Pub Date : 2025-02-17DOI: 10.1016/j.compchemeng.2025.109035
Martin Bubel , Jochen Schmid , Maximilian Carmesin , Volodymyr Kozachynskyi , Erik Esche , Michael Bortz
Models are commonly utilized in chemical engineering to simulate real-world processes and phenomena. Given their role in guiding decision-making, accurately quantifying the uncertainty of these models is essential. Typically, these models are calibrated using experimental data that contain measurement errors, leading to uncertainty in the fitted model parameters. Current methods for estimating the prediction uncertainty of nonlinear regression models are often either computationally intensive or biased. In this study, we use sparse cubature formulas to estimate the prediction uncertainty of nonlinear regression models. Our findings indicate that this method provides a favorable balance between accuracy and computational efficiency, making it suitable for application in chemical engineering. We validate the performance of our proposed method through various regression case studies, including both theoretical toy models and practical models from chemical engineering.
{"title":"Cubature-based uncertainty estimation for nonlinear regression models","authors":"Martin Bubel , Jochen Schmid , Maximilian Carmesin , Volodymyr Kozachynskyi , Erik Esche , Michael Bortz","doi":"10.1016/j.compchemeng.2025.109035","DOIUrl":"10.1016/j.compchemeng.2025.109035","url":null,"abstract":"<div><div>Models are commonly utilized in chemical engineering to simulate real-world processes and phenomena. Given their role in guiding decision-making, accurately quantifying the uncertainty of these models is essential. Typically, these models are calibrated using experimental data that contain measurement errors, leading to uncertainty in the fitted model parameters. Current methods for estimating the prediction uncertainty of nonlinear regression models are often either computationally intensive or biased. In this study, we use sparse cubature formulas to estimate the prediction uncertainty of nonlinear regression models. Our findings indicate that this method provides a favorable balance between accuracy and computational efficiency, making it suitable for application in chemical engineering. We validate the performance of our proposed method through various regression case studies, including both theoretical toy models and practical models from chemical engineering.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"197 ","pages":"Article 109035"},"PeriodicalIF":3.9,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592108","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 : 2025-02-16DOI: 10.1016/j.compchemeng.2025.109065
Ting Wu , Peilin Zhan , Wei Chen , Miaoqing Lin , Quanyuan Qiu , Yinan Hu , Jiuhang Song , Xiaoqing Lin
Deep eutectic solvents (DESs) are sustainable alternatives to traditional solvents, but their structural complexity makes accurate prediction of melting points and densities challenging. This study utilizes ChemBERTa, a pre-trained Transformer model, to extract high-dimensional embeddings from Simplified Molecular Input Line Entry System (SMILES) strings, effectively capturing complex molecular interactions and subtle structural features. Through feature importance analysis, we identified missing molecular information in the ChemBERTa embeddings and supplemented it with select physicochemical descriptors from RDKit, creating a feature set that enhances both interpretability and predictive accuracy. Optimized ensemble models, including ExtraTreesRegressor (ETR) and XGBRegressor (XGBR), are then applied to this enriched feature set, achieving notable improvements in prediction accuracy for DES melting point and density. Rigorous grid search and ten-fold cross-validation ensure model robustness and generalizability. Experimental results confirm the effectiveness of this approach, underscoring the transformative role of pre-trained deep learning models in chemical informatics and supporting scalable, sustainable DESs design.
{"title":"ChemBERTa embeddings and ensemble learning for prediction of density and melting point of deep eutectic solvents with hybrid features","authors":"Ting Wu , Peilin Zhan , Wei Chen , Miaoqing Lin , Quanyuan Qiu , Yinan Hu , Jiuhang Song , Xiaoqing Lin","doi":"10.1016/j.compchemeng.2025.109065","DOIUrl":"10.1016/j.compchemeng.2025.109065","url":null,"abstract":"<div><div>Deep eutectic solvents (DESs) are sustainable alternatives to traditional solvents, but their structural complexity makes accurate prediction of melting points and densities challenging. This study utilizes ChemBERTa, a pre-trained Transformer model, to extract high-dimensional embeddings from Simplified Molecular Input Line Entry System (SMILES) strings, effectively capturing complex molecular interactions and subtle structural features. Through feature importance analysis, we identified missing molecular information in the ChemBERTa embeddings and supplemented it with select physicochemical descriptors from RDKit, creating a feature set that enhances both interpretability and predictive accuracy. Optimized ensemble models, including ExtraTreesRegressor (ETR) and XGBRegressor (XGBR), are then applied to this enriched feature set, achieving notable improvements in prediction accuracy for DES melting point and density. Rigorous grid search and ten-fold cross-validation ensure model robustness and generalizability. Experimental results confirm the effectiveness of this approach, underscoring the transformative role of pre-trained deep learning models in chemical informatics and supporting scalable, sustainable DESs design.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"196 ","pages":"Article 109065"},"PeriodicalIF":3.9,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429981","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 : 2025-02-16DOI: 10.1016/j.compchemeng.2025.109064
Michael Baldea , Apostolos T. Georgiou , Bhushan Gopaluni , Mehmet Mercangöz , Constantinos C. Pantelides , Kiran Sheth , Victor M. Zavala , Christos Georgakis
This paper considers current trends towards a higher degree of automation of process operations. Often referred to as “autonomous” process operations, these developments involve cyber-physical systems that can automate tasks that have hitherto relied extensively on human plant operators and, in particular, on their accurate assessment of the current plant situation based on a multitude of information sources, and on their ability to devise and implement plans of actions for dealing with often novel situations. The paper analyses the main drivers behind the need for a higher level of automation in process operations, and reviews the industrial applications that have been described in the public domain to date. It also presents a review of advances and potential impact of some of the enabling technologies for autonomy; these include sensors, mathematical modelling abstractions, reinforcement learning, knowledge graphs, and large language models.
{"title":"From automated to autonomous process operations","authors":"Michael Baldea , Apostolos T. Georgiou , Bhushan Gopaluni , Mehmet Mercangöz , Constantinos C. Pantelides , Kiran Sheth , Victor M. Zavala , Christos Georgakis","doi":"10.1016/j.compchemeng.2025.109064","DOIUrl":"10.1016/j.compchemeng.2025.109064","url":null,"abstract":"<div><div>This paper considers current trends towards a higher degree of automation of process operations. Often referred to as “autonomous” process operations, these developments involve cyber-physical systems that can automate tasks that have hitherto relied extensively on human plant operators and, in particular, on their accurate assessment of the current plant situation based on a multitude of information sources, and on their ability to devise and implement plans of actions for dealing with often novel situations. The paper analyses the main drivers behind the need for a higher level of automation in process operations, and reviews the industrial applications that have been described in the public domain to date. It also presents a review of advances and potential impact of some of the enabling technologies for autonomy; these include sensors, mathematical modelling abstractions, reinforcement learning, knowledge graphs, and large language models.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"196 ","pages":"Article 109064"},"PeriodicalIF":3.9,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143452982","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 : 2025-02-15DOI: 10.1016/j.compchemeng.2025.109042
Felix Birkelbach
For including piecewise linear (PWL) functions in MILP problems, the logarithmic convex combination (Log) formulation has been shown to yield very fast solving times. However, identifying approximations that can be used with Log is a big challenge since the approximation has to be compatible with a J1 triangulation. In this article, an algorithm is proposed that identifies approximations using J1 compatible triangulations. It seeks to satisfy the specified error tolerance with the minimum number of linear pieces, so that the MILP formulation is small. To evaluate the performance of the J1 approach it is applied to two sets of benchmark functions from literature and results are compared to state-of-the-art approaches.
Overall the J1 approach is shown to efficiently approximate functions in up to 3 dimensions. Especially for tight error tolerances, these J1 approximations require fewer auxiliary variables in MILP compared to alternative approaches.
{"title":"Piecewise linear approximation using J1 compatible triangulations for efficient MILP representation","authors":"Felix Birkelbach","doi":"10.1016/j.compchemeng.2025.109042","DOIUrl":"10.1016/j.compchemeng.2025.109042","url":null,"abstract":"<div><div>For including piecewise linear (PWL) functions in MILP problems, the logarithmic convex combination (Log) formulation has been shown to yield very fast solving times. However, identifying approximations that can be used with Log is a big challenge since the approximation has to be compatible with a J1 triangulation. In this article, an algorithm is proposed that identifies approximations using J1 compatible triangulations. It seeks to satisfy the specified error tolerance with the minimum number of linear pieces, so that the MILP formulation is small. To evaluate the performance of the J1 approach it is applied to two sets of benchmark functions from literature and results are compared to state-of-the-art approaches.</div><div>Overall the J1 approach is shown to efficiently approximate functions in up to 3 dimensions. Especially for tight error tolerances, these J1 approximations require fewer auxiliary variables in MILP compared to alternative approaches.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"196 ","pages":"Article 109042"},"PeriodicalIF":3.9,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429979","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 : 2025-02-15DOI: 10.1016/j.compchemeng.2025.109037
Ashley Dan , Urjit Patil , Abhinav De , Bhavani Nandhini Mummidi Manuraj , Rohit Ramachandran
The development of mathematical models for physical systems often necessitates the use of high-dimensional spaces and fine discretizations to accurately capture complex dynamics. These models, which involve large matrices and extensive mathematical operations, tend to be computationally intensive, leading to slow execution times. In this study, we analyzed various acceleration strategies by comparing the simulation accuracy, computational time, and resource utilization of various vectorization and parallelization methods on both CPUs and GPUs, using a multi-dimensional Population Balance Model simulated in MATLAB and Python. Our findings revealed that GPU-based vectorization provided the highest performance, achieving a 40-fold speedup compared to the serial implementations. Unlike simulations on CPUs, where run time is often limited by processing power, GPUs simulations are limited by the available memory, especially at high resolution. This work highlights the importance of using appropriate resources and code optimization strategies to reduce computational time, for development of an efficient model.
{"title":"CPU and GPU based acceleration of high-dimensional population balance models via the vectorization and parallelization of multivariate aggregation and breakage integral terms","authors":"Ashley Dan , Urjit Patil , Abhinav De , Bhavani Nandhini Mummidi Manuraj , Rohit Ramachandran","doi":"10.1016/j.compchemeng.2025.109037","DOIUrl":"10.1016/j.compchemeng.2025.109037","url":null,"abstract":"<div><div>The development of mathematical models for physical systems often necessitates the use of high-dimensional spaces and fine discretizations to accurately capture complex dynamics. These models, which involve large matrices and extensive mathematical operations, tend to be computationally intensive, leading to slow execution times. In this study, we analyzed various acceleration strategies by comparing the simulation accuracy, computational time, and resource utilization of various vectorization and parallelization methods on both CPUs and GPUs, using a multi-dimensional Population Balance Model simulated in MATLAB and Python. Our findings revealed that GPU-based vectorization provided the highest performance, achieving a 40-fold speedup compared to the serial implementations. Unlike simulations on CPUs, where run time is often limited by processing power, GPUs simulations are limited by the available memory, especially at high resolution. This work highlights the importance of using appropriate resources and code optimization strategies to reduce computational time, for development of an efficient model.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"196 ","pages":"Article 109037"},"PeriodicalIF":3.9,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422192","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 : 2025-02-15DOI: 10.1016/j.compchemeng.2025.109040
Johannes Leipold , Daliborka Nikolic , Andreas Seidel-Morgenstern , Achim Kienle
Methanol synthesis with a conventional Cu/ZnO/AlO-catalyst is typically carried out under stationary conditions. However, due to the process non-linearities, dynamic operation may improve the reactor performance. This paper numerically investigates such a potential of improvement through forced periodic operation of methanol synthesis in a non-isothermal lab-scale fixed-bed reactor. A multi-objective optimization is performed in which both the molar flow rate of methanol and the yield of methanol based on the used carbon molecules are considered as objective functions. The best possible steady state operation is then compared with the best possible periodic operation to evaluate the full potential of improvement. Focus is on periodic forcing of two inputs with same forcing frequency but different phase. Several possible input combinations are considered systematically. In particular the possibility of inlet and/or cooling temperature modulation is explored and compared. The results demonstrate a significant improvement for several input combinations through forced periodic operation.
{"title":"Optimization of methanol synthesis under forced periodic operation in a non-isothermal fixed-bed reactor","authors":"Johannes Leipold , Daliborka Nikolic , Andreas Seidel-Morgenstern , Achim Kienle","doi":"10.1016/j.compchemeng.2025.109040","DOIUrl":"10.1016/j.compchemeng.2025.109040","url":null,"abstract":"<div><div>Methanol synthesis with a conventional Cu/ZnO/Al<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>O<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span>-catalyst is typically carried out under stationary conditions. However, due to the process non-linearities, dynamic operation may improve the reactor performance. This paper numerically investigates such a potential of improvement through forced periodic operation of methanol synthesis in a non-isothermal lab-scale fixed-bed reactor. A multi-objective optimization is performed in which both the molar flow rate of methanol and the yield of methanol based on the used carbon molecules are considered as objective functions. The best possible steady state operation is then compared with the best possible periodic operation to evaluate the full potential of improvement. Focus is on periodic forcing of two inputs with same forcing frequency but different phase. Several possible input combinations are considered systematically. In particular the possibility of inlet and/or cooling temperature modulation is explored and compared. The results demonstrate a significant improvement for several input combinations through forced periodic operation.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"196 ","pages":"Article 109040"},"PeriodicalIF":3.9,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143452983","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 : 2025-02-15DOI: 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}
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