eThe simulation was preformed using SuperPro\u0026trade; as a Software Process Designer (SPD). The tools were obtained from parts of the software library and sized accordingly.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 SuperPro\u0026trade; Simulation Software\u003c/h2\u003e \u003cp\u003eSuperPro\u0026trade;, a commercially distributed software from Intelligen Inc., is a bioprocess-oriented production simulation software. This software contains multiple simulated chemical process units and additional supply chain-oriented units. The software version utilized in this assessment is equipped with SchedulePro\u0026trade;, which is available under the SuperPro\u0026trade; suite, and is integrated within the system. SchedulePro\u0026trade; is a scheduling software that identifies bottlenecks and provides solutions or overlaps batches to maximize the time efficiency.\u003c/p\u003e \u003cp\u003eSuperPro\u0026trade;, in general, works passively off the user\u0026rsquo;s input within the process and the material parameters. The materials in SuperPro\u0026trade; are mostly provided through the built-in library; however, if required in a specific case, users can input their own materials through the \u0026lsquo;substance and mixtures\u0026rsquo; menu. The software can perform a general economic assessment, technical assessment, and provide a Gantt chart that visually represents the batch schedule in a production plant. SuperPro\u0026trade; can also be integrated with Microsoft Excel to generate \u0026ldquo;. xls\u0026rdquo; reports that have broader appeal for investors and engineers alike.\u003c/p\u003e \u003cp\u003eThe economic aspects of SuperPro\u0026trade; are also passively influenced by manual input of the material costs, unit costs, labor, consumables, and utilities. Debt and repayment can also be managed through the investment ratio, taxes, and factory lifetime, which users can input into the software. Revenue, waste, and emissions can be classified per stream rather than per material.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Mode of Operation\u003c/h2\u003e \u003cp\u003eThe plant simulation was conducted within the framework of a batch system characterized by an annual operational time of 7920 h (Ahmed et al., 2022). Ahmed et al. suggested that 330 d was sufficient for a lean manufacturing simulation. This intentional selection of the operating time was also predicated based on the need to accommodate the intermittent downtimes necessitated by system upgrades or routine equipment maintenance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Definition of Components in the Simulation\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e lists the components used in the simulations. The components were collected in the SuperPro\u0026trade; built-in database; in this case, several additional components were inputted into the library due to default collection limitations.\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\u003eComponents and Definition of the Simulation\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\u003eComponents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDetails\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarrageenan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefined by the user\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMolar mass of 193 kg/mol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeaweed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixture of Proteins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFats, Ash, Cellulose,\u003c/p\u003e \u003cp\u003eBiomass and Carrageenan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCellulose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSuperPro\u0026trade; database,\u003c/p\u003e \u003cp\u003echemical formula\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 \u003cp\u003eAsh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSuperPro\u0026trade; database\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMolar mass of 200 g/mol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSuperPro\u0026trade; database\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMolar mass of 18.02 g/mol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSuperPro\u0026trade; database\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.72% Nitrogen,\u003c/p\u003e \u003cp\u003e23.29% Oxygen\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium Chloride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSuperPro\u0026trade; database\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMolar mass of 74.54 g/mol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium Oxide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSuperPro\u0026trade; database\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMolar mass of 56.11 g/mol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSuperPro\u0026trade; database\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDefined as CH\u003csub\u003e1.8\u003c/sub\u003eO\u003csub\u003e0.5\u003c/sub\u003eN\u003csub\u003e0.2\u003c/sub\u003e,\u003c/p\u003e \u003cp\u003eMolar mass of 147.60 g/mol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSuperPro\u0026trade; database\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDefined as CH\u003csub\u003e1.8\u003c/sub\u003eO\u003csub\u003e0.5\u003c/sub\u003eN\u003csub\u003e0.2\u003c/sub\u003e,\u003c/p\u003e \u003cp\u003eMolar mass of 147.60 g/mol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbohydrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSuperPro\u0026trade; database\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003csub\u003e6\u003c/sub\u003eH\u003csub\u003e12\u003c/sub\u003eO\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiomass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSuperPro\u0026trade; database\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDefined as CH\u003csub\u003e1.8\u003c/sub\u003eO\u003csub\u003e0.5\u003c/sub\u003eN\u003csub\u003e0.2\u003c/sub\u003e\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=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Chemical Modelling of Simulated Seaweed\u003c/h2\u003e \u003cp\u003eThe input material for this factory, namely. \u003cem\u003eKappaphycus\u003c/em\u003e seaweed, has multiple compositions, which were programmed by customizing the SuperPro\u0026trade; library and are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The baseline ratios were obtained through a literature review and wet-chemical information from the University of Riau, and were rounded to two decimal places (Diharmi et al., 2020).\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\u003eCarrageenan Stock Mixture Modelling\u003c/p\u003e \u003cdiv class=\"Credit\"\u003e\u003cp\u003e(Source, Diharmi, et.al, 2020)\u003c/p\u003e\u003c/div\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIngredient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFats\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarrageenan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiomass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100.00\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=\"Section2\"\u003e \u003ch2\u003e2.5 Carrageenan Modelling\u003c/h2\u003e \u003cp\u003eCarrageenan as a substrate does not possess any fixed properties; the researcher utilized information provided by the University of Riau to define the baseline properties for Carrageenan modeling. The molar mass and ash content were particularly variable; thus, a number from the literature review was used (Younes et al., 2018).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Unit Processes Involved in the Plant\u003c/h2\u003e \u003cp\u003eThe unit processes performed herein followed the unit library provided by the SuperPro\u0026trade; simulator software. Labeling followed the default automated unit labeling system built into the software; the final product may differ in labeling, but the overall functions typically remain the same. The SuperPro\u0026trade; library was deemed sufficient for unit process simulations in terms of technical feasibility, whereas the scheduling and economic parameters had to be modified according to more modern capabilities. The details of the unit processes are listed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\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\u003eClassification of Units Utilized in the Production Plant\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\u003eUnit type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnit Label\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFunction in the process\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransport truck\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTransport of raw feed to the factory site\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCold water washer 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWSH-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePartial removal of ash\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtractor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMSX-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRemoves carrageenan from raw feed with strong acid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCold water washer 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWSH-102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePartially washes the acid from carrageenan slurry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBatch vessel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSedimentation site\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlate and frame filter 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePFF-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFilters out sludge from water\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBatch vessel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV-102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeutralization using a strong base\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTray drying 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTDR-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDries the Carrageenan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrinder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGR-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProduces powder form of Carrageenan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlate and frame Filter 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePFF-102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRemoved by-product from water\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTray drying 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTDR-102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDries the by-product\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=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Equipment Sizing\u003c/h2\u003e \u003cp\u003eUser-inputted, design-calculated sizing was applied to the equipment operating within the factory, instead of a more customized throughput-calculated sizing, because of the lack of available information regarding the prices of customized chemical process equipment. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows a brief breakdown of the units required in the simulation, construction materials, and simulated capacity. The simulation did not use staggered or standby units.\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\u003eUnit Processes Utilized in the Plant\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo. of Units\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSize (Capacity)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMaterial of Construction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWSH-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWasher (Bulk Flow)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,000 kg/h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSX-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixer-Settler Extractor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,509 L/h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSS316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWSH-102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWasher (Bulk Flow)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,886 kg/h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlending Tank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27,870 L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSS316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFF-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlate \u0026amp; Frame Filter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e402 m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSS316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV-102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlending Tank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,256 L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSS316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTDR-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTray Dryer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e236 m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSS316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGR-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrinder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,413 kg/h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFF-102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlate \u0026amp; Frame Filter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88 m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSS316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTDR-102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTray Dryer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e322 m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSS316\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=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Power Demands of the Factory\u003c/h2\u003e \u003cp\u003eDominated by the electricity consumed by the unit processes established within the factory, the power demands were modeled in watts owing to the electrically driven nature of most processes. Note that the superheated steam was primarily modeled as an economic factor, and power costs were not considered. Most of the process-unit equipment were purchased from a third party and assembled onsite. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents a summary of the equipment required in the factory, with the baseline power demand and a reference for the power demand.\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\u003ePower Demands Per Unit of Process\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\u003eUnit label\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePower\u003c/p\u003e \u003cp\u003eDemand\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWSH-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.5 kWh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHenan Ocean Machinery Equipment Co., Ltd., 2024.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSX-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.0 kWh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWenzhou Chinz Machinery Co., Ltd., 2024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWSH-102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.5 kWh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHenan Ocean Machinery Equipment Co., Ltd., 2024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.0 kWh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHenan Xingyang Mining Machinery Manufactory, 2024.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFF-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.50 kWh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWoking Environmental Technology Co., Ltd., 2024.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV-102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.00 kWh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeishu Machinery Technology (Shanghai) Co., Ltd., 2024.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTDR-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.0 kWh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShandong Qike Machinery Equipment Co., Ltd., 2024.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGR-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.0 kWh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHarun et al., 2019.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFF-102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.50 kWh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWoking Environmental Technology Co., Ltd., 2024.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTDR-102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.0 kWh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShandong Qike Machinery Equipment Co., Ltd., 2024).\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=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Process Parameters\u003c/h2\u003e \u003cp\u003eEach discrete module within the simulation framework is tailored to align with the specific throughput and compositional characteristics of each sequential phase within the batch input stream (Arias et al., 2022). The implemented software facilitates automated labeling of individual units by incorporating inherent numerical identifiers. Temperature and pressure emerged as the predominant variables that were uniformly employed across all modules (Araque et al., 2020). Notably, the operation environment in the factory did not entail a vacuum, and for simulation purposes, atmospheric air was modeled as a composite mixture within the simulation library. Details of these processes are listed in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\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\u003eProcess Parameters and Conditions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnit label\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTemp.\u003c/p\u003e \u003cp\u003e(\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePressure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eConditions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTruck\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en/a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en/a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAssumed to be a singular unit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeaweed washing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWSH-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAtmospheric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75% ash removal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain extraction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMSX-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAtmospheric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStrong base extraction using KOH\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarrageenan washing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWSH-102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAtmospheric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAsh and KOH removal from the substrate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSedimentation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAtmospheric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdded water\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarrageenan filtration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePFF-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAtmospheric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAssumed LOD of 10% and 8 cm cake thickness\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutralization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV-102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAtmospheric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStrong acid KCl to counter the strong base\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarrageenan drying\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTDR-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAtmospheric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWater removal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProduct grinding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGR-101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAtmospheric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAssumed no dissipation to heat\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBy product filtration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePFF-102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAtmospheric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAssumed LOD 30% and 8 cm Cake thickness\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBy product drying\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTDR-102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAtmospheric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWater removal\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=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Contents of the Flow\u003c/h2\u003e \u003cp\u003eThe constituent elements of plant apparatus encompass the conveyance of raw materials and intermediate substances in diverse forms. Nevertheless, owing to the influence of the environmental parameters inherent to operational processes and discernible effects on the physical attributes of specific components, coupled with considerations of equipment sizing and constraints, a degree of anticipated incongruity in the volumetric content of the flow became evident. The expectations regarding what should be found in a particular flow for the entire simulation are listed in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\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\u003eProcess Flow Contents and Their Functions\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\u003eFlow label\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExpected contents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFlow purpose\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupplier\u0026rsquo;s stash\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaw Seaweed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStarts the whole process\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFresh chopped seaweed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaw seaweed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMain Process flow, feed for the washer\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst ash removal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsh and water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWaste classification\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWash water 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWasher input flow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKOH Mix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePotassium hydroxide and water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdditional Process flow, Extraction Solvent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWashed seaweed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeaweed components and water with reduced ash\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMain process flow, feed for the extractor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeaweed sludge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsh, Biomass, Carrageenan, Proteins, Water, KOH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecently extracted seaweeds.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWash Water 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater\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 \u003cp\u003eSecond ash removal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsh and water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWaste classification\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWashed sludge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsh, Biomass, Carrageenan, Proteins, Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFeed for sedimentation tank\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKCl Mix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePotassium Chloride and water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdditional Process flow: Neutralize the basic sludge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAir and Biomass\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 \u003cp\u003eSediments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarrageenan, Proteins, water\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 \u003cp\u003eFilter Cake 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProteins and Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWaste product of filtration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFiltered product\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarrageenan, proteins, water\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 \u003cp\u003eKCl Input\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKCl-water mix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeutralizing solution\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAir and water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeutralizing vessel exhaust\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWash water 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAiding in sedimentation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRaw Carrageenan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSolid Carrageenan, water, and proteins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCarrageenan sludge, feed for the tray drying\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude Carrageenan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSolid Carrageenan and protein\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 \u003cp\u003eCarrageenan powder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSolid Carrageenan and protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFinal product for the market, the main revenue flow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFilter cake 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsh, Biomass, Fats, KOH Sediments, Proteins, and water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWaste product of filtration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiomass, Carrageenan, Fats, KOH, Proteins, water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFiltered product feed for by-product tray drying.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRemoved water vapor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDried By-Product\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiomass, fats, protein, carbohydrates, Cellulose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBy-product, secondary revenue flow\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=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Plant Consumables\u003c/h2\u003e \u003cp\u003ePlant consumables encompass a category of material inputs that influence processes by modulating the environmental parameters, distinct from active involvement in operational processes. This classification arises from the capacity of the plant consumables to directly influence the operational costs, warranting amalgamation with such costs during a rigorous economic assessment. The consumables for the plant are recorded in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e with suitable quantitative units, preferably SI units, along with the respective functions.\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\u003eConsumables Required by the Plant\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsumables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMain dimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnit of measurement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFunction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuperheated steam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMTs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHeat exchanger agent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSuperPro\u0026trade; library\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCooling water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003em\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHeat exchanger agent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSuperPro\u0026trade; Library\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectricity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnergy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKW/h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnit process consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSuperPro\u0026trade; Library\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"},{"header":"3 RESULTS","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Factory Schematics\u003c/h2\u003e \u003cp\u003eThe plant generates two revenue flows and eight waste flows. The revenue flows were split into carrageenan powder as the primary revenue flow and a mixture of waste products that could be used as premium fertilizer as the secondary revenue flow. The wastes comprised ash, fats, used acids, and base- and tray-dried exhaust waste outputs, with ash being deemed the most troublesome because of its low potential value.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides an overview of the process simulation as generated by SuperPro\u0026trade; 10.3. The simulation contained a washer, grinder, reactor, three filtration units, centrifuge, three distillation towers, four heat exchanger units, and one freeze-drying unit. The flowchart presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the process and its demands. The flowchart includes details of the power demands and step-by-step breakdown of the process flow.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Time Constraints of the Factory\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Plant Operational Scheduling\u003c/h2\u003e \u003cp\u003eThe deviation from the conventional calendrical standards of 365 or 360 days within an operational timeframe of 7,920 h per annum was implemented as a deliberate measure to accurately capture and account for instances of maintenance and upgrade-related downtime. This approach acknowledges the inherent complexities in large-scale production lines, wherein interruptions to regular operations necessitate more comprehensive accounting for time to achieve a deeper understanding of the system's functioning. Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e lists the recipe cycle times between batches. The factory operated a batch process model instead of a continuous process (Goršek \u0026amp; Glavič, 1997).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eFactory's Batch Scheduling\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\u003eScheduling item\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual operating time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,918.43 h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecipe batch time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.17 h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecipe cycle time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.07 h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of batches per year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,830 cycles\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=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Plant Batch Scheduling\u003c/h2\u003e \u003cp\u003eBatch operation of a plant involves the orchestration of multiple interdependent processes within the operational framework. Notably, these processes are sequentially linked, wherein the output of a given process serves as the input for the successive processes. Additionally, specific processes operate concurrently, imparting a semi-continuous character to the overall processing regimen as the output seamlessly transitions into subsequent processing units (Tan et al., 2019).\u003c/p\u003e \u003cp\u003eMost processes delineated in the Gantt chart were configured within the parameters stipulated by the software, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Notably, several processes were assigned a time-span of 1 h to align with the conventional duration of a standard 8 h workday. Accordingly, the graphical representation in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e is demarcated into 8 h cycles. As a departure from the default flow or process set, which adhered to throughput-based computation, the pull-in and transfer-in processes within the factory were deliberately set to durations of less than 1 h. This adjustment was aimed at aligning with the principal process while accounting for scaling considerations and enhancing the overall representational fidelity of the simulation.\u003c/p\u003e \u003cp\u003eAfter considering the manner in which a single batch can be executed in a factory, multiple batches must be considered in parallel. In this case, a new batch can be started when the current batch is halfway to completion. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows how such overlapping processes can be conducted within the same plant. This arrangement allows more batches to be run within 24 h, thus increasing profitability and productivity (Joseph \u0026amp; B, 2021).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe material balance report can be broken down into a summary and stream details. The summary, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, was split into three columns, and consisted of the materials, annual demands, and batch demands.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eBatch and Annual Material Consumptions for the Factory\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaterial\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMT/yr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMT/batch\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKClMix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKOH mix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15,320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeaweed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44,045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103,410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170,435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.5\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\u003eThe water input was derived passively through a composite methodology involving manual adjustment of the per-unit input, as well as the computed demand of specific unit processes beyond the researcher's immediate purview. The main aim of the factory simulation was to process the seaweed supply using the amount of seaweed that was first determined by the researcher. Potassium hydroxide, potassium chloride, and water were used to fulfill the processing demands of raw seaweed input. This calculation was extended to encompass nuanced process demands, including those of a more esoteric nature, such as the requirement for a thermal jacket with fluid convection.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Revenue and Cost Classifications\u003c/h2\u003e \u003cp\u003eThe revenue flow was split into two major components: the main stream of revenue consisted mostly of \u003cem\u003eKappaphycus alvarezii\u003c/em\u003e carrageenan, and the by-product comprised multiple biodegradable components. The Capital costs are split into various elements, such as the establishment price, labor costs, and unit purchases (Al-Sharrah \u0026amp; Marafi, 2023). The pie chart in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that the raw materials are the primary contributors to the operating cost. R\u0026amp;D will be conducted chiefly through university collaborations with external funding; thus, the costs in this chart will be lowered by default.\u003c/p\u003e \u003cp\u003eWaste disposal has a low contribution to the cost, consisting solely of the costs of disposing ash and biomass. The energy costs of waste disposal are divided into utility and unit costs with depreciation. Land-based truck transportation is the main mode of transportation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Materials and Energy\u003c/h2\u003e \u003cp\u003eThe generated income comprised \u003cem\u003eKappaphycus alvarezii\u003c/em\u003e carrageenan output as the main product and the secondary biomass-dominated by-product. The calculated yield for these substances was heavily influenced by the composition of the fresh seaweed, which is the primary feedstock of this factory, assuming that the composition would not change in the long run. The factory produces approximately 3,143 kg of carrageenan powder from 11,000 kg of fresh seaweed.\u003c/p\u003e \u003cp\u003eThe main contributor to the energy consumption is the electricity required to power process units within the factory. Some additional energy consumption in the model is expected in the transport phase using long-haul trucks that run primarily on diesel fuel. However, the software opted to treat the input fuel consumption directly as an economic burden instead of passively as fuel consumption.\u003c/p\u003e \u003cp\u003eThis factory has a built-in waste treatment process for separating low-value ash waste from biomass and biodegradable waste with potential economic value. Although built-in waste treatment processes within factories can contribute to a more independently run factory because the presence of any third-party waste treatment service is rendered unnecessary, the upfront capital required to establish this process is much higher and may be a prelude to higher debt or required investments.\u003c/p\u003e \u003cp\u003eThe factory had two plate and frame filter units. Although this filter is commonplace and deemed necessary by the author, the carrageenan found in the simulated results of the first filtration should be neglected. Carrageenan is the main product; thus, neglecting carrageenan within the by-product load may reduce both the yield and profitability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Assessment of the Predicted Final Product\u003c/h2\u003e \u003cp\u003eThe final marketable product of this factory is solid carrageenan in powdered form. The final product contained less than 0.1% impurities, which complies with the FAO standards for carrageenan purity. The calculation showed that the ash was completely removed through multiple separation processes in the plant simulation. Ash within the simulation was considered as a monolith instead of the commonplace dual classification of acid-soluble and acid-insoluble wastes.\u003c/p\u003e \u003cp\u003eA minor hurdle in harnessing the final product is the loss of carrageenan due to bulk filtering. Bulk filtering was deemed an appropriate choice for economic reasons; however, from the technical perspective, the carrageenan lost by filtering can cause a loss of potential revenue.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Economic Breakdown\u003c/h2\u003e \u003cp\u003eThe intent is for the factory to process and transport freshly harvested seaweed instead of establishing an in-house cultivation unit or seaweed farm. This significantly increases the cost required to run the factory, with raw material procurement dominating the cost. The utilities mainly consist of the electricity needed to run the unit processes, whereas ash removal increases the waste-treatment cost.\u003c/p\u003e \u003cp\u003eThe factory payment system was evaluated through a meticulous cash flow analysis with a protracted 20-year payment horizon. Notably, in typical scenarios, a factory commences operations with an initial phase of nonprofitability, gradually transitioning to capital returns after a minimum of 10\u0026nbsp;year. The economic framework of the analysis predicts a flat tax rate of 6%. The factory profitability was measured by examining two crucial financial metrics: the internal rate of return (IRR) and net present value (NPV).\u003c/p\u003e \u003cp\u003eThe IRR represents the discount rate at which a project's NPV attains equilibrium, reaching zero (Zenkovich et al., 2021). Concurrently, the NPV is defined as the sum of all forthcoming cash flows, encompassing both positive and negative flows, throughout the lifespan of an investment and is meticulously discounted to the present value (Naim et al., 2007). This analytical framework ensures a rigorous and comprehensive assessment of the factory payment system, thereby enhancing the investigation's scholarly rigor and depth.\u003c/p\u003e \u003cp\u003eThe costs of energy and water in the plant were modeled after the national electricity and water prices set by the Indonesian Government, which means that the costs were somewhat subsidized. The classification of this plant fell under \u0026lsquo;Industri skala besar\u0026rsquo; (large-scale industries), which warrants a reduced purchase price for both water and energy. Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e shows the potential payback time and gross margins of the factory, as generated by SuperPro\u0026trade; software.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eBasic Economic Breakdown of the Factory\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\u003eTotal Capital Investment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,914,000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCapital Investment Charged to This Project\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,914,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperating Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,950,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e/year\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRevenues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,109,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e/year\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGross Margin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.23\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\u003eReturn on Investment (ROI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.35\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\u003ePayback Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyears\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIRR (After Taxes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.11\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\u003eNPV (at 7.0% Interest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e\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=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents a sensitivity analysis for determining which of the many market factors has the strongest impact on the factory\u0026rsquo;s profitability (represented by the NPV) in the long run. The authors considered three significant factors: the price of the sold product, price of seaweed as factory feed, and cost of labor (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The factory was most strongly affected by fluctuations in the carrageenan market selling price, with labor costs affecting it the least. The three graphs meet at the equilibrium point close to 0%, where the prices of these components will cause the factory to barely break-even, even when covering the operations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eThe analysis shows that the upscaling of a \u003cem\u003eKappaphycus alvarezii\u003c/em\u003e carrageenan extraction factory is technically and economically feasible within a 10-year payback period, assuming the products can find considerable demand on the market. Certain considerations must be made regarding the waste treatment and secondary product manufacturing. In the present study, there were no clear prospects for secondary products that could properly supplement the income provided by \u003cem\u003eKappaphycus alvarezii\u003c/em\u003e carrageenan. The waste products within the simulation contained a significant amount of ash, which is considered nonbiodegradable and may inflict both economic and environmental burdens on the factory, regardless of the ash classification.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting Interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing economic interests\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors did not receive support from any organization for the submitted work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eR.N Conceptualizes the research, A.D provides the wet lab information and aided with building a database. 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Retrieved 11th of august 2024, from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.omega.2005.09.006\u003c/span\u003e\u003cspan address=\"10.1016/j.omega.2005.09.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"","identity":"journal-of-applied-phycology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"10811","submissionUrl":"https://submission.nature.com/new-submission/10811/3","title":"Journal of Applied Phycology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Biorefinery, Carrageenan, Process Simulations, Process optimizations, Kappaphycus alvarezii, Waste Treatment","lastPublishedDoi":"10.21203/rs.3.rs-5405906/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5405906/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eKappaphycus alvarezii\u003c/em\u003e carrageenan extraction was optimized using a software-based process simulation. The study focused on the Indonesian seaweed industry, utilizing the advanced modeling capabilities of \u0026ldquo;SuperPro\u0026trade;\u0026rdquo;. The simulation involved comprehensive analysis of the extraction process, from dried seaweed transport to final carrageenan production. Furthermore, an economic sensitivity analysis was conducted, incorporating the seaweed production cost as a critical parameter. This analysis provides valuable insights into the financial viability of \u003cem\u003eKappaphycus alvarezii\u003c/em\u003e carrageenan extraction by considering the variations in the input costs, market prices, and other economic factors. The data show that a factory producing \u003cem\u003eKappaphycus alvarezii\u003c/em\u003e carrageenan is both technically and economically feasible within a 10-year lifespan. The plant processes 11.5 MT of raw \u003cem\u003eKappaphycus\u003c/em\u003e seaweed per batch, amounting to an annual input of 44.045 MT raw materials, and an annual output of 3.14 MTs of carrageenan, totaling the processing of 13,074 MTs of \u003cem\u003eKappaphycus alvarezii\u003c/em\u003e carrageenan annually with a payback period of around 8\u0026nbsp;year and a return on investment of 11.33%. The results provide information for stakeholders, including seaweed farmers, processors, and policymakers, about the potential financial benefits and challenges associated with scaling-up \u003cem\u003eKappaphycus alvarezii\u003c/em\u003e carrageenan extraction in Indonesia.\u003c/p\u003e","manuscriptTitle":"Techno-Economical Assessment of Kappaphycus alvarezii Carrageenan Extraction Plant","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-25 14:29:48","doi":"10.21203/rs.3.rs-5405906/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-02T02:33:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-02T02:15:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-25T18:40:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-16T03:54:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61985008532744108985270222244679580279","date":"2024-11-15T00:04:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333923078514426981622179447413847381435","date":"2024-11-13T18:33:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213886664457554437879723469591110424430","date":"2024-11-13T15:46:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-11T01:51:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-11T01:44:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-07T08:49:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Applied Phycology","date":"2024-11-07T01:42:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"","identity":"journal-of-applied-phycology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"10811","submissionUrl":"https://submission.nature.com/new-submission/10811/3","title":"Journal of Applied Phycology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"bd6cbc42-065e-4ccc-820f-c78ebad28ebb","owner":[],"postedDate":"November 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-02-04T04:53:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-25 14:29:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5405906","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5405906","identity":"rs-5405906","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}