Pub Date : 2025-03-01Epub Date: 2024-11-30DOI: 10.1016/j.dche.2024.100201
Sergio M.S. Neiro , Tarun Madan , Christos T. Maravelias , José M. Pinto
In this work, we address a nationwide tactical planning for industrial gas supply chains, particularly argon. The proposed approaches follow as extensions of our previous work (Comp. & Chem. Eng., 161 (2022) 107778) in which a regional argon supply chain problem is addressed; in that work, both production and distribution could be represented in detail. Two different types of deliveries from the Air Separating Units (ASU) to customers, which involve single driver deliveries for short distance trips and sleeper team that require multiple days. The nationwide problem requires simplifications to keep the problem mathematically tractable, primarily the representation of production sites with different tier costs and the aggregation of customers in clusters. The regional problem addressed in our previous work is used as a benchmark case study for benchmarking. We then focus on a real-world problem that represents a nationwide argon supply chain. Despite the size of the models, near optimal solutions could be found in reasonable times. Finally, we highlight important features of the proposed approaches.
{"title":"A nationwide planning model for argon supply chains with coordinated production and distribution","authors":"Sergio M.S. Neiro , Tarun Madan , Christos T. Maravelias , José M. Pinto","doi":"10.1016/j.dche.2024.100201","DOIUrl":"10.1016/j.dche.2024.100201","url":null,"abstract":"<div><div>In this work, we address a nationwide tactical planning for industrial gas supply chains, particularly argon. The proposed approaches follow as extensions of our previous work (<em>Comp. & Chem. Eng., 161 (2022) 107778</em>) in which a regional argon supply chain problem is addressed; in that work, both production and distribution could be represented in detail. Two different types of deliveries from the Air Separating Units (ASU) to customers, which involve single driver deliveries for short distance trips and sleeper team that require multiple days. The nationwide problem requires simplifications to keep the problem mathematically tractable, primarily the representation of production sites with different tier costs and the aggregation of customers in clusters. The regional problem addressed in our previous work is used as a benchmark case study for benchmarking. We then focus on a real-world problem that represents a nationwide argon supply chain. Despite the size of the models, near optimal solutions could be found in reasonable times. Finally, we highlight important features of the proposed approaches.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100201"},"PeriodicalIF":3.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01Epub Date: 2024-12-04DOI: 10.1016/j.dche.2024.100205
Kaleem Ullah , Sara Maen Asaad , Abrar Inayat
Hydrogen production from biogas presents a significant opportunity to address major sustainability challenges by providing an economically viable replacement of fossil fuels and reducing greenhouse gas emissions. However, the conversion of biogas into hydrogen using steam reforming is affected by several process parameters. Therefore, this study aims to use a combined approach of DWSIM chemical process simulator and response surface methodology (RSM) as an optimization technique to enhance the effectiveness of the hydrogen production process. The process was modeled with the help of DWSIM software and then validated. Additionally, sensitivity analysis was performed to assess the impact of varying raw material flow rates and reactor conditions on the hydrogen yield as well as investigate the effect of varying biogas composition on the hydrogen yield. Design Expert software was used to optimize the hydrogen production using the Central composite design and a quadratic model. Four input parameters were considered: biogas flow rate, steam flow rate, inlet temperature, and pressure of reformer reactor, with hydrogen yield at the outlet of the last reactor considered as the response. The model and the independent parameters were found to be significant with p-values< 0.0001. The interactions of parameters showed that pressure had the least impact on the hydrogen yield. The optimal parameters identified were 57 kg/hr biogas flow rate, 33.97 kg/hr steam flow rate, 954.38 °C reformer inlet temperature, and 12.52 bar pressure, ultimately achieving a maximum hydrogen yield of 65.992 %. Validation of optimal conditions in DWSIM simulation tool yielded a hydrogen yield of 64.874 % with an error margin of <2.0 %. Overall, this study demonstrates the effect of each parameter and optimizes the hydrogen production process to increase the yield.
{"title":"Process modelling and optimization of hydrogen production from biogas by integrating DWSIM with response surface methodology","authors":"Kaleem Ullah , Sara Maen Asaad , Abrar Inayat","doi":"10.1016/j.dche.2024.100205","DOIUrl":"10.1016/j.dche.2024.100205","url":null,"abstract":"<div><div>Hydrogen production from biogas presents a significant opportunity to address major sustainability challenges by providing an economically viable replacement of fossil fuels and reducing greenhouse gas emissions. However, the conversion of biogas into hydrogen using steam reforming is affected by several process parameters. Therefore, this study aims to use a combined approach of DWSIM chemical process simulator and response surface methodology (RSM) as an optimization technique to enhance the effectiveness of the hydrogen production process. The process was modeled with the help of DWSIM software and then validated. Additionally, sensitivity analysis was performed to assess the impact of varying raw material flow rates and reactor conditions on the hydrogen yield as well as investigate the effect of varying biogas composition on the hydrogen yield. Design Expert software was used to optimize the hydrogen production using the Central composite design and a quadratic model. Four input parameters were considered: biogas flow rate, steam flow rate, inlet temperature, and pressure of reformer reactor, with hydrogen yield at the outlet of the last reactor considered as the response. The model and the independent parameters were found to be significant with p-values< 0.0001. The interactions of parameters showed that pressure had the least impact on the hydrogen yield. The optimal parameters identified were 57 kg/hr biogas flow rate, 33.97 kg/hr steam flow rate, 954.38 °C reformer inlet temperature, and 12.52 bar pressure, ultimately achieving a maximum hydrogen yield of 65.992 %. Validation of optimal conditions in DWSIM simulation tool yielded a hydrogen yield of 64.874 % with an error margin of <2.0 %. Overall, this study demonstrates the effect of each parameter and optimizes the hydrogen production process to increase the yield.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100205"},"PeriodicalIF":3.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01Epub Date: 2024-12-19DOI: 10.1016/j.dche.2024.100209
Jesse Y. Rumbo-Morales , Gerardo Ortiz-Torres , Felipe D.J. Sorcia-Vázquez , Carlos Alberto Torres-Cantero , Jair Gómez Radilla , Mario Martínez García , Julio César Rodríguez-Cerda , Antonio Márquez Rosales , Moises Ramos-Martinez , Juan Carlos Mixteco-Sánchez , Mayra G. Mena-Enriquez , Mario A. Juarez
Biofuels produced from renewable raw materials, in this case bioethanol, provide a sustainable and renewable energy source for the future, as bioethanol positively impacts the economy, the environment, and society. Bioethanol is an alternative and immediate solution to mitigate the main greenhouse gases generated by transportation and industries that use fossil fuels. However, to produce bioethanol it is necessary to use advanced dehydration processes or technologies. Currently, azeotropic distillation, extractive distillation, and the Pressure Swing Adsorption (PSA) process using selective zeolites on water molecules are used. This PSA process has shown high selectivity, high yield, and high energy efficiency for producing anhydrous ethanol compared to other technologies. This work aims to implement automatic control laws (geometric and PID) to maintain stable the desired purity (99.5%), have higher bioethanol recovery and generate higher productivity using less energy. Both controllers performed adequately on the PSA bioethanol-producing plant, however, the geometric control presented greater robustness against disturbances, achieving to maintain stable bioethanol purity above 99% by wt, generating a recovery of 73.62%, with productivity of 59.07 and using an energy efficiency of 59.21%. Using this control law, it was possible to use the entire length of the columns to adsorb a greater amount of water molecules and achieve higher production.
{"title":"Energy efficiency and productivity of a Pressure Swing Adsorption plant to purify bioethanol: Disturbance attenuation through geometric control","authors":"Jesse Y. Rumbo-Morales , Gerardo Ortiz-Torres , Felipe D.J. Sorcia-Vázquez , Carlos Alberto Torres-Cantero , Jair Gómez Radilla , Mario Martínez García , Julio César Rodríguez-Cerda , Antonio Márquez Rosales , Moises Ramos-Martinez , Juan Carlos Mixteco-Sánchez , Mayra G. Mena-Enriquez , Mario A. Juarez","doi":"10.1016/j.dche.2024.100209","DOIUrl":"10.1016/j.dche.2024.100209","url":null,"abstract":"<div><div>Biofuels produced from renewable raw materials, in this case bioethanol, provide a sustainable and renewable energy source for the future, as bioethanol positively impacts the economy, the environment, and society. Bioethanol is an alternative and immediate solution to mitigate the main greenhouse gases generated by transportation and industries that use fossil fuels. However, to produce bioethanol it is necessary to use advanced dehydration processes or technologies. Currently, azeotropic distillation, extractive distillation, and the Pressure Swing Adsorption (PSA) process using selective zeolites on water molecules are used. This PSA process has shown high selectivity, high yield, and high energy efficiency for producing anhydrous ethanol compared to other technologies. This work aims to implement automatic control laws (geometric and PID) to maintain stable the desired purity (99.5%), have higher bioethanol recovery and generate higher productivity using less energy. Both controllers performed adequately on the PSA bioethanol-producing plant, however, the geometric control presented greater robustness against disturbances, achieving to maintain stable bioethanol purity above 99% by wt, generating a recovery of 73.62%, with productivity of 59.07 <span><math><mrow><mi>k</mi><mi>m</mi><mi>o</mi><mi>l</mi></mrow></math></span> and using an energy efficiency of 59.21%. Using this control law, it was possible to use the entire length of the columns to adsorb a greater amount of water molecules and achieve higher production.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100209"},"PeriodicalIF":3.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the oil industry, gas lift is essential for facilitating fluid flow toward the production unit. However, the challenge lies in balancing gas availability constraints to achieve maximum efficiency in an oil field. This study utilizes the integrated production modeling (IPM) software to simulate an oil field operation in Iran. To this end, 154 data points constructed by a central composite design (CCD) experiment were utilized to develop neural network models. Therefore, four robust models, including multilayer perceptron (MLP), radial basis function (RBF), general regression neural network (GRNN), and cascade forward neural network (CFNN), were implemented for modeling. In addition, the net present value (NPV) serves as the objective function. To optimize the selected input variables, including tubing inside diameter, gas injection rate, and separator pressure, various optimization algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO), genetic algorithm (GA), and a Novel optimization algorithm in a gas-lift study called grey wolf optimization (GWO), were utilized considering the constraint of the limited available gas. A penalty function was used to incorporate this constraint into the optimization procedure. There has previously been much research in the area of gas lift optimization. However, robust neural networks (GRNN and CFNN) have not been used for integrated production system modeling, nor have GWO algorithms been used to maximize the production or NPV in gas lift operations until now. The results for model errors were found to be %2.09, %2.99, %10.68, and %1.75 for MLP, RBF, GRNN, and CFNN, respectively. These findings imply that the CFNN model is more efficient. Also, comparing the GWO approach to other algorithms, the largest NPV ($788,512,038$) was yielded with less sensitivity of its adjustable parameters. Thereupon, NPV and cumulated oil production indicate a significant increase compared to ordinary NPV and oil production with values of 351,087,876.4 $ and 14,308 STB, respectively. High NPV effectively captures the overall added value of the project and, as a benchmark, helps to make informed decisions about investment and resource allocation, ultimately driving economic growth and increasing competitiveness in using this method.
{"title":"Integration of artificial intelligence and advanced optimization techniques for continuous gas lift under restricted gas supply: A case study","authors":"Leila Zeinolabedini , Forough Ameli , Abdolhossein Hemmati-Sarapardeh","doi":"10.1016/j.dche.2025.100220","DOIUrl":"10.1016/j.dche.2025.100220","url":null,"abstract":"<div><div>In the oil industry, gas lift is essential for facilitating fluid flow toward the production unit. However, the challenge lies in balancing gas availability constraints to achieve maximum efficiency in an oil field. This study utilizes the integrated production modeling (IPM) software to simulate an oil field operation in Iran. To this end, 154 data points constructed by a central composite design (CCD) experiment were utilized to develop neural network models. Therefore, four robust models, including multilayer perceptron (MLP), radial basis function (RBF), general regression neural network (GRNN), and cascade forward neural network (CFNN), were implemented for modeling. In addition, the net present value (NPV) serves as the objective function. To optimize the selected input variables, including tubing inside diameter, gas injection rate, and separator pressure, various optimization algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO), genetic algorithm (GA), and a Novel optimization algorithm in a gas-lift study called grey wolf optimization (GWO), were utilized considering the constraint of the limited available gas. A penalty function was used to incorporate this constraint into the optimization procedure. There has previously been much research in the area of gas lift optimization. However, robust neural networks (GRNN and CFNN) have not been used for integrated production system modeling, nor have GWO algorithms been used to maximize the production or NPV in gas lift operations until now. The results for model errors were found to be %2.09, %2.99, %10.68, and %1.75 for MLP, RBF, GRNN, and CFNN, respectively. These findings imply that the CFNN model is more efficient. Also, comparing the GWO approach to other algorithms, the largest NPV ($788,512,038$) was yielded with less sensitivity of its adjustable parameters. Thereupon, NPV and cumulated oil production indicate a significant increase compared to ordinary NPV and oil production with values of 351,087,876.4 $ and 14,308 STB, respectively. High NPV effectively captures the overall added value of the project and, as a benchmark, helps to make informed decisions about investment and resource allocation, ultimately driving economic growth and increasing competitiveness in using this method.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100220"},"PeriodicalIF":3.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143198410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01Epub Date: 2025-01-16DOI: 10.1016/j.dche.2025.100219
Xiaojie Li , Mingxue Yan , Xuewen Zhang , Minghao Han , Adrian Wing-Keung Law , Xunyuan Yin
Model predictive control (MPC) has become a key tool for optimizing real-time operations in industrial systems and processes, particularly to enhance performance, safety, and resilience. However, the growing complexity and nonlinearity of modern industrial systems present significant challenges for both first-principles modeling and real-time implementation of typical non-convex optimization associated with conventional MPC designs based on nonlinear models. In this review, we aim to provide an overview of current data-driven predictive control methods that have attributes of being computationally efficient as well as having the distinctive potential to address the above two challenges simultaneously. We focus particularly on two promising frameworks: (1) Koopman-based model predictive control, and (2) data-enabled predictive control, both of which are capable of formulating the optimization problem into a convex form even in the presence of strong nonlinearity in the underlying system. Additionally, we provide an outlook on the potential applications of these methods and briefly discuss their future directions across various industrial sectors.
{"title":"Efficient data-driven predictive control of nonlinear systems: A review and perspectives","authors":"Xiaojie Li , Mingxue Yan , Xuewen Zhang , Minghao Han , Adrian Wing-Keung Law , Xunyuan Yin","doi":"10.1016/j.dche.2025.100219","DOIUrl":"10.1016/j.dche.2025.100219","url":null,"abstract":"<div><div>Model predictive control (MPC) has become a key tool for optimizing real-time operations in industrial systems and processes, particularly to enhance performance, safety, and resilience. However, the growing complexity and nonlinearity of modern industrial systems present significant challenges for both first-principles modeling and real-time implementation of typical non-convex optimization associated with conventional MPC designs based on nonlinear models. In this review, we aim to provide an overview of current data-driven predictive control methods that have attributes of being computationally efficient as well as having the distinctive potential to address the above two challenges simultaneously. We focus particularly on two promising frameworks: (1) Koopman-based model predictive control, and (2) data-enabled predictive control, both of which are capable of formulating the optimization problem into a convex form even in the presence of strong nonlinearity in the underlying system. Additionally, we provide an outlook on the potential applications of these methods and briefly discuss their future directions across various industrial sectors.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100219"},"PeriodicalIF":3.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01Epub Date: 2025-01-02DOI: 10.1016/j.dche.2024.100216
Yiming Lang , Michelle Xin Yi Ng , Kai Xiang Yu , Binghui Chen , Peng Chee Tan , Khang Wei Tan , Weng Hoong Lam , Parthiban Siwayanan , Kek Seong Kim , Thomas Shean Yaw Choong , Joon Yoon Ten , Zhen Hong Ban
Illegal practices like open electronic waste incineration release hazardous pollutants, endangering the environment and human health. The Internet of Things (IoT) enables online real-time gas concentrations, but its capability to predict leak sources accurately remains a challenge. A large amount of historical data is required to train the source localization model, as gas dispersion is affected by wind speed and wind direction. Furthermore, sensor placement critically affects precise detection and prediction. This study introduces an innovative approach integrating Computational Fluid Dynamics (CFD), Mixed-Integer Linear Programming (MILP), and Artificial Neural Network modeling (ANN). CFD was utilized for machine learning model training. The MILP was used to optimize sensor placement, while the ANN model was used to optimize sensor number. The source localization model was again realized by the ANN model with optimized sensors data. The trained model was able to identify the unknown illegal electronic waste treatment locations with 97.22 % accuracy in this study. This method allows for the rapid detection of gas sources, as well as the execution of an emergency response, in line with Sustainable Development Goal Target 3.9.
{"title":"A novel CFD-MILP-ANN approach for optimizing sensor placement, number, and source localization in large-scale gas dispersion from unknown locations","authors":"Yiming Lang , Michelle Xin Yi Ng , Kai Xiang Yu , Binghui Chen , Peng Chee Tan , Khang Wei Tan , Weng Hoong Lam , Parthiban Siwayanan , Kek Seong Kim , Thomas Shean Yaw Choong , Joon Yoon Ten , Zhen Hong Ban","doi":"10.1016/j.dche.2024.100216","DOIUrl":"10.1016/j.dche.2024.100216","url":null,"abstract":"<div><div>Illegal practices like open electronic waste incineration release hazardous pollutants, endangering the environment and human health. The Internet of Things (IoT) enables online real-time gas concentrations, but its capability to predict leak sources accurately remains a challenge. A large amount of historical data is required to train the source localization model, as gas dispersion is affected by wind speed and wind direction. Furthermore, sensor placement critically affects precise detection and prediction. This study introduces an innovative approach integrating Computational Fluid Dynamics (CFD), Mixed-Integer Linear Programming (MILP), and Artificial Neural Network modeling (ANN). CFD was utilized for machine learning model training. The MILP was used to optimize sensor placement, while the ANN model was used to optimize sensor number. The source localization model was again realized by the ANN model with optimized sensors data. The trained model was able to identify the unknown illegal electronic waste treatment locations with 97.22 % accuracy in this study. This method allows for the rapid detection of gas sources, as well as the execution of an emergency response, in line with Sustainable Development Goal Target 3.9.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100216"},"PeriodicalIF":3.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01Epub Date: 2025-01-17DOI: 10.1016/j.dche.2025.100218
Hasan Nikkhah , Zahir Aghayev , Amir Shahbazi , Vassilis M. Charitopoulos , Styliani Avraamidou , Burcu Beykal
Planning and scheduling are crucial components of enterprise-wide optimization (EWO). For the successful execution of EWO, it is vital to view the enterprise operations as a holistic decision-making problem, composed of different interconnected elements or layers, to make the most efficient use of resources in process industries. Among different layers of the operating decisions, planning and scheduling are often treated sequentially, leading to impractical solutions. To tackle this problem, integrated approaches, such as bi-level programming are utilized to optimize these two layers simultaneously. Nonetheless, the bi-level optimization of such interdependent and holistic formulations is still difficult, particularly when dealing with mixed-integer nonlinear programming (MINLP) problems, due to a lack of effective algorithms. In this study, we employ the Data-driven Optimization of bi-level Mixed-Integer NOnlinear problems (DOMINO) framework, a data-driven algorithm developed to handle single-leader single-follower bi-level mixed-integer problems, to solve single-leader multi-follower planning and scheduling problems subject to MINLP scheduling formulations. We apply DOMINO to the continuous production of multi-product methyl methacrylate polymerization process formulated as a Traveling Salesman Problem and demonstrate its capability in achieving near-optimal guaranteed feasible solutions. Building on this foundation, we extend this strategy to solve a high-dimensional and highly constrained nonlinear crude oil refinery operation problem that has not been previously tackled in this context. Our study further evaluates the efficacy of using local, NOMAD (Nonlinear Optimization by Mesh Adaptive Direct Search), and a global data-driven optimizer, ARGONAUT (AlgoRithms for Global Optimization of coNstrAined grey-box compUTational), within the DOMINO framework and characterize their performance both in terms of solution quality and computational expense. The results indicate that DOMINO-NOMAD consistently achieves superior performance compared to DOMINO-ARGONAUT by identifying lower planning costs and generating more feasible solutions across multiple runs. Overall, this study demonstrates DOMINO’s ability to optimize production targets, meet market demands, and address large-scale EWO problems.
计划和调度是企业范围优化（EWO）的关键组成部分。为了成功地实施EWO，至关重要的是将企业运营视为一个整体决策问题，由不同的相互关联的要素或层次组成，以最有效地利用过程工业中的资源。在操作决策的不同层次中，计划和调度通常是顺序处理的，从而导致不切实际的解决方案。为了解决这一问题，利用双层编程等综合方法同时优化这两层。然而，由于缺乏有效的算法，这种相互依赖的整体公式的双层优化仍然很困难，特别是在处理混合整数非线性规划（MINLP）问题时。本文采用数据驱动优化双级混合整数非线性问题（Data-driven Optimization of bi-level Mixed-Integer NOnlinear problems， DOMINO）框架来解决单领导者单追随者双级混合整数问题，这是一种用于处理单领导者单追随者双级别混合整数问题的数据驱动算法。我们将多米诺应用于多产品甲基丙烯酸甲酯聚合过程的连续生产中，该过程被表述为一个旅行推销员问题，并证明了它在获得近最优保证可行解决方案方面的能力。在此基础上，我们将该策略扩展到解决高维、高约束的非线性原油炼油厂运行问题，这是以前在此背景下尚未解决的问题。我们的研究进一步评估了在DOMINO框架中使用局部NOMAD（通过网格自适应直接搜索的非线性优化）和全局数据驱动优化器ARGONAUT（约束灰盒计算的全局优化算法）的效果，并从解决方案质量和计算费用两方面描述了它们的性能。结果表明，与DOMINO-ARGONAUT相比，DOMINO-NOMAD通过确定更低的规划成本并在多次运行中生成更可行的解决方案，始终具有更优越的性能。总体而言，本研究证明了DOMINO优化生产目标、满足市场需求和解决大规模EWO问题的能力。
{"title":"Bi-level data-driven enterprise-wide optimization with mixed-integer nonlinear scheduling problems","authors":"Hasan Nikkhah , Zahir Aghayev , Amir Shahbazi , Vassilis M. Charitopoulos , Styliani Avraamidou , Burcu Beykal","doi":"10.1016/j.dche.2025.100218","DOIUrl":"10.1016/j.dche.2025.100218","url":null,"abstract":"<div><div>Planning and scheduling are crucial components of enterprise-wide optimization (EWO). For the successful execution of EWO, it is vital to view the enterprise operations as a holistic decision-making problem, composed of different interconnected elements or layers, to make the most efficient use of resources in process industries. Among different layers of the operating decisions, planning and scheduling are often treated sequentially, leading to impractical solutions. To tackle this problem, integrated approaches, such as bi-level programming are utilized to optimize these two layers simultaneously. Nonetheless, the bi-level optimization of such interdependent and holistic formulations is still difficult, particularly when dealing with mixed-integer nonlinear programming (MINLP) problems, due to a lack of effective algorithms. In this study, we employ the Data-driven Optimization of bi-level Mixed-Integer NOnlinear problems (DOMINO) framework, a data-driven algorithm developed to handle single-leader single-follower bi-level mixed-integer problems, to solve single-leader multi-follower planning and scheduling problems subject to MINLP scheduling formulations. We apply DOMINO to the continuous production of multi-product methyl methacrylate polymerization process formulated as a Traveling Salesman Problem and demonstrate its capability in achieving near-optimal guaranteed feasible solutions. Building on this foundation, we extend this strategy to solve a high-dimensional and highly constrained nonlinear crude oil refinery operation problem that has not been previously tackled in this context. Our study further evaluates the efficacy of using local, NOMAD (Nonlinear Optimization by Mesh Adaptive Direct Search), and a global data-driven optimizer, ARGONAUT (AlgoRithms for Global Optimization of coNstrAined grey-box compUTational), within the DOMINO framework and characterize their performance both in terms of solution quality and computational expense. The results indicate that DOMINO-NOMAD consistently achieves superior performance compared to DOMINO-ARGONAUT by identifying lower planning costs and generating more feasible solutions across multiple runs. Overall, this study demonstrates DOMINO’s ability to optimize production targets, meet market demands, and address large-scale EWO problems.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100218"},"PeriodicalIF":3.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01Epub Date: 2024-12-20DOI: 10.1016/j.dche.2024.100200
Tanuj Karia, Gustavo Chaparro, Benoît Chachuat, Claire S. Adjiman
The ability to guarantee a single homogeneous liquid phase is a key consideration in computer-aided mixture/blend design (CAMbD). In this article, we investigate the use of a classifier surrogate of the phase stability condition within a CAMbD optimisation model for designing solvent mixtures with guaranteed phase stability properties. We show how to develop such classifiers for describing multiple candidate mixtures over a range of compositions and temperatures based on the generation of phase stability data using thermodynamic models such as UNIFAC. We test the approach on two solvent design case studies and illustrate its effectiveness in enabling the in silico design of stable mixtures, simultaneously providing a probability of phase stability as an interpretable metric.
{"title":"Classifier surrogates to ensure phase stability in optimisation-based design of solvent mixtures","authors":"Tanuj Karia, Gustavo Chaparro, Benoît Chachuat, Claire S. Adjiman","doi":"10.1016/j.dche.2024.100200","DOIUrl":"10.1016/j.dche.2024.100200","url":null,"abstract":"<div><div>The ability to guarantee a single homogeneous liquid phase is a key consideration in computer-aided mixture/blend design (CAM<sup>b</sup>D). In this article, we investigate the use of a classifier surrogate of the phase stability condition within a CAM<sup>b</sup>D optimisation model for designing solvent mixtures with guaranteed phase stability properties. We show how to develop such classifiers for describing multiple candidate mixtures over a range of compositions and temperatures based on the generation of phase stability data using thermodynamic models such as UNIFAC. We test the approach on two solvent design case studies and illustrate its effectiveness in enabling the <em>in silico</em> design of stable mixtures, simultaneously providing a probability of phase stability as an interpretable metric.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100200"},"PeriodicalIF":3.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01Epub Date: 2024-12-21DOI: 10.1016/j.dche.2024.100215
Foteini Michalopoulou , Maria M. Papathanasiou
Chromatographic separation process models are described by nonlinear partial differential and algebraic equations, often leading to high computational cost that limits their applicability in real-time applications. To address this, in this work we propose a hybrid modelling approach that integrates artificial neural networks with process knowledge to describe the system nonlinear dynamics. Specifically, the separation isotherm is maintained in its mechanistic form, while the need for spatial discretisation is eliminated, reducing computational effort by 97 % in the open-loop simulation. The resulting hybrid model relies solely on experimentally measurable variables and performs well both in interpolation and extrapolation tests. It is further utilised within a process optimisation framework, for the maximisation of process yield and product purity. The results demonstrate that the hybrid model accurately captures the intricate dynamics of chromatographic separations while providing a computationally efficient alternative, making it an effective tool for development in industrial applications.
{"title":"An approach to hybrid modelling in chromatographic separation processes","authors":"Foteini Michalopoulou , Maria M. Papathanasiou","doi":"10.1016/j.dche.2024.100215","DOIUrl":"10.1016/j.dche.2024.100215","url":null,"abstract":"<div><div>Chromatographic separation process models are described by nonlinear partial differential and algebraic equations, often leading to high computational cost that limits their applicability in real-time applications. To address this, in this work we propose a hybrid modelling approach that integrates artificial neural networks with process knowledge to describe the system nonlinear dynamics. Specifically, the separation isotherm is maintained in its mechanistic form, while the need for spatial discretisation is eliminated, reducing computational effort by 97 % in the open-loop simulation. The resulting hybrid model relies solely on experimentally measurable variables and performs well both in interpolation and extrapolation tests. It is further utilised within a process optimisation framework, for the maximisation of process yield and product purity. The results demonstrate that the hybrid model accurately captures the intricate dynamics of chromatographic separations while providing a computationally efficient alternative, making it an effective tool for development in industrial applications.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100215"},"PeriodicalIF":3.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143159672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01Epub Date: 2024-12-10DOI: 10.1016/j.dche.2024.100206
Henrik Wang , Feiyang Ou , Julius Suherman , Gerassimos Orkoulas , Panagiotis D. Christofides
Control methods for Atomic Layer Etching (ALE) processes are constantly evolving due to the increasing level of precision needed to manufacture next-gen semiconductor devices. This work presents a novel, real-time Endpoint-based (EP) control approach for an Al2O3 ALE process in a discrete feed reactor. The proposed method dynamically adjusts the process time of both ALE half-cycles to ensure an optimal process outcome. The EP controller uses a machine learning-based transformer to take in variable-length, time-series pressure profiles to identify when the ALE process is complete. However, this model requires a large amount of process data to ensure that it will perform well even when under a variety of kinetic and pressure disturbances that mimic common issues in a real-world manufacturing environment. Thus, this work uses a multiscale modeling method that integrates a macroscopic Computational Fluid Dynamics (CFD) and a mesoscopic kinetic Monte Carlo (kMC) simulation to generate process data and test the proposed controllers. After testing the performance of the EP controller on individual runs, various combinations of ex-situ Run-to-Run (R2R) and EP controllers are examined in order to determine the strongest control strategy in a manufacturing environment. The final results show that the EP controller is highly accurate when trained on conditions that are representative of its implementation environment. Compared to traditional EWMA controllers, it has significantly fewer misprocesses, which enhances the overall control performance and efficiency of the ALE process.
{"title":"Integration of on-line machine learning-based endpoint control and run-to-run control for an atomic layer etching process","authors":"Henrik Wang , Feiyang Ou , Julius Suherman , Gerassimos Orkoulas , Panagiotis D. Christofides","doi":"10.1016/j.dche.2024.100206","DOIUrl":"10.1016/j.dche.2024.100206","url":null,"abstract":"<div><div>Control methods for Atomic Layer Etching (ALE) processes are constantly evolving due to the increasing level of precision needed to manufacture next-gen semiconductor devices. This work presents a novel, real-time Endpoint-based (EP) control approach for an Al<sub>2</sub>O<sub>3</sub> ALE process in a discrete feed reactor. The proposed method dynamically adjusts the process time of both ALE half-cycles to ensure an optimal process outcome. The EP controller uses a machine learning-based transformer to take in variable-length, time-series pressure profiles to identify when the ALE process is complete. However, this model requires a large amount of process data to ensure that it will perform well even when under a variety of kinetic and pressure disturbances that mimic common issues in a real-world manufacturing environment. Thus, this work uses a multiscale modeling method that integrates a macroscopic Computational Fluid Dynamics (CFD) and a mesoscopic kinetic Monte Carlo (kMC) simulation to generate process data and test the proposed controllers. After testing the performance of the EP controller on individual runs, various combinations of ex-situ Run-to-Run (R2R) and EP controllers are examined in order to determine the strongest control strategy in a manufacturing environment. The final results show that the EP controller is highly accurate when trained on conditions that are representative of its implementation environment. Compared to traditional EWMA controllers, it has significantly fewer misprocesses, which enhances the overall control performance and efficiency of the ALE process.</div></div>","PeriodicalId":72815,"journal":{"name":"Digital Chemical Engineering","volume":"14 ","pages":"Article 100206"},"PeriodicalIF":3.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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