Computers & Chemical Engineering最新文献

{"title":"Dynamic hybrid modeling: Incremental identification and model predictive control","authors":"Adrian Caspari ,&nbsp;Thomas Bierweiler ,&nbsp;Sarah Fadda ,&nbsp;Daniel Labisch ,&nbsp;Maarten Nauta ,&nbsp;Franzisko Wagner ,&nbsp;Merle Warmbold ,&nbsp;Constantinos C. Pantelides","doi":"10.1016/j.compchemeng.2025.109413","DOIUrl":"10.1016/j.compchemeng.2025.109413","url":null,"abstract":"<div><div>Mathematical models are crucial for optimizing and controlling chemical processes, yet they often face significant limitations in terms of computational time, algorithm complexity, and development costs. Hybrid models, which combine mechanistic models with data-driven models (i.e. models derived via the application of machine learning to experimental data), have emerged as a promising solution to these challenges. However, the identification of dynamic hybrid models remains difficult due to the need to integrate data-driven models within mechanistic model structures.</div><div>We present an incremental identification approach for dynamic hybrid models that decouples the mechanistic and data-driven components to overcome computational and conceptual difficulties. Our methodology comprises four key steps: (1) regularized dynamic parameter estimation to determine optimal time profiles for flux variables, (2) correlation analysis to evaluate relationships between variables, (3) data-driven model identification using advanced machine learning techniques, and (4) hybrid model integration to combine the mechanistic and data-driven components. This approach facilitates early evaluation of model structure suitability, accelerates the development of hybrid models, and allows for independent identification of data-driven components.</div><div>Three case studies are presented to illustrate the robustness, reliability, and efficiency of our incremental approach in handling complex systems and scenarios with limited data.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109413"},"PeriodicalIF":3.9,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145155407","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}

{"title":"A novel data augmentation strategy for gas leak detection and segmentation using Mask R-CNN and bit plane slicing in chemical process environments","authors":"Hritu Raj,&nbsp;Gargi Srivastava","doi":"10.1016/j.compchemeng.2025.109407","DOIUrl":"10.1016/j.compchemeng.2025.109407","url":null,"abstract":"<div><div>Gas leak detection is a critical task for environmental and industrial safety, often facilitated through imaging techniques such as Mask R-CNN. However, accurately segmenting gas plumes remains challenging due to their dynamic nature and complex background. In this study, we propose a novel approach to improve gas leak plume segmentation accuracy by combining Mask R-CNN with augmented bit plane images. Initially trained on a dataset of 1000 gas leak images, our model, utilizing a ResNet101 backbone, achieved a commendable F1-Score of 95.6%, outperforming MobileNetV2 and DenseNet169. Through the incorporation of a novel bit plane image augmentation strategy, specifically focusing on the XOR combination of bit planes 4 and 5, the ResNet101 model’s F1-Score significantly improved to 98.7%, showcasing the effectiveness of our approach in enriching the training data and enhancing the model’s ability to generalize to unseen instances. This bit plane augmentation method also demonstrated superior performance compared to other mainstream image enhancement techniques like CLAHE and Gamma correction. These findings suggest promising implications for improving gas leak detection systems, thereby contributing to enhanced safety measures in various industrial and environmental settings, with considerations for real-time industrial deployment.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109407"},"PeriodicalIF":3.9,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154735","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}

{"title":"Reinforcement learning-based autonomous control of bench-scale primary separation vessel","authors":"Oguzhan Dogru,&nbsp;Mahmut Berat Tatlici,&nbsp;Biao Huang","doi":"10.1016/j.compchemeng.2025.109405","DOIUrl":"10.1016/j.compchemeng.2025.109405","url":null,"abstract":"<div><div>In the process industry, smart automation of complex operations has great potential for efficient and safe operation, making it a key component for unlocking economic and sustainable large-scale production. However, real-world process units such as primary separation vessels (PSVs) pose numerous challenges, such as sensory uncertainty, nonlinear dynamics, and operational variability. This study introduces a novel autonomous control framework integrating model predictive control (MPC), reinforcement learning (RL), and state estimation techniques for building an adaptive, optimal, and safe control strategy. The proposed framework is demonstrated in a real-world scenario using a bench-scale experimental setup of the PSV that mimics the actual process. The implemented closed-loop control system accurately predicted a crucial process variable, optimized the operating point in real time, and achieved robust set-point tracking performance by tuning the controller for real process conditions. The results indicate that incorporating adaptive and data-driven techniques such as reinforcement learning into feedback control approaches is promising for building robust autonomous control strategies that maximize efficiency while respecting physical constraints, paving the way for autonomous control systems that are deployable in complex real-world scenarios.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109405"},"PeriodicalIF":3.9,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145155405","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}

{"title":"Data-driven globalized distributionally robust multi-period location-routing-scheduling model for waste-to-energy supply chain under emissions ambiguity","authors":"Xuekun Wang ,&nbsp;Zhaozhuang Guo ,&nbsp;Ying Liu","doi":"10.1016/j.compchemeng.2025.109397","DOIUrl":"10.1016/j.compchemeng.2025.109397","url":null,"abstract":"<div><div>The intensification of global energy shortages and continuous expansion of municipal solid waste require effectively optimizing the waste-to-energy supply chain (WtESC). When the distribution information of uncertain parameters is partially known, WtESC often faces complex and ambiguous challenges. To address this, we construct data-driven inner and outer ambiguity sets based on real data and utilize globalized distributionally robust (GDR) optimization framework to handle uncertainty. Compared with classical distributionally robust optimization, it allows for controllable violations of constraints in the outer ambiguity set. A data-driven globalized distributionally robust WtESC (GDR-WtESC) model is developed, and transformed into an equivalent mixed integer linear programming model according to duality theory. The computational results of real case indicate that <span><math><mrow><mo>(</mo><mi>i</mi><mo>)</mo></mrow></math></span> There is a conflict between economic and environmental objectives, and decision-makers can prioritize them based on their own preferences. <span><math><mrow><mo>(</mo><mi>i</mi><mi>i</mi><mo>)</mo></mrow></math></span> The tolerance level for constraint violation has a positive impact on the total cost. Specifically, the increase of tolerance level from 0.1 to 0.9 can reduce the optimal cost by 1.07%. <span><math><mrow><mo>(</mo><mi>i</mi><mi>i</mi><mi>i</mi><mo>)</mo></mrow></math></span> The optimal decision of GDR-WtESC model has strong stability and high quality. Compared with the sample average approximation (SAA) model, the variance of the objective value in out of sample experiments decreases by 88.28% on average, and the average cost decreases by 0.55%. The SAA method can address the uncertainty, but cannot handle constraint violations in realistic. Thus, for decision makers who are sensitive to distributional ambiguity, the GDR method is recommended for WtESC problem, because it enhances the robustness and reduces conservatism.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"204 ","pages":"Article 109397"},"PeriodicalIF":3.9,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154737","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}

{"title":"Online parameter estimation and model maintenance using parameter-aware physics-informed neural network","authors":"Devavrat Thosar ,&nbsp;Abhijit Bhakte ,&nbsp;Zukui Li ,&nbsp;Rajagopalan Srinivasan ,&nbsp;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}

{"title":"Deep reinforcement learning-based thermal management of battery subpack in electric vehicle","authors":"Sanghoon Shin,&nbsp;Dabin Jeong,&nbsp;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}

{"title":"Optimal experiments for hybrid modeling of methanol synthesis kinetics","authors":"Lothar Kaps ,&nbsp;Johannes Leipold ,&nbsp;Christoph Plate ,&nbsp;Carl Julius Martensen ,&nbsp;Wieland Kortuz ,&nbsp;Andreas Seidel-Morgenstern ,&nbsp;Achim Kienle ,&nbsp;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/Al₂O₃ 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}

{"title":"Behavioral strategies evolution of stakeholders for wastewater recycling in eco-industrial parks under financial constraints","authors":"Kaixuan Zhang ,&nbsp;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}

{"title":"A risk-averse two-stage stochastic programming for biomass supply chain planning problem","authors":"Bilge Bilgen ,&nbsp;Halil Akbaş ,&nbsp;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}

{"title":"Reinforcement learning-guided two-stage optimization framework for multi-product batch scheduling","authors":"Jiawen Zhu ,&nbsp;Wenli Du ,&nbsp;Chen Fan ,&nbsp;Muyi Huang ,&nbsp;Chuan Wang ,&nbsp;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}