Pub Date : 2025-02-15DOI: 10.1016/j.compchemeng.2025.109046
Deepshikha Singh, Antarim Dutta, Ankur Gaur, Shabih Ul Hasan
The present contribution is first of its kind in the field of conceptual designs of reactive distillation (RD) configurations, focusing on the impact of nonideal kinetics in obtaining the feasible designs of desired selectivity for complex reaction schemes, both with and without inert components. Our earlier work on selectivity engineering with reactive distillation through a series of publications was restricted to complex reaction schemes with ideal kinetics only. In this work, we extend it for nonideal kinetics and explore the impact of nonideal kinetics on the choice of hybrid RD configuration (HRDC) needed to achieve the desired selectivity for intermediate products. It has been found that the choice of HRDC strongly depends on the number of components involved, including inert components in a given complex reaction scheme with nonideal kinetics. The developed methodology was successfully applied to four industrially important multireaction schemes that featured nonideal kinetics with/without inerts.
{"title":"Selectivity engineering with single-feed hybrid reactive distillation configurations: Complex reaction schemes having nonideal kinetics with/without inerts","authors":"Deepshikha Singh, Antarim Dutta, Ankur Gaur, Shabih Ul Hasan","doi":"10.1016/j.compchemeng.2025.109046","DOIUrl":"10.1016/j.compchemeng.2025.109046","url":null,"abstract":"<div><div>The present contribution is first of its kind in the field of conceptual designs of reactive distillation (RD) configurations, focusing on the impact of nonideal kinetics in obtaining the feasible designs of desired selectivity for complex reaction schemes, both with and without inert components. Our earlier work on selectivity engineering with reactive distillation through a series of publications was restricted to complex reaction schemes with ideal kinetics only. In this work, we extend it for nonideal kinetics and explore the impact of nonideal kinetics on the choice of hybrid RD configuration (HRDC) needed to achieve the desired selectivity for intermediate products. It has been found that the choice of HRDC strongly depends on the number of components involved, including inert components in a given complex reaction scheme with nonideal kinetics. The developed methodology was successfully applied to four industrially important multireaction schemes that featured nonideal kinetics with/without inerts.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"197 ","pages":"Article 109046"},"PeriodicalIF":3.9,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-14DOI: 10.1016/j.compchemeng.2025.109060
Sumit K. Bishnu , Sabla Y. Alnouri , Dhabia M. Al Mohannadi
There is a need for comprehensive tools that combine data-driven modeling with optimization techniques. In this work, a robust Random Forest Regression (RFR) model was developed to capture the behavior and characteristics of a Sorption Enhanced Steam Methane Reformer (SE-SMR) Reactor system. This model was then integrated into a Simulated Annealing (SA) optimization framework that helped identify the optimal operating conditions for the unit. The combined approach demonstrates the potential of using machine learning models in conjunction with optimization techniques to improve the solving process. The proposed methodology achieved an optimal methane conversion rate of 0.99979, and was successful in effectively identifying the optimal operating conditions that were required for near-complete conversion.
{"title":"Stochastic algorithm-based optimization using artificial intelligence/machine learning models for sorption enhanced steam methane reformer reactor","authors":"Sumit K. Bishnu , Sabla Y. Alnouri , Dhabia M. Al Mohannadi","doi":"10.1016/j.compchemeng.2025.109060","DOIUrl":"10.1016/j.compchemeng.2025.109060","url":null,"abstract":"<div><div>There is a need for comprehensive tools that combine data-driven modeling with optimization techniques. In this work, a robust Random Forest Regression (RFR) model was developed to capture the behavior and characteristics of a Sorption Enhanced Steam Methane Reformer (SE-SMR) Reactor system. This model was then integrated into a Simulated Annealing (SA) optimization framework that helped identify the optimal operating conditions for the unit. The combined approach demonstrates the potential of using machine learning models in conjunction with optimization techniques to improve the solving process. The proposed methodology achieved an optimal methane conversion rate of 0.99979, and was successful in effectively identifying the optimal operating conditions that were required for near-complete conversion.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"196 ","pages":"Article 109060"},"PeriodicalIF":3.9,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Working fluid selection is a crucial part of organic Rankine cycle (ORC) designs. Traditional selection methods primarily focus on optimizing performance under specific nominal operating conditions, often neglecting potential efficiency losses and feasibility issues that may arise under off-design conditions due to fluctuations in the heat source and sink. This research introduces a novel method for optimizing working fluid selection to achieve robust and efficient operation in the face of environmental variations. Specifically, operational flexibility is analyzed based on the ORC operational model to capture performance deviations from nominal conditions, and is quantified by evaluating the size of the feasible operational region within the uncertain parameter space. Working fluid selection is optimized simultaneously with the cycle configurations, resulting in a computationally challenging mixed-integer nonlinear programming (MINLP) problem, which is addressed through Bayesian optimization. A case study on geothermal brine heat recovery with a recuperative ORC compares flexibility-oriented and conventional working fluid selections, demonstrating a 102% increase in operational flexibility at the cost of an 11.5% efficiency loss. This research underscores the significant impact of working fluid selection on operational flexibility and demonstrates the effectiveness of Bayesian optimization in solving complex MINLP problems for integrated molecule-level and process-level designs.
{"title":"Operational flexibility-oriented selection of working fluid for organic Rankine cycles via Bayesian optimization","authors":"Jiayuan Wang, Yuxin Zhang, Chentao Mei, Lingyu Zhu","doi":"10.1016/j.compchemeng.2025.109043","DOIUrl":"10.1016/j.compchemeng.2025.109043","url":null,"abstract":"<div><div>Working fluid selection is a crucial part of organic Rankine cycle (ORC) designs. Traditional selection methods primarily focus on optimizing performance under specific nominal operating conditions, often neglecting potential efficiency losses and feasibility issues that may arise under off-design conditions due to fluctuations in the heat source and sink. This research introduces a novel method for optimizing working fluid selection to achieve robust and efficient operation in the face of environmental variations. Specifically, operational flexibility is analyzed based on the ORC operational model to capture performance deviations from nominal conditions, and is quantified by evaluating the size of the feasible operational region within the uncertain parameter space. Working fluid selection is optimized simultaneously with the cycle configurations, resulting in a computationally challenging mixed-integer nonlinear programming (MINLP) problem, which is addressed through Bayesian optimization. A case study on geothermal brine heat recovery with a recuperative ORC compares flexibility-oriented and conventional working fluid selections, demonstrating a 102% increase in operational flexibility at the cost of an 11.5% efficiency loss. This research underscores the significant impact of working fluid selection on operational flexibility and demonstrates the effectiveness of Bayesian optimization in solving complex MINLP problems for integrated molecule-level and process-level designs.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"197 ","pages":"Article 109043"},"PeriodicalIF":3.9,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-11DOI: 10.1016/j.compchemeng.2025.109047
Naeme Zarrinpoor , Kannan Govindan
This research aims to offer a biodiesel supply chain design by utilizing microalgae as the feedstock. The model examines both economic optimization and the intricately interconnected nexus of natural resources so that overall costs, water consumption, released emissions, and food loss are all minimized, and the amount of clean energy production is maximized. In order to prevent diminishing fresh water supplies, this study employs sewage and saline water as additional sources of water. Furthermore, the suggested model employs sewage water as a source of nutrients to reduce fertilizer rivalry between biomass and agricultural output. The model accounts for the uncertainty of important characteristics including costs, resources availability, and demand. A handling method for uncertainty based on robust optimization, possibilistic programming, and flexible programming is created. An Iranian case study is utilized to verify the model and uncertainty handling method.
{"title":"A supply chain design for creating microalgae-based biodiesel considering resources nexus and uncertainty","authors":"Naeme Zarrinpoor , Kannan Govindan","doi":"10.1016/j.compchemeng.2025.109047","DOIUrl":"10.1016/j.compchemeng.2025.109047","url":null,"abstract":"<div><div>This research aims to offer a biodiesel supply chain design by utilizing microalgae as the feedstock. The model examines both economic optimization and the intricately interconnected nexus of natural resources so that overall costs, water consumption, released emissions, and food loss are all minimized, and the amount of clean energy production is maximized. In order to prevent diminishing fresh water supplies, this study employs sewage and saline water as additional sources of water. Furthermore, the suggested model employs sewage water as a source of nutrients to reduce fertilizer rivalry between biomass and agricultural output. The model accounts for the uncertainty of important characteristics including costs, resources availability, and demand. A handling method for uncertainty based on robust optimization, possibilistic programming, and flexible programming is created. An Iranian case study is utilized to verify the model and uncertainty handling method.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"197 ","pages":"Article 109047"},"PeriodicalIF":3.9,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-11DOI: 10.1016/j.compchemeng.2025.109028
Feiya Lv , Borui Yang , Shujian Yu , Shengwu Zou , Xiaolin Wang , Jinsong Zhao , Chenglin Wen
Reasoning about cause and effect in industrial processes is fundamental to fault diagnosis. However, traditional methods for causal discovery and fault diagnosis are typically developed separately, resulting in complex and fragmented approaches that lack transparency and interpretability. Since the explicit identification of root causes from causal graphs remains an open issue, we propose a unified diagnosis model for chemical processes that integrates causal discovery, fault detection, and root cause diagnosis within a single framework. Granger causality is learned from monitoring time-series data for online predictions. This causal embedding ensures that prediction deviations occur only in variables causally linked to the root cause, effectively mitigating the ’smearing effect’ caused by unrelated variables. The explicit causal graph provides interpretive insights into fault propagation and enhances the traceability of the diagnostic process by enabling the identification of fault evolution paths and root causes. Experimental results on synthetic data, a continuously stirred-tank reactor (CSTR) process, and a real-world continuous catalytic reforming (CCR) process demonstrate that our approach achieves high diagnostic accuracy and low false alarm rates, offering a practical, interpretable, and scalable solution for fault diagnosis in industrial chemical processes.
{"title":"A unified model integrating Granger causality-based causal discovery and fault diagnosis in chemical processes","authors":"Feiya Lv , Borui Yang , Shujian Yu , Shengwu Zou , Xiaolin Wang , Jinsong Zhao , Chenglin Wen","doi":"10.1016/j.compchemeng.2025.109028","DOIUrl":"10.1016/j.compchemeng.2025.109028","url":null,"abstract":"<div><div>Reasoning about cause and effect in industrial processes is fundamental to fault diagnosis. However, traditional methods for causal discovery and fault diagnosis are typically developed separately, resulting in complex and fragmented approaches that lack transparency and interpretability. Since the explicit identification of root causes from causal graphs remains an open issue, we propose a unified diagnosis model for chemical processes that integrates causal discovery, fault detection, and root cause diagnosis within a single framework. Granger causality is learned from monitoring time-series data for online predictions. This causal embedding ensures that prediction deviations occur only in variables causally linked to the root cause, effectively mitigating the ’smearing effect’ caused by unrelated variables. The explicit causal graph provides interpretive insights into fault propagation and enhances the traceability of the diagnostic process by enabling the identification of fault evolution paths and root causes. Experimental results on synthetic data, a continuously stirred-tank reactor (CSTR) process, and a real-world continuous catalytic reforming (CCR) process demonstrate that our approach achieves high diagnostic accuracy and low false alarm rates, offering a practical, interpretable, and scalable solution for fault diagnosis in industrial chemical processes.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"196 ","pages":"Article 109028"},"PeriodicalIF":3.9,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-11DOI: 10.1016/j.compchemeng.2025.109036
Alexander W. Rogers , Amanda Lane , Cesar Mendoza , Simon Watson , Adam Kowalski , Philip Martin , Dongda Zhang
In today's competitive and dynamic global markets, rapidly designing processes for formulated products – complex blends such as cosmetics, detergents, or personal care goods – is both essential and challenging. Understanding how processing conditions and chemical composition interact to determine product key performance indicators (KPIs) often remains unclear. In this work, we introduce a novel model-based design of experiments (MbDoE) framework that combines artificial neural network feature attribution with symbolic regression (SR) to uncover interpretable physical relationships. By leveraging feature attribution to guide the search within SR's large combinatorial space, our method efficiently targets structural improvements in candidate models. Additionally, a strategic sampling approach determining the most informative time points to measure KPI determining attributes ensures that each experiment yields maximum information. Applied to a comprehensive in-silico case study, the framework successfully recovered the differential equations for the underlying mechanisms driving the rate of change in the KPIs during the formulated product manufacturing process and reduced the required number of experiments threefold, even with limited data availability. These results highlight the significant potential of artificial neural network guided SR-MbDoE to accelerate process flow diagram development, enhance understanding of complex formulated processes, and improve decision-making in the chemical industry.
{"title":"Integrating feature attribution and symbolic regression for automatic model structure identification and strategic sampling","authors":"Alexander W. Rogers , Amanda Lane , Cesar Mendoza , Simon Watson , Adam Kowalski , Philip Martin , Dongda Zhang","doi":"10.1016/j.compchemeng.2025.109036","DOIUrl":"10.1016/j.compchemeng.2025.109036","url":null,"abstract":"<div><div>In today's competitive and dynamic global markets, rapidly designing processes for formulated products – complex blends such as cosmetics, detergents, or personal care goods – is both essential and challenging. Understanding how processing conditions and chemical composition interact to determine product key performance indicators (KPIs) often remains unclear. In this work, we introduce a novel model-based design of experiments (MbDoE) framework that combines artificial neural network feature attribution with symbolic regression (SR) to uncover interpretable physical relationships. By leveraging feature attribution to guide the search within SR's large combinatorial space, our method efficiently targets structural improvements in candidate models. Additionally, a strategic sampling approach determining the most informative time points to measure KPI determining attributes ensures that each experiment yields maximum information. Applied to a comprehensive in-silico case study, the framework successfully recovered the differential equations for the underlying mechanisms driving the rate of change in the KPIs during the formulated product manufacturing process and reduced the required number of experiments threefold, even with limited data availability. These results highlight the significant potential of artificial neural network guided SR-MbDoE to accelerate process flow diagram development, enhance understanding of complex formulated processes, and improve decision-making in the chemical industry.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"197 ","pages":"Article 109036"},"PeriodicalIF":3.9,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-10DOI: 10.1016/j.compchemeng.2025.109049
Olakunle Ayodeji Omole , Chukwuma C. Ogbaga , Jude A. Okolie , Olugbenga Akande , Richard Kimera , Joseph Lepnaan Dayil
This paper examines machine learning (ML)'s contemporary applications in biofuel production, emphasizing microalgae-based bioenergy systems. The study aims to explore various aspects of ML integration in the biofuel production process, including microalgae detection, classification, growth phase optimization, and dataset quality and quantity considerations. The research methodology is in a detailed literature review of current ML models and their applications in biofuel production. It covers bioenergy systems, microalgae detection, growth phase optimization, dataset quality, ML applications in microalgal biorefineries, and the advantages and disadvantages of ML models over first-principle models. The analysis highlights the challenges and implications of utilizing smaller datasets in biofuel production models and investigates the impact of dataset quality and quantity on ML model performance. Despite sparse datasets, the findings offer insights into leveraging ML techniques for improved efficiency and sustainability in microalgae-based biofuel production systems.
{"title":"Advancing algal biofuel production through data-driven insights: A comprehensive review of machine learning applications","authors":"Olakunle Ayodeji Omole , Chukwuma C. Ogbaga , Jude A. Okolie , Olugbenga Akande , Richard Kimera , Joseph Lepnaan Dayil","doi":"10.1016/j.compchemeng.2025.109049","DOIUrl":"10.1016/j.compchemeng.2025.109049","url":null,"abstract":"<div><div>This paper examines machine learning (ML)'s contemporary applications in biofuel production, emphasizing microalgae-based bioenergy systems. The study aims to explore various aspects of ML integration in the biofuel production process, including microalgae detection, classification, growth phase optimization, and dataset quality and quantity considerations. The research methodology is in a detailed literature review of current ML models and their applications in biofuel production. It covers bioenergy systems, microalgae detection, growth phase optimization, dataset quality, ML applications in microalgal biorefineries, and the advantages and disadvantages of ML models over first-principle models. The analysis highlights the challenges and implications of utilizing smaller datasets in biofuel production models and investigates the impact of dataset quality and quantity on ML model performance. Despite sparse datasets, the findings offer insights into leveraging ML techniques for improved efficiency and sustainability in microalgae-based biofuel production systems.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"196 ","pages":"Article 109049"},"PeriodicalIF":3.9,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-10DOI: 10.1016/j.compchemeng.2025.109048
Qiang Zhou , Yefei Yang , Fangfang Ma
Dynamic market design through cooperative advertising programs is conducive to generating immediate sales as well as win-win or Pareto-efficient yields. However, in a decentralized supply chain, there is no known optimal advertising and ordering policy for different participants. Motivated by this, we integrate inventory control with the expectation-based reference price in a two-player supply chain where a manufacturer provides a cooperative advertising program for a retailer. After eliciting the problem as an infinite-horizon Stackelberg game, we propose a novel Leader-Follower Deep Deterministic Policy Gradient (LFDDPG) algorithm. Extensive experiments show that our algorithm significantly outperforms metaheuristics with regard to different configurations of demand distribution and costs. Similar results are observed using data from a real-life manufacturer. Furthermore, our algorithm is robust in hyperparameter spaces and exhibits superior convergence behavior. These findings provide valuable insights and pave the way for future research and practical implementations in supply chains with complex decision-making processes.
{"title":"A Stackelberg-based deep reinforcement learning approach for dynamic cooperative advertising in a two-echelon supply chain","authors":"Qiang Zhou , Yefei Yang , Fangfang Ma","doi":"10.1016/j.compchemeng.2025.109048","DOIUrl":"10.1016/j.compchemeng.2025.109048","url":null,"abstract":"<div><div>Dynamic market design through cooperative advertising programs is conducive to generating immediate sales as well as win-win or Pareto-efficient yields. However, in a decentralized supply chain, there is no known optimal advertising and ordering policy for different participants. Motivated by this, we integrate inventory control with the expectation-based reference price in a two-player supply chain where a manufacturer provides a cooperative advertising program for a retailer. After eliciting the problem as an infinite-horizon Stackelberg game, we propose a novel Leader-Follower Deep Deterministic Policy Gradient (LFDDPG) algorithm. Extensive experiments show that our algorithm significantly outperforms metaheuristics with regard to different configurations of demand distribution and costs. Similar results are observed using data from a real-life manufacturer. Furthermore, our algorithm is robust in hyperparameter spaces and exhibits superior convergence behavior. These findings provide valuable insights and pave the way for future research and practical implementations in supply chains with complex decision-making processes.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"196 ","pages":"Article 109048"},"PeriodicalIF":3.9,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143452981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-10DOI: 10.1016/j.compchemeng.2025.109045
Lea Trescher , Lena-Marie Ränger , David Mogalle , Tobias Seidel , Michael Bortz , Thomas Grützner
Introductory studies on the relationship between design and robustness of optimized Dividing Wall Columns are presented. These columns are optimized multicriterially for different mixtures and numbers of stages. The internal distribution of stages depending on the total number is analyzed. Supporting Material analyzes the stage-dependent vapor demand of the binary sub-systems and shows that the relationships can change fundamentally, especially for low stages. The deviations in process variables considered are defined and screening results are presented. Supporting Material provides an analysis of the nominal and disturbed concentration profiles for deviating internal splits, and shows that at high numbers of stages, the formation of pinch zones leads to significantly higher purity losses. Finally, results for combined deviations are presented. For all result parts, patterns are worked out that depend only on the number of stages, specific mixture properties like characteristics of the Vmin diagram or the individual product.
{"title":"Pareto-optimized Dividing Wall Columns for Ideal Mixtures and Influences of Deviations in Process Variables","authors":"Lea Trescher , Lena-Marie Ränger , David Mogalle , Tobias Seidel , Michael Bortz , Thomas Grützner","doi":"10.1016/j.compchemeng.2025.109045","DOIUrl":"10.1016/j.compchemeng.2025.109045","url":null,"abstract":"<div><div>Introductory studies on the relationship between design and robustness of optimized Dividing Wall Columns are presented. These columns are optimized multicriterially for different mixtures and numbers of stages. The internal distribution of stages depending on the total number is analyzed. Supporting Material analyzes the stage-dependent vapor demand of the binary sub-systems and shows that the relationships can change fundamentally, especially for low stages. The deviations in process variables considered are defined and screening results are presented. Supporting Material provides an analysis of the nominal and disturbed concentration profiles for deviating internal splits, and shows that at high numbers of stages, the formation of pinch zones leads to significantly higher purity losses. Finally, results for combined deviations are presented. For all result parts, patterns are worked out that depend only on the number of stages, specific mixture properties like characteristics of the <em>V<sub>min</sub></em> diagram or the individual product.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"196 ","pages":"Article 109045"},"PeriodicalIF":3.9,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-09DOI: 10.1016/j.compchemeng.2025.109038
Syed Muhammad Aun Rizvi, Khurram Kamal, Tahir Abdul Hussain Ratlamwala
This study presents a comprehensive mathematical simulation using Simulink software for a novel hybrid waste-to-energy sustainable system tailored for restaurant communities. The system integrates a microbial fuel cell with subsystems for biodiesel production, anaerobic biogas digestion, methane reforming, and methanol production. The hybrid system aims to convert 500 kg of waste cooking oil, 2000 kg of food waste, and wastewater produced daily by the community into valuable resources. Results revealed that the system can produce 319,376 kWh of electricity, 14.6 t of H2 gas, 116.8 t of CO2 and 525 m3 of purified water annually. These outputs provide a net saving/profit of $245,530 with a return on investment of just 6 months. Additionally, the system demonstrates environmental benefits by reducing annual emissions by 200 tCO2 and 27.450 tCH4. The findings highlight the hybrid system's effectiveness in mitigating environmental impact, generating clean energy and valuable fuels, and advancing sustainable waste management practices within restaurant communities.
{"title":"Mathematical modelling of a sustainable energy system for restaurant communities: Waste-to-H2 conversion, CO2 sequestration, clean fuel production, and power generation","authors":"Syed Muhammad Aun Rizvi, Khurram Kamal, Tahir Abdul Hussain Ratlamwala","doi":"10.1016/j.compchemeng.2025.109038","DOIUrl":"10.1016/j.compchemeng.2025.109038","url":null,"abstract":"<div><div>This study presents a comprehensive mathematical simulation using Simulink software for a novel hybrid waste-to-energy sustainable system tailored for restaurant communities. The system integrates a microbial fuel cell with subsystems for biodiesel production, anaerobic biogas digestion, methane reforming, and methanol production. The hybrid system aims to convert 500 kg of waste cooking oil, 2000 kg of food waste, and wastewater produced daily by the community into valuable resources. Results revealed that the system can produce 319,376 kWh of electricity, 14.6 t of H<sub>2</sub> gas, 116.8 t of CO<sub>2</sub> and 525 m<sup>3</sup> of purified water annually. These outputs provide a net saving/profit of $245,530 with a return on investment of just 6 months. Additionally, the system demonstrates environmental benefits by reducing annual emissions by 200 tCO<sub>2</sub> and 27.450 tCH<sub>4.</sub> The findings highlight the hybrid system's effectiveness in mitigating environmental impact, generating clean energy and valuable fuels, and advancing sustainable waste management practices within restaurant communities.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"199 ","pages":"Article 109038"},"PeriodicalIF":3.9,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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