Computers & Chemical Engineering最新文献_第4页

Integrating machine learning and distributionally robust optimization for sustainable agricultural supply chains under global warming uncertainty 全球变暖不确定性下可持续农业供应链的机器学习与分布鲁棒优化集成

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-09-22 DOI: 10.1016/j.compchemeng.2025.109412

Hamed Darouni, Farnaz Barzinpour, Amin Reza Kalantari Khalil Abad

{"title":"Integrating machine learning and distributionally robust optimization for sustainable agricultural supply chains under global warming uncertainty","authors":"Hamed Darouni, Farnaz Barzinpour, Amin Reza Kalantari Khalil Abad","doi":"10.1016/j.compchemeng.2025.109412","DOIUrl":"10.1016/j.compchemeng.2025.109412","url":null,"abstract":"<div><div>Agricultural supply chains face substantial challenges in ensuring food security and sustainability, particularly due to the impacts of climate change, including global warming. To optimize resource use and minimize waste, it is essential to manage these supply chains effectively, especially in the face of uncertainty. This research addresses the crucial challenge of designing a sustainable closed-loop agricultural supply chain network, with a specific focus on jujube products in the context of temperature-yield uncertainty. The model enhances economic sustainability by minimizing costs, social sustainability through job creation requirements, and environmental sustainability by implementing carbon emission caps, while taking into account decisions regarding facility locations, inter-facility flows, inventory, and shortage management. Our main contribution is a distributionally robust optimization approach that integrates a K-means clustering machine learning algorithm to generate scenarios from historical data patterns, addressing the dynamic and interrelated uncertainties in temperature-yield data. The framework incorporates closed-loop principles through thermochemical conversion processes that transform agricultural waste into value-added biochar products. A comprehensive case study of the jujube industry in South Khorasan Province, Iran, validates the model's effectiveness. Results demonstrate that moderate conservatism levels (<span><math><mi>ω</mi></math></span> between 0.8 and 1.2) achieve an 88% reduction in operational risk variability while incurring only a 3% cost increase. A comparative analysis reveals that the proposed approach achieves a 0.95 risk-adjusted performance score, outperforming traditional stochastic programming and robust optimization alternatives. This research provides agricultural supply chain managers with practical guidelines for managing temperature-yield uncertainty.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109412"},"PeriodicalIF":3.9,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227131","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}

引用次数: 0

Online parameter estimation and model maintenance using parameter-aware physics-informed neural network 基于参数感知物理信息神经网络的在线参数估计和模型维护

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-09-20 DOI: 10.1016/j.compchemeng.2025.109403

Devavrat Thosar , Abhijit Bhakte , Zukui Li , Rajagopalan Srinivasan , Vinay Prasad

{"title":"Online parameter estimation and model maintenance using parameter-aware physics-informed neural network","authors":"Devavrat Thosar , Abhijit Bhakte , Zukui Li , Rajagopalan Srinivasan , Vinay Prasad","doi":"10.1016/j.compchemeng.2025.109403","DOIUrl":"10.1016/j.compchemeng.2025.109403","url":null,"abstract":"<div><div>Machine learning-based (ML) digital twins for chemical processes are gaining popularity with the advent of Industry 4.0. These digital twins are often developed under the assumption of constant process parameters. However, in most chemical engineering processes, parameters often change during operations. To ensure optimal performance under such evolving conditions, there is a need for models that can adapt to these changes. In this work, we propose a framework for developing a PINN-based (Physics-Informed Neural Network) digital twin that is sensitive to parameter variations. The proposed framework also monitors the process in real-time using physics-based residual equations, identifies the parameters undergoing changes using sensitivity matrices, and re-estimates them to maintain the performance of the PINN model. We demonstrate the utility of the framework through a case study involving a continuous stirred tank reactor experiencing changes in activation energy and the overall heat transfer coefficient. The results show that the proposed framework improves the predictive accuracy of the PINN by approximately 84% for ramp changes and 12% for step changes in parameters. The framework is further applied to more realistic case studies, including a polymethyl methacrylate polymerization reactor and a pressure swing adsorption process, highlighting its applicability to high-dimensional nonlinear systems and cyclic separation processes. These findings indicate that the performance of digital twins can be significantly enhanced in the presence of varying process parameters by employing a PINN architecture that incorporates parameters as inputs and solves real-time inverse problems to estimate parameter values.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109403"},"PeriodicalIF":3.9,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105346","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}

引用次数: 0

Deep reinforcement learning-based thermal management of battery subpack in electric vehicle 基于深度强化学习的电动汽车电池子组热管理

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-09-19 DOI: 10.1016/j.compchemeng.2025.109406

Sanghoon Shin, Dabin Jeong, Yeonsoo Kim

{"title":"Deep reinforcement learning-based thermal management of battery subpack in electric vehicle","authors":"Sanghoon Shin, Dabin Jeong, Yeonsoo Kim","doi":"10.1016/j.compchemeng.2025.109406","DOIUrl":"10.1016/j.compchemeng.2025.109406","url":null,"abstract":"<div><div>With the increasing adoption of electric vehicles (EVs), effective battery thermal management is crucial to maintain safety and optimize performance. This study proposes a deep reinforcement learning (DRL)- based approach for battery thermal management, employing the Deep Deterministic Policy Gradient (DDPG) algorithm to regulate coolant flow rate and temperature. The objective is to maintain the battery temperature within the desirable operating range while minimizing energy consumption. A tailored reward function is formulated to consider the energy consumption minimization and thermal management. The effectiveness of the proposed DRL-based controller is evaluated by comparing the results with those of the zone model predictive controller (MPC). Simulation results demonstrate that the DRL-based controller achieves comparable performance to the MPC in battery temperature regulation, while reducing overall energy consumption and maintaining thermal stability. These findings highlight the potential of DRL-based control strategies as a viable alternative to MPC, offering improved energy efficiency for battery thermal management systems without requiring an explicit system model.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109406"},"PeriodicalIF":3.9,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118170","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}

引用次数: 0

Optimal experiments for hybrid modeling of methanol synthesis kinetics 甲醇合成动力学混合建模的优化实验

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-09-17 DOI: 10.1016/j.compchemeng.2025.109387

Lothar Kaps , Johannes Leipold , Christoph Plate , Carl Julius Martensen , Wieland Kortuz , Andreas Seidel-Morgenstern , Achim Kienle , Sebastian Sager

{"title":"Optimal experiments for hybrid modeling of methanol synthesis kinetics","authors":"Lothar Kaps , Johannes Leipold , Christoph Plate , Carl Julius Martensen , Wieland Kortuz , Andreas Seidel-Morgenstern , Achim Kienle , Sebastian Sager","doi":"10.1016/j.compchemeng.2025.109387","DOIUrl":"10.1016/j.compchemeng.2025.109387","url":null,"abstract":"<div><div>The transition of the chemical industry towards the utilization of feedstocks based on renewable energies results in a more dynamic process behavior. Advanced mathematical methods are a key factor to handle this complexity. In this contribution, methanol synthesis from hydrogen, carbon dioxide and carbon monoxide is investigated as promising power-2-X technology. Optimal experimental design is used to recalibrate an existing mechanistic kinetic model. Subsequently, the most uncertain sub-model, namely the reversible catalyst dynamics, is partially replaced by neural networks. Several architectures were evaluated, and optimal experimental design was applied to enhance the performance of a chosen architecture. All experiments were realized in an experimental set-up able to acquire time-resolved data. A commercial CuO/ZnO/Al<sub>2</sub>O<sub>3</sub> catalyst was used in a well-mixed Berty type reactor. The combination of optimal experimental design with hybrid modeling led to an improved quality of the kinetic model needed for process control and optimization.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109387"},"PeriodicalIF":3.9,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118171","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}

引用次数: 0

Behavioral strategies evolution of stakeholders for wastewater recycling in eco-industrial parks under financial constraints 资金约束下生态工业园区废水循环利用利益相关者行为策略演化

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-09-15 DOI: 10.1016/j.compchemeng.2025.109402

Kaixuan Zhang , Xu Han

{"title":"Behavioral strategies evolution of stakeholders for wastewater recycling in eco-industrial parks under financial constraints","authors":"Kaixuan Zhang , Xu Han","doi":"10.1016/j.compchemeng.2025.109402","DOIUrl":"10.1016/j.compchemeng.2025.109402","url":null,"abstract":"<div><div>Wastewater recycling in eco-industrial parks (EIPs) represents an effective approach to achieving water sustainability. However, financial constraints among multiple stakeholders hinder the development of wastewater recycling systems. To address this challenge, this paper develops a tripartite evolutionary game model involving upstream manufacturers, downstream manufacturers, and banks under government environmental regulations. The model examines wastewater management strategies while accounting for constrained access to green financing. The analysis reveals that multiple factors significantly influence stakeholders’ participation behaviors and evolutionarily stable strategies (ESS). Specifically, higher wastewater transaction prices strengthen downstream manufacturers’ engagement in industrial symbiosis. Higher green loan interest rates motivate banks to extend credit to upstream manufacturers. Although increased subsidies promote downstream manufacturers’ participation, they may simultaneously diminish upstream manufacturers’ demand for green loans. Notably, higher emission allowances reduce the incentive for upstream manufacturers to borrow green loans, while higher transaction prices have the opposite effect. Based on these findings, this paper offers policy suggestions aimed at improving financing mechanisms and fostering sustainable industrial symbiosis within EIPs. The results provide valuable insights into stakeholder behavior dynamics and support decision-making for both practitioners and policymakers.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109402"},"PeriodicalIF":3.9,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105347","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}

引用次数: 0

A risk-averse two-stage stochastic programming for biomass supply chain planning problem 生物质供应链规划问题的风险规避两阶段随机规划

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-09-14 DOI: 10.1016/j.compchemeng.2025.109401

Bilge Bilgen , Halil Akbaş , Melis Karaşahin

{"title":"A risk-averse two-stage stochastic programming for biomass supply chain planning problem","authors":"Bilge Bilgen , Halil Akbaş , Melis Karaşahin","doi":"10.1016/j.compchemeng.2025.109401","DOIUrl":"10.1016/j.compchemeng.2025.109401","url":null,"abstract":"<div><div>This study addresses the problem of designing a sustainable biomass supply chain (BSC) network under uncertainty. The main challenge lies in determining how to optimally locate biomass processing facilities and manage the flow of materials, such as biomass, biogas, fertilizer, and water, while accounting for uncertain factors. A mixed-integer linear programming model is proposed. The model identifies optimal plant locations, determines the quantities of biomass to be delivered and processed for biogas production, and manages the distribution of outputs to agricultural fields. The objective is to minimize transportation and production costs across a two-echelon BSC network. A risk-neutral two-stage stochastic programming (SP) model is presented to incorporate uncertainties associated with electricity demand and transportation costs. In addition, conditional value-at-risk is used as a risk measure in the modeling and robust solutions are obtained by applying a risk-averse two-stage SP model. Sensitivity analysis is performed to support decision-making processes in BSC management. The proposed BSC models are tested in a sustainable BSC network involving two-echelon biomass supply and biorefinery sites in the municipal area of Izmir in Türkiye. The empirical study on BSC models confirms that the risk parameters influence the objective function value. The experimental findings prove that BSC risk models provide optimal results with lower costs from a cost minimization perspective.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109401"},"PeriodicalIF":3.9,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105880","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}

引用次数: 0

Reinforcement learning-guided two-stage optimization framework for multi-product batch scheduling 基于强化学习的多产品批调度两阶段优化框架

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-09-13 DOI: 10.1016/j.compchemeng.2025.109399

Jiawen Zhu , Wenli Du , Chen Fan , Muyi Huang , Chuan Wang , Furong Zhang

{"title":"Reinforcement learning-guided two-stage optimization framework for multi-product batch scheduling","authors":"Jiawen Zhu , Wenli Du , Chen Fan , Muyi Huang , Chuan Wang , Furong Zhang","doi":"10.1016/j.compchemeng.2025.109399","DOIUrl":"10.1016/j.compchemeng.2025.109399","url":null,"abstract":"<div><div>With the increasing demand for high-end and fine manufacturing, multi-product batch scheduling has become essential in process industries. Its inherent complexity stems from hybrid decision variables and tightly coupled constraints. To address these challenges, this study proposes a two-stage optimization framework that integrates reinforcement learning (RL) and mathematical programming (MP). The RL layer determines batch allocations and production sequences, which are then transmitted as time windows within which the MP layer optimizes continuous variables to ensure feasibility. To handle hybrid action spaces, a mapping mechanism is introduced to unify discrete and continuous decisions. In addition, dynamic short-term targets based on reformulated constraints are designed to address the sparsity of rewards caused by long-horizon objectives. Experiments on polyolefin production scheduling demonstrate that the proposed method outperforms MP and standalone RL in terms of economic profit, production stability, and computational performance.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109399"},"PeriodicalIF":3.9,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105879","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}

引用次数: 0

Synchronous optimization framework for the integrated hydrogen network and heat exchanging system: A decomposition optimization strategy of the system based on the structure generation method 氢网换热一体化系统同步优化框架：基于结构生成法的系统分解优化策略

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-09-12 DOI: 10.1016/j.compchemeng.2025.109364

Shizhao Chen , Dan Yang , Xin Peng , Weichao Ding , Shuai Tan , Weimin Zhong

引用次数: 0

Adaptive fault detection via machine unlearning 基于机器学习的自适应故障检测

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-09-12 DOI: 10.1016/j.compchemeng.2025.109394

Amal Anto , Deepak Kumar , Hariprasad Kodamana , Manojkumar Ramteke

{"title":"Adaptive fault detection via machine unlearning","authors":"Amal Anto , Deepak Kumar , Hariprasad Kodamana , Manojkumar Ramteke","doi":"10.1016/j.compchemeng.2025.109394","DOIUrl":"10.1016/j.compchemeng.2025.109394","url":null,"abstract":"<div><div>Data-driven models are widely relied upon for process fault detection. However, their performance is susceptible to the quality of training data. Anomalous data in training or online updates degrade detection models, increasing false alarms or missed detections, and retraining models on corrected datasets is impractical for real-time fault detection. To address this problem, we propose a machine unlearning based adaptive fault detection that updates the model parameters to selectively remove the influence of faulty data from trained models without retraining and compromising the accuracy on normal data. We implement blindspot unlearning on four deep learning-based fault detection models: Autoencoder (AE), Variational Autoencoder (VAE), LSTM-AE, and LSTM-VAE, and evaluate their performance on two benchmark datasets: the Tennessee Eastman Process (TEP) and the Wastewater Treatment Plant (WWTP). We evaluate the models’ fault detection performance before and after unlearning. Our findings demonstrate that unlearning improves fault detection performance while significantly reducing computational overhead. Compared to the original models, unlearned models showcased improved fault detection rate, achieving a 44% increase on the TEP dataset and reaching 90% on the WWTP dataset. Unlearned models achieved fault detection performance comparable to retrained models, reducing computational time by up to 46% on TEP and 33% on WWTP. This validates the effectiveness of machine unlearning for adaptive fault detection.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109394"},"PeriodicalIF":3.9,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118169","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}

引用次数: 0

Uniting neural network-based control and model predictive control: Application to a large-scale nonlinear process 结合神经网络控制与模型预测控制：在大规模非线性过程中的应用

IF 3.9 2区工程技术

Computers & Chemical Engineering Pub Date : 2025-09-12 DOI: 10.1016/j.compchemeng.2025.109396

Arthur Khodaverdian , Dhruv Gohil , Panagiotis D. Christofides

{"title":"Uniting neural network-based control and model predictive control: Application to a large-scale nonlinear process","authors":"Arthur Khodaverdian , Dhruv Gohil , Panagiotis D. Christofides","doi":"10.1016/j.compchemeng.2025.109396","DOIUrl":"10.1016/j.compchemeng.2025.109396","url":null,"abstract":"<div><div>This work proposes a method to overcome the issue of nonlinear model predictive control (MPC) requiring practically infeasible computation times for large-scale systems. In particular, the use of Neural Networks (NN) to approximate nonlinear MPC calculated control actions in a real-time closed-loop implementation with externally enforced stability guarantees is explored. Using Lyapunov-based stability constraints, the reduced computational complexity of NNs paired with the ability to train using MPC that would be infeasible to apply in real-time systems (due to the use of a large prediction horizon to ensure good closed-loop performance) enables the training of an NN-based approximate control policy that directly substitutes MPC. With a stabilizing fallback controller available, this NN controller enables real-time stabilizing control of high-dimensional nonlinear systems. To demonstrate this, Aspen Plus Dynamics, a dynamic chemical process simulation software, is used to create a large-scale nonlinear chemical process example. Using an NN trained off of an offline MPC using a first-principles model and a large prediction horizon, a comprehensive study of the resulting closed-loop behavior is carried out to evaluate the closed-loop stability, performance, and robustness properties of the approach.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109396"},"PeriodicalIF":3.9,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044734","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}

引用次数: 0