Pub Date : 2024-11-23DOI: 10.1016/j.compchemeng.2024.108936
Yanju Chen , Mengxuan Chen , Tianran Hu
In recent years, outbreaks of diseases have been prevalent, significantly impacting human’s work, life and social economy. Vaccination is widely seen as the most promising way to fight against most of the epidemics. However, building a sustainable-resilient vaccine cold chain network is a complex planning problem, which may face various challenges, such as low-temperature transportation and storage, uncertain environments, and waste management. To address these challenges, a distributionally robust vaccine cold chain network design model is established. Using Wasserstein ambiguity set to manage uncertainties, the Wasserstein distributionally robust optimization (WDRO) model can be transformed into a computationally tractable form. A case study on influenza vaccines in Clalit reveals that the proposed WDRO model can yield a robust solution, incurring a small robust price. Conservative decision makers can choose a slightly larger Wasserstein ambiguity set to enhance the supply chain resilience at the cost of reducing economic and environmental benefits.
{"title":"Designing a sustainable-resilient vaccine cold chain network in uncertain environments","authors":"Yanju Chen , Mengxuan Chen , Tianran Hu","doi":"10.1016/j.compchemeng.2024.108936","DOIUrl":"10.1016/j.compchemeng.2024.108936","url":null,"abstract":"<div><div>In recent years, outbreaks of diseases have been prevalent, significantly impacting human’s work, life and social economy. Vaccination is widely seen as the most promising way to fight against most of the epidemics. However, building a sustainable-resilient vaccine cold chain network is a complex planning problem, which may face various challenges, such as low-temperature transportation and storage, uncertain environments, and waste management. To address these challenges, a distributionally robust vaccine cold chain network design model is established. Using Wasserstein ambiguity set to manage uncertainties, the Wasserstein distributionally robust optimization (WDRO) model can be transformed into a computationally tractable form. A case study on influenza vaccines in Clalit reveals that the proposed WDRO model can yield a robust solution, incurring a small robust price. Conservative decision makers can choose a slightly larger Wasserstein ambiguity set to enhance the supply chain resilience at the cost of reducing economic and environmental benefits.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108936"},"PeriodicalIF":3.9,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747134","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 : 2024-11-22DOI: 10.1016/j.compchemeng.2024.108935
Ali Ghodba , Anne Richelle , Chris McCready , Luis Ricardez-Sandoval , Hector Budman
The study proposes a robust algorithm for batch-to-batch optimization in the presence of model-mismatch. Robustness is achieved by the implementation of the following features: i — the gradient correction step is modified to consider the gradients of the cost function and constraints at both final and intermediate points, ii — Economic Model Predictive Control is applied to mitigate the impact of unmeasured disturbances on the optimum, and iii — an optimal design of experiments is performed to expedite convergence. Significant improvements of the proposed algorithm in convergence to the process optimum and robustness to noise, unmeasured disturbances, and model error are demonstrated using a fed-batch fermentation for penicillin production.
{"title":"A robust batch-to-batch optimization framework for pharmaceutical applications","authors":"Ali Ghodba , Anne Richelle , Chris McCready , Luis Ricardez-Sandoval , Hector Budman","doi":"10.1016/j.compchemeng.2024.108935","DOIUrl":"10.1016/j.compchemeng.2024.108935","url":null,"abstract":"<div><div>The study proposes a robust algorithm for batch-to-batch optimization in the presence of model-mismatch. Robustness is achieved by the implementation of the following features: i — the gradient correction step is modified to consider the gradients of the cost function and constraints at both final and intermediate points, ii — Economic Model Predictive Control is applied to mitigate the impact of unmeasured disturbances on the optimum, and iii — an optimal design of experiments is performed to expedite convergence. Significant improvements of the proposed algorithm in convergence to the process optimum and robustness to noise, unmeasured disturbances, and model error are demonstrated using a fed-batch fermentation for penicillin production.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"193 ","pages":"Article 108935"},"PeriodicalIF":3.9,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743282","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 : 2024-11-22DOI: 10.1016/j.compchemeng.2024.108923
Om Prakash, Biao Huang
We consider a dynamic mode decomposition (DMD) based technique to identify data-driven reduced-order and full-order models and propose two approaches to update them in real-time. These updates are crucial for the models to adapt to the evolving process. The proposed approaches function by calculating the update of the singular value decomposition (SVD), which is the core operation in DMD. In particular, two approaches involving temporal updates and additive modifications are used to update the SVDs. Further, the equivalence of both approaches is proved under special rank conditions. Also, the computational costs involved in these approaches are discussed. The technique is well suited for adaptive process modeling that can be exploited for real-time process monitoring, estimation, control, and optimization. The efficacy of the proposed approach is demonstrated using a large-scale benchmark wastewater treatment process.
{"title":"Real-time update of data-driven reduced and full order models with applications","authors":"Om Prakash, Biao Huang","doi":"10.1016/j.compchemeng.2024.108923","DOIUrl":"10.1016/j.compchemeng.2024.108923","url":null,"abstract":"<div><div>We consider a dynamic mode decomposition (DMD) based technique to identify data-driven reduced-order and full-order models and propose two approaches to update them in real-time. These updates are crucial for the models to adapt to the evolving process. The proposed approaches function by calculating the update of the singular value decomposition (SVD), which is the core operation in DMD. In particular, two approaches involving temporal updates and additive modifications are used to update the SVDs. Further, the equivalence of both approaches is proved under special rank conditions. Also, the computational costs involved in these approaches are discussed. The technique is well suited for adaptive process modeling that can be exploited for real-time process monitoring, estimation, control, and optimization. The efficacy of the proposed approach is demonstrated using a large-scale benchmark wastewater treatment process.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108923"},"PeriodicalIF":3.9,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747136","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 : 2024-11-22DOI: 10.1016/j.compchemeng.2024.108925
Yan Xu , Qun-Xiong Zhu , Wei Ke , Yan-Lin He , Ming-Qing Zhang , Yuan Xu
In industrial processes, limitations of the physical environment, sensors drop-out, and repetitive sampling often lead to insufficient and unevenly distributed representative instances, which greatly hinders the accuracy of soft-sensing models. This paper presents a novel virtual sample generation method based on Glow-embedded variational autoencoder (GVAE-VSG), aimed at enhancing data richness and diversity to improve the modeling performance. Specifically, GVAE-VSG embeds the Glow model from flow transformations into the variational autoencoder. This allows for the derivation of a more generalized posterior distribution without reducing sample dimensionality, thereby ensuring the generation of higher-quality virtual input samples. Subsequently, a nonlinear iterative partial least squares regression framework, incorporating a sparse constrained error matrix, is employed to generate virtual output samples that more closely resemble actual data. Finally, by a synthetic nonlinear function and an actual purification terephthalic acid (PTA) solvent system, the generative and modeling performance of the proposed method are comprehensively assessed.
{"title":"Virtual sample generation for soft-sensing in small sample scenarios using glow-embedded variational autoencoder","authors":"Yan Xu , Qun-Xiong Zhu , Wei Ke , Yan-Lin He , Ming-Qing Zhang , Yuan Xu","doi":"10.1016/j.compchemeng.2024.108925","DOIUrl":"10.1016/j.compchemeng.2024.108925","url":null,"abstract":"<div><div>In industrial processes, limitations of the physical environment, sensors drop-out, and repetitive sampling often lead to insufficient and unevenly distributed representative instances, which greatly hinders the accuracy of soft-sensing models. This paper presents a novel virtual sample generation method based on Glow-embedded variational autoencoder (GVAE-VSG), aimed at enhancing data richness and diversity to improve the modeling performance. Specifically, GVAE-VSG embeds the Glow model from flow transformations into the variational autoencoder. This allows for the derivation of a more generalized posterior distribution without reducing sample dimensionality, thereby ensuring the generation of higher-quality virtual input samples. Subsequently, a nonlinear iterative partial least squares regression framework, incorporating a sparse constrained error matrix, is employed to generate virtual output samples that more closely resemble actual data. Finally, by a synthetic nonlinear function and an actual purification terephthalic acid (PTA) solvent system, the generative and modeling performance of the proposed method are comprehensively assessed.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"193 ","pages":"Article 108925"},"PeriodicalIF":3.9,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743283","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 : 2024-11-22DOI: 10.1016/j.compchemeng.2024.108950
A. Abdallah El Hadj , A. Ait Yahia , K. Hamza , M. Laidi , S. Hanini
The main subject of this work is the application of advanced artificial intelligence (AI) techniques to accurately predict the parameters of the hydrogen liquefaction process. This study employs a comparative analysis of the most reliable AI techniques: Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), support vector machines (SVM), perturbed chain statistical associated fluid theory (PCSAFT) equation of state and Hybrid technique based on the combination of ANN model and perturbed chain statistical associated fluid theory (AI-PCSAFT). The training and validation strategy focuses on using a validation agreement vector, determined through linear regression analysis of the predicted versus reference outputs, as an indication of the predictive ability of the studied models. A dataset collected from scientific papers containing hydrogen liquefaction process data was utilized in the modeling process. The modeling strategy is performed using the temperature (T), pressure (P), and mass flow rate (m) as input parameters and the stream energy (E) as output parameters.
The results show high predictability of the optimized ANFIS model followed by AI-PACSAFT model compared to ANN, SVM models and PCSAFT equation of state with coefficient of correlation (R) and absolute relative deviation (AARD) equal to 0.9988 and 0.98% respectively.
{"title":"Modeling of hydrogen liquefaction process parameters using advanced artificial intelligence technique","authors":"A. Abdallah El Hadj , A. Ait Yahia , K. Hamza , M. Laidi , S. Hanini","doi":"10.1016/j.compchemeng.2024.108950","DOIUrl":"10.1016/j.compchemeng.2024.108950","url":null,"abstract":"<div><div>The main subject of this work is the application of advanced artificial intelligence (AI) techniques to accurately predict the parameters of the hydrogen liquefaction process. This study employs a comparative analysis of the most reliable AI techniques: Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), support vector machines (SVM), perturbed chain statistical associated fluid theory (PCSAFT) equation of state and Hybrid technique based on the combination of ANN model and perturbed chain statistical associated fluid theory (AI-PCSAFT). The training and validation strategy focuses on using a validation agreement vector, determined through linear regression analysis of the predicted versus reference outputs, as an indication of the predictive ability of the studied models. A dataset collected from scientific papers containing hydrogen liquefaction process data was utilized in the modeling process. The modeling strategy is performed using the temperature (T), pressure (P), and mass flow rate (m) as input parameters and the stream energy (E) as output parameters.</div><div>The results show high predictability of the optimized ANFIS model followed by AI-PACSAFT model compared to ANN, SVM models and PCSAFT equation of state with coefficient of correlation (R) and absolute relative deviation (AARD) equal to 0.9988 and 0.98% respectively.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108950"},"PeriodicalIF":3.9,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136632","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 : 2024-11-22DOI: 10.1016/j.compchemeng.2024.108921
Ethan M. Sunshine , Giovanna Bucci , Tanusree Chatterjee , Shyam Deo , Victoria M. Ehlinger , Wenqin Li , Thomas Moore , Corey Myers , Wenyu Sun , Bo-Xun Wang , Mengyao Yuan , John R. Kitchin , Carl D. Laird , Matthew J. McNenly , Sneha A. Akhade
Multiscale optimization problems require the interconnection of several models of distinct phenomena which occur at different scales in length or time. However, the best model for any particular phenomenon may not be amenable to rigorous optimization techniques. For instance, molecular interactions are often modeled by computational chemistry software packages that cannot be easily converted into optimization constraints. Data-driven surrogate models can overcome this problem. By choosing surrogates with functional forms that are convertible to a mixed-integer linear model, one can connect and optimize these surrogates instead of the underlying models. We demonstrate the interconnection of linear model decision trees to optimize across three scales of a formic acid dehydrogenation process. We show that optimizing across all three scales simultaneously leads to a 40% cost savings compared to optimizing each model independently. Furthermore, the surrogates retain some relevant physical behaviors and provide insights into the optimal design of this process.
{"title":"Multiscale optimization of formic acid dehydrogenation process via linear model decision tree surrogates","authors":"Ethan M. Sunshine , Giovanna Bucci , Tanusree Chatterjee , Shyam Deo , Victoria M. Ehlinger , Wenqin Li , Thomas Moore , Corey Myers , Wenyu Sun , Bo-Xun Wang , Mengyao Yuan , John R. Kitchin , Carl D. Laird , Matthew J. McNenly , Sneha A. Akhade","doi":"10.1016/j.compchemeng.2024.108921","DOIUrl":"10.1016/j.compchemeng.2024.108921","url":null,"abstract":"<div><div>Multiscale optimization problems require the interconnection of several models of distinct phenomena which occur at different scales in length or time. However, the best model for any particular phenomenon may not be amenable to rigorous optimization techniques. For instance, molecular interactions are often modeled by computational chemistry software packages that cannot be easily converted into optimization constraints. Data-driven surrogate models can overcome this problem. By choosing surrogates with functional forms that are convertible to a mixed-integer linear model, one can connect and optimize these surrogates instead of the underlying models. We demonstrate the interconnection of linear model decision trees to optimize across three scales of a formic acid dehydrogenation process. We show that optimizing across all three scales simultaneously leads to a 40% cost savings compared to optimizing each model independently. Furthermore, the surrogates retain some relevant physical behaviors and provide insights into the optimal design of this process.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108921"},"PeriodicalIF":3.9,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747138","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 : 2024-11-20DOI: 10.1016/j.compchemeng.2024.108914
Misagh Rahbari, Alireza Arshadi Khamseh, Mohammad Mohammadi
The agri-food supply chain management plays a crucial role in ensuring the interests of supply chain components and food security in society. Additionally, due to the nature of agri-food products, sustainability dimensions have always been of concern to organizations engaged in this field. The importance of the timely and quality provision of agri-food products has doubled after the global crisis. Therefore, this study focuses on optimizing and analyzing the sustainable multi-objective closed-loop supply chain network for agri-food products, with a case study on the canned food under uncertainty. Strategic and operational decisions and other features are considered to achieve more accurate results. To address the various dimensions of sustainability, the problem is considered as a four-objective one, aiming to maximize the use of available production throughput for factories, maximize job opportunities created, minimize supply chain costs, and ultimately minimize unmet demands. The carbon cap and trade mechanism is used to control greenhouse gas emissions in the supply chain network. A robust scenario-based stochastic chance constrained programming approach is employed to deal with the uncertainty, and also validation is performed using various criteria. Moreover, an augmented ε-constraint optimization approach is used to solve the multi-objective problem and achieve Pareto optimal solutions. Finally, sensitivity analysis is employed to prepare for potential changes in some problem parameters.
{"title":"A multi-objective robust scenario-based stochastic chance constrained programming model for sustainable closed-loop agri-food supply chain","authors":"Misagh Rahbari, Alireza Arshadi Khamseh, Mohammad Mohammadi","doi":"10.1016/j.compchemeng.2024.108914","DOIUrl":"10.1016/j.compchemeng.2024.108914","url":null,"abstract":"<div><div>The agri-food supply chain management plays a crucial role in ensuring the interests of supply chain components and food security in society. Additionally, due to the nature of agri-food products, sustainability dimensions have always been of concern to organizations engaged in this field. The importance of the timely and quality provision of agri-food products has doubled after the global crisis. Therefore, this study focuses on optimizing and analyzing the sustainable multi-objective closed-loop supply chain network for agri-food products, with a case study on the canned food under uncertainty. Strategic and operational decisions and other features are considered to achieve more accurate results. To address the various dimensions of sustainability, the problem is considered as a four-objective one, aiming to maximize the use of available production throughput for factories, maximize job opportunities created, minimize supply chain costs, and ultimately minimize unmet demands. The carbon cap and trade mechanism is used to control greenhouse gas emissions in the supply chain network. A robust scenario-based stochastic chance constrained programming approach is employed to deal with the uncertainty, and also validation is performed using various criteria. Moreover, an augmented ε-constraint optimization approach is used to solve the multi-objective problem and achieve Pareto optimal solutions. Finally, sensitivity analysis is employed to prepare for potential changes in some problem parameters.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108914"},"PeriodicalIF":3.9,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747135","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}
Growing energy demand and its consequences, such as fossil fuel depletion, greenhouse gas emissions, and global warming, prompted the need for large-scale solar power plants. Floating photovoltaic systems have many advantages over ground-mounted systems, including methods and resources, reducing costs, and improving efficiency. In this regard, this study aims at presenting an optimization model for developing a sustainable and resilient floating solar photovoltaic supply chain network design. The concerned model's objective function is minimizing the total supply chain costs in addition to maximizing greenhouse gas emissions reduction. To identify the most suitable dams for establishing the floating photovoltaic system, the hybrid approach by applying the fuzzy best-worst method and the TOPSIS technique is first exploited. Thereinafter, the selected dams are exerted in the presented mathematical model. Eventually, a real case study is implemented on floating photovoltaic systems to assess the proposed model's performance, from which important managerial insights are attained.
{"title":"An optimization approach for sustainable and resilient closed-loop floating solar photovoltaic supply chain network design","authors":"Maryam Nili , Mohammad Saeed Jabalameli , Armin Jabbarzadeh , Ehsan Dehghani","doi":"10.1016/j.compchemeng.2024.108927","DOIUrl":"10.1016/j.compchemeng.2024.108927","url":null,"abstract":"<div><div>Growing energy demand and its consequences, such as fossil fuel depletion, greenhouse gas emissions, and global warming, prompted the need for large-scale solar power plants. Floating photovoltaic systems have many advantages over ground-mounted systems, including methods and resources, reducing costs, and improving efficiency. In this regard, this study aims at presenting an optimization model for developing a sustainable and resilient floating solar photovoltaic supply chain network design. The concerned model's objective function is minimizing the total supply chain costs in addition to maximizing greenhouse gas emissions reduction. To identify the most suitable dams for establishing the floating photovoltaic system, the hybrid approach by applying the fuzzy best-worst method and the TOPSIS technique is first exploited. Thereinafter, the selected dams are exerted in the presented mathematical model. Eventually, a real case study is implemented on floating photovoltaic systems to assess the proposed model's performance, from which important managerial insights are attained.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"193 ","pages":"Article 108927"},"PeriodicalIF":3.9,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722609","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 : 2024-11-19DOI: 10.1016/j.compchemeng.2024.108945
Dominic Bui Viet, Gustavo Fimbres Weihs, Gobinath Rajarathnam, Ali Abbas
Kinetic Monte Carlo (kMC) models are a well-established modelling framework for the simulation of complex free-radical kinetic systems. kMC models offer the advantage of discretely monitoring every chain sequence in the system, providing full accounting of the chain molecular weight distribution. These models are marred by the necessity to simulate a minimum number of molecules, which confers significant computational burden. This paper adapts and creates a highly generalizable methodology for scaling dilute radical populations in discrete stochastic models, such as Gillespie's Stochastic Simulation Algorithm (SSA). The methodology is then applied to a kMC simulation of polystyrene (PS) pyrolysis, using a modelling framework adapted from literature. The results show that the required number of simulated molecules can be successfully reduced by up to three orders of magnitude with minimal loss of convergent behaviour, corresponding to a wall-clock simulation speed reduction of between 95.2 to 99.6 % at common pyrolysis temperatures.
{"title":"Highly accelerated kinetic Monte Carlo models for depolymerisation systems","authors":"Dominic Bui Viet, Gustavo Fimbres Weihs, Gobinath Rajarathnam, Ali Abbas","doi":"10.1016/j.compchemeng.2024.108945","DOIUrl":"10.1016/j.compchemeng.2024.108945","url":null,"abstract":"<div><div>Kinetic Monte Carlo (kMC) models are a well-established modelling framework for the simulation of complex free-radical kinetic systems. kMC models offer the advantage of discretely monitoring every chain sequence in the system, providing full accounting of the chain molecular weight distribution. These models are marred by the necessity to simulate a minimum number of molecules, which confers significant computational burden. This paper adapts and creates a highly generalizable methodology for scaling dilute radical populations in discrete stochastic models, such as Gillespie's Stochastic Simulation Algorithm (SSA). The methodology is then applied to a kMC simulation of polystyrene (PS) pyrolysis, using a modelling framework adapted from literature. The results show that the required number of simulated molecules can be successfully reduced by up to three orders of magnitude with minimal loss of convergent behaviour, corresponding to a wall-clock simulation speed reduction of between 95.2 to 99.6 % at common pyrolysis temperatures.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"193 ","pages":"Article 108945"},"PeriodicalIF":3.9,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722608","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 : 2024-11-19DOI: 10.1016/j.compchemeng.2024.108926
Parth Shah, Silabrata Pahari, Raj Bhavsar, Joseph Sang-Il Kwon
In recent years, the integration of mechanistic process models with advanced machine learning techniques has led to the development of hybrid models, which have shown remarkable potential across various domains. However, despite numerous applications and reviews, there is a significant gap in practical resources that guide new researchers through the process of building these models from the ground up. This work addresses this gap by offering a comprehensive tutorial designed to demystify the development of hybrid models. We focus on the practical implementation, beginning with fundamental concepts and advancing to detailed mathematical formulations, providing a step-by-step walkthrough for constructing hybrid models. The tutorial includes detailed case studies illustrating the application of hybrid models in solving complex problems in process systems engineering. By following this guide, researchers will acquire the necessary tools and knowledge to apply hybrid modeling techniques effectively for real-world implementations, paving the way for further innovation and adoption in the field.
{"title":"Hybrid modeling of first-principles and machine learning: A step-by-step tutorial review for practical implementation","authors":"Parth Shah, Silabrata Pahari, Raj Bhavsar, Joseph Sang-Il Kwon","doi":"10.1016/j.compchemeng.2024.108926","DOIUrl":"10.1016/j.compchemeng.2024.108926","url":null,"abstract":"<div><div>In recent years, the integration of mechanistic process models with advanced machine learning techniques has led to the development of hybrid models, which have shown remarkable potential across various domains. However, despite numerous applications and reviews, there is a significant gap in practical resources that guide new researchers through the process of building these models from the ground up. This work addresses this gap by offering a comprehensive tutorial designed to demystify the development of hybrid models. We focus on the practical implementation, beginning with fundamental concepts and advancing to detailed mathematical formulations, providing a step-by-step walkthrough for constructing hybrid models. The tutorial includes detailed case studies illustrating the application of hybrid models in solving complex problems in process systems engineering. By following this guide, researchers will acquire the necessary tools and knowledge to apply hybrid modeling techniques effectively for real-world implementations, paving the way for further innovation and adoption in the field.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108926"},"PeriodicalIF":3.9,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136258","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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