Electronic waste management presents a pressing global challenge. This paper proposes the implementation of blockchain technology as a crucial facilitator for closed-loop waste management systems, leveraging its established effectiveness across various phases of the waste lifecycle. The research investigates the impact of manufacturers' competitiveness on the electronic waste recycling and collection market, with the primary goal of minimizing natural resource depletion. Specifically, this study analyzes electronic component pricing strategies within both traditional and blockchain-enabled closed-loop supply chains (CLSCs) under conditions of market competition and industry restructuring. The problem considers the interactions between manufacturers, blockchain service provider (BSP), and customers, focusing on two distinct CLSCs, each involving a single manufacturer producing a unique product. Key decision variables encompass pricing strategies and the extent of blockchain technology adoption. The problem is addressed through the development of two decentralized and cooperative scenarios. The results indicate that collaboration between the original manufacturer and the BSP yields the most competitive product selling price. Furthermore, the second scenario achieves the highest degree of blockchain technology implementation through cooperative revenue-sharing agreements between the originating manufacturer and the BSP.
{"title":"A game-theoretic approach to pricing electronic components in two traditional and blockchain-based closed-loop supply chains under refurbishment and competition: A case study of mobile phones","authors":"Hedieh Nazemzadegan , Morteza Rasti-Barzoki , Mohammad-Bagher Jamali , Jörn Altmann","doi":"10.1016/j.compchemeng.2026.109562","DOIUrl":"10.1016/j.compchemeng.2026.109562","url":null,"abstract":"<div><div>Electronic waste management presents a pressing global challenge. This paper proposes the implementation of blockchain technology as a crucial facilitator for closed-loop waste management systems, leveraging its established effectiveness across various phases of the waste lifecycle. The research investigates the impact of manufacturers' competitiveness on the electronic waste recycling and collection market, with the primary goal of minimizing natural resource depletion. Specifically, this study analyzes electronic component pricing strategies within both traditional and blockchain-enabled closed-loop supply chains (CLSCs) under conditions of market competition and industry restructuring. The problem considers the interactions between manufacturers, blockchain service provider (BSP), and customers, focusing on two distinct CLSCs, each involving a single manufacturer producing a unique product. Key decision variables encompass pricing strategies and the extent of blockchain technology adoption. The problem is addressed through the development of two decentralized and cooperative scenarios. The results indicate that collaboration between the original manufacturer and the BSP yields the most competitive product selling price. Furthermore, the second scenario achieves the highest degree of blockchain technology implementation through cooperative revenue-sharing agreements between the originating manufacturer and the BSP.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"208 ","pages":"Article 109562"},"PeriodicalIF":3.9,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170558","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 : 2026-05-01Epub Date: 2026-02-09DOI: 10.1016/j.compchemeng.2026.109591
Philipp Glücker , Sonja H.M. Germscheid , Ariana Y. Ojeda-Paredes , Andrea Benigni , Manuel Dahmen , Thiemo Pesch
Demand response of industrial processes generally accounts for active power, but not reactive power which grows in importance for balancing local voltage levels in future electricity grids. We present an optimization-based approach to integrate reactive power into demand response scheduling and derive first estimates on the arising potentials. To this end, we extend a resource-task network scheduling model to account for the reactive power of electrically-powered process tasks, local power converters, and the local power grid. As an illustrative example, we study the multi-step copper production. We find a large achievable range of reactive power provision without compromising production volume or operating cost. Furthermore, we demonstrate how reactive power could be provided as an ancillary service by following a signal. Our results show that penalties or additional investment in compensation devices for power factor correction can be avoided through reactive power control of local power converters. Moreover, we demonstrate that industrial processes with sufficient capacity can alleviate voltage problems in transmission grids. Our work therefore lays the groundwork towards determining the reactive power scheduling potential of power-intensive production processes, and showcases its potential support for the voltage stability of future power grids.
{"title":"Unlocking reactive power potential of industrial processes for voltage support through scheduling optimization","authors":"Philipp Glücker , Sonja H.M. Germscheid , Ariana Y. Ojeda-Paredes , Andrea Benigni , Manuel Dahmen , Thiemo Pesch","doi":"10.1016/j.compchemeng.2026.109591","DOIUrl":"10.1016/j.compchemeng.2026.109591","url":null,"abstract":"<div><div>Demand response of industrial processes generally accounts for active power, but not reactive power which grows in importance for balancing local voltage levels in future electricity grids. We present an optimization-based approach to integrate reactive power into demand response scheduling and derive first estimates on the arising potentials. To this end, we extend a resource-task network scheduling model to account for the reactive power of electrically-powered process tasks, local power converters, and the local power grid. As an illustrative example, we study the multi-step copper production. We find a large achievable range of reactive power provision without compromising production volume or operating cost. Furthermore, we demonstrate how reactive power could be provided as an ancillary service by following a signal. Our results show that penalties or additional investment in compensation devices for power factor correction can be avoided through reactive power control of local power converters. Moreover, we demonstrate that industrial processes with sufficient capacity can alleviate voltage problems in transmission grids. Our work therefore lays the groundwork towards determining the reactive power scheduling potential of power-intensive production processes, and showcases its potential support for the voltage stability of future power grids.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"208 ","pages":"Article 109591"},"PeriodicalIF":3.9,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170562","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 : 2026-05-01Epub Date: 2026-02-06DOI: 10.1016/j.compchemeng.2026.109586
Dian Ning Chia, Fanyi Duanmu, Eva Sorensen
Optimal design of dynamic processes is far more challenging than steady-state optimization due to the added complexity of time, leading to a highly complex optimization problem. If more than one possible design or operation is also to be considered, a dynamic superstructure approach is more efficient than considering a series of individual dynamic optimizations. This work proposes a four-step methodology to optimize a dynamic chemical process, here applied to high-performance liquid chromatography (HPLC), based on a superstructure approach. HPLC is commonly used to separate valuable pharmaceutical products, normally based on a single-column elution process, although the basic operation can be improved by considering recycling. A superstructure model for a single recycling HPLC column is introduced, capable of handling the conventional elution policy as well as three recycling policies — conventional recycling, peak shaving (PS), and peak shaving with multiple feed injection (PS-MFI). The superstructure methodology is found to be capable of identifying the optimal operating policy for different objective functions, and can save over 60% of the CPU time when compared to the total time needed to carry out individual optimizations for each operating policy.
{"title":"Superstructure modeling and optimization of dynamic processes applied to high-performance liquid chromatography with recycling","authors":"Dian Ning Chia, Fanyi Duanmu, Eva Sorensen","doi":"10.1016/j.compchemeng.2026.109586","DOIUrl":"10.1016/j.compchemeng.2026.109586","url":null,"abstract":"<div><div>Optimal design of dynamic processes is far more challenging than steady-state optimization due to the added complexity of time, leading to a highly complex optimization problem. If more than one possible design or operation is also to be considered, a dynamic superstructure approach is more efficient than considering a series of individual dynamic optimizations. This work proposes a four-step methodology to optimize a dynamic chemical process, here applied to high-performance liquid chromatography (HPLC), based on a superstructure approach. HPLC is commonly used to separate valuable pharmaceutical products, normally based on a single-column elution process, although the basic operation can be improved by considering recycling. A superstructure model for a single recycling HPLC column is introduced, capable of handling the conventional elution policy as well as three recycling policies — conventional recycling, peak shaving (PS), and peak shaving with multiple feed injection (PS-MFI). The superstructure methodology is found to be capable of identifying the optimal operating policy for different objective functions, and can save over 60% of the CPU time when compared to the total time needed to carry out individual optimizations for each operating policy.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"208 ","pages":"Article 109586"},"PeriodicalIF":3.9,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170559","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 : 2026-05-01Epub Date: 2026-01-20DOI: 10.1016/j.compchemeng.2026.109572
Yoochan Moon , Seung-Tae Han , Ji-Beob Kim , Choongsub Yeom , Duhwan Mun
This study presents a hybrid approach for the automated recognition and classification of line objects in piping and instrumentation diagrams (P&IDs), with the goal of supporting the digital transformation of chemical process design and operation. By integrating Deep Learning (DL) techniques with rule-based methods, the proposed approach extracts flow and signal paths from legacy P&ID images, enabling applications such as process simulation, safety verification, and control logic validation. The approach consists of two stages. In the first stage, all line objects in a P&ID are detected and categorized into lines with special signs and continuous lines. A DL model identifies directional arrows and determines the overall flow structure. In the second stage, the continuous lines are further classified into dimension, extension, and leader lines using the rule-based algorithms, according to their functional characteristics. The method was tested on 30 P&ID sheets from Project A and two from Project B. Initially, the model trained on Project A data achieved precision and recall rates of 95.02% and 93.09%, respectively. On Project B, the performance dropped to 88.92% and 84.76% due to domain shift. After applying transfer learning using the four additional Project B sheets, the performance improved to 95.32% precision and 91.55% recall. These results demonstrate the potential of the proposed approach for accurate and scalable conversion of P&ID data into structured formats, contributing to smart plant design and engineering data integration.
{"title":"Hybrid approach for comprehensive recognition of line objects contained in high-density piping and instrumentation diagrams using deep learning and rules","authors":"Yoochan Moon , Seung-Tae Han , Ji-Beob Kim , Choongsub Yeom , Duhwan Mun","doi":"10.1016/j.compchemeng.2026.109572","DOIUrl":"10.1016/j.compchemeng.2026.109572","url":null,"abstract":"<div><div>This study presents a hybrid approach for the automated recognition and classification of line objects in piping and instrumentation diagrams (P&IDs), with the goal of supporting the digital transformation of chemical process design and operation. By integrating Deep Learning (DL) techniques with rule-based methods, the proposed approach extracts flow and signal paths from legacy P&ID images, enabling applications such as process simulation, safety verification, and control logic validation. The approach consists of two stages. In the first stage, all line objects in a P&ID are detected and categorized into lines with special signs and continuous lines. A DL model identifies directional arrows and determines the overall flow structure. In the second stage, the continuous lines are further classified into dimension, extension, and leader lines using the rule-based algorithms, according to their functional characteristics. The method was tested on 30 P&ID sheets from Project A and two from Project B. Initially, the model trained on Project A data achieved precision and recall rates of 95.02% and 93.09%, respectively. On Project B, the performance dropped to 88.92% and 84.76% due to domain shift. After applying transfer learning using the four additional Project B sheets, the performance improved to 95.32% precision and 91.55% recall. These results demonstrate the potential of the proposed approach for accurate and scalable conversion of P&ID data into structured formats, contributing to smart plant design and engineering data integration.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"208 ","pages":"Article 109572"},"PeriodicalIF":3.9,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075818","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 : 2026-05-01Epub Date: 2026-01-25DOI: 10.1016/j.compchemeng.2026.109583
Wentao Du , Tingting Liu , Zicheng Meng , Muhammad Waqas Yaqub , Baofeng Wang , Xizhong Chen
Stirred tanks are extensively employed in various industries to realize efficient mixing processes. In this work, an optimization framework integrating the Kolmogorov–Arnold network (KAN) surrogate model and the non-dominated sorting genetic algorithm (NSGA-II) is developed for the geometric design of unbaffled stirred-tank impellers. Three-dimensional computational fluid dynamics (CFD) simulations were conducted over broad parametric ranges of impeller blade length, width, number, and tilt angle to produce datasets of the flow fields. These datasets were subsequently employed to train the KAN surrogate model, enabling rapid and accurate prediction of the three-dimensional flow fields. The root-mean-square (RMS) of static pressure and mixing intensity (MI) were calculated from the surrogate-predicted data and served as dual objective functions for NSGA-II optimization. The optimal impeller geometry identified by the KAN–NSGA-II framework was further validated, revealing a significant reduction in RMS pressure and an enhancement in MI relative to the baseline design. The result shows that combining data-driven surrogate modeling with evolutionary optimization provides a robust and efficient strategy for the performance-driven geometric optimization of industrial mixing equipment.
{"title":"Kolmogorov–Arnold network-assisted multi-objective approach for design and optimization of unbaffled stirred tanks","authors":"Wentao Du , Tingting Liu , Zicheng Meng , Muhammad Waqas Yaqub , Baofeng Wang , Xizhong Chen","doi":"10.1016/j.compchemeng.2026.109583","DOIUrl":"10.1016/j.compchemeng.2026.109583","url":null,"abstract":"<div><div>Stirred tanks are extensively employed in various industries to realize efficient mixing processes. In this work, an optimization framework integrating the Kolmogorov–Arnold network (KAN) surrogate model and the non-dominated sorting genetic algorithm (NSGA-II) is developed for the geometric design of unbaffled stirred-tank impellers. Three-dimensional computational fluid dynamics (CFD) simulations were conducted over broad parametric ranges of impeller blade length, width, number, and tilt angle to produce datasets of the flow fields. These datasets were subsequently employed to train the KAN surrogate model, enabling rapid and accurate prediction of the three-dimensional flow fields. The root-mean-square (RMS) of static pressure and mixing intensity (MI) were calculated from the surrogate-predicted data and served as dual objective functions for NSGA-II optimization. The optimal impeller geometry identified by the KAN–NSGA-II framework was further validated, revealing a significant reduction in RMS pressure and an enhancement in MI relative to the baseline design. The result shows that combining data-driven surrogate modeling with evolutionary optimization provides a robust and efficient strategy for the performance-driven geometric optimization of industrial mixing equipment.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"208 ","pages":"Article 109583"},"PeriodicalIF":3.9,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075821","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 : 2026-04-01Epub Date: 2025-12-30DOI: 10.1016/j.compchemeng.2025.109544
Ellis R. Crabtree , Dimitris G. Giovanis , Nikolaos Evangelou , Juan M. Bello-Rivas , Ioannis G. Kevrekidis
In dynamical systems characterized by separation of time scales, the approximation of so called “slow manifolds”, on which the long term dynamics lie, is a useful step for model reduction. Initializing on such slow manifolds is a useful step in modeling, since it circumvents fast transients, and is crucial in multiscale algorithms (like the equation-free approach) alternating between fine scale (fast) and coarser scale (slow) simulations. In a similar spirit, when one studies the infinite time dynamics of systems depending on parameters, the system attractors (e.g., its steady states) lie on bifurcation diagrams (curves for one-parameter continuation, and more generally, on manifolds in state parameter space. Sampling these manifolds gives us representative attractors (here, steady states of ODEs or PDEs) at different parameter values. Algorithms for the systematic construction of these manifolds (slow manifolds, bifurcation diagrams) are required parts of the “traditional” numerical nonlinear dynamics toolkit.
In more recent years, as the field of Machine Learning develops, conditional score-based generative models (cSGMs) have been demonstrated to exhibit remarkable capabilities in generating plausible data from target distributions that are conditioned on some given label. It is tempting to exploit such generative models to produce samples of data distributions (points on a slow manifold, steady states on a bifurcation surface) conditioned on (consistent with) some quantity of interest (QoI, observable). In this work, we present a framework for using cSGMs to quickly (a) initialize on a low-dimensional (reduced-order) slow manifold of a multi-time-scale system consistent with desired value(s) of a QoI (a “label”) on the manifold, and (b) approximate steady states in a bifurcation diagram consistent with a (new, out-of-sample) parameter value. This conditional sampling can help uncover the geometry of the reduced slow-manifold and/or approximately “fill in” missing segments of steady states in a bifurcation diagram. The quantity of interest, which determines how the sampling is conditioned, is either known a priori or identified using manifold learning-based dimensionality reduction techniques applied to the training data.
{"title":"Generative learning for slow manifolds and bifurcation diagrams","authors":"Ellis R. Crabtree , Dimitris G. Giovanis , Nikolaos Evangelou , Juan M. Bello-Rivas , Ioannis G. Kevrekidis","doi":"10.1016/j.compchemeng.2025.109544","DOIUrl":"10.1016/j.compchemeng.2025.109544","url":null,"abstract":"<div><div>In dynamical systems characterized by separation of time scales, the approximation of so called “slow manifolds”, on which the long term dynamics lie, is a useful step for model reduction. Initializing on such slow manifolds is a useful step in modeling, since it circumvents fast transients, and is crucial in multiscale algorithms (like the equation-free approach) alternating between fine scale (fast) and coarser scale (slow) simulations. In a similar spirit, when one studies the infinite time dynamics of systems depending on parameters, the system attractors (e.g., its steady states) lie on bifurcation diagrams (curves for one-parameter continuation, and more generally, on manifolds in state <span><math><mo>×</mo></math></span> parameter space. Sampling these manifolds gives us representative attractors (here, steady states of ODEs or PDEs) at different parameter values. Algorithms for the systematic construction of these manifolds (slow manifolds, bifurcation diagrams) are required parts of the “traditional” numerical nonlinear dynamics toolkit.</div><div>In more recent years, as the field of Machine Learning develops, conditional score-based generative models (cSGMs) have been demonstrated to exhibit remarkable capabilities in generating plausible data from target distributions that are conditioned on some given label. It is tempting to exploit such generative models to produce samples of data distributions (points on a slow manifold, steady states on a bifurcation surface) conditioned on (consistent with) some quantity of interest (QoI, observable). In this work, we present a framework for using cSGMs to quickly (a) initialize on a low-dimensional (reduced-order) slow manifold of a multi-time-scale system consistent with desired value(s) of a QoI (a “label”) on the manifold, and (b) approximate steady states in a bifurcation diagram consistent with a (new, out-of-sample) parameter value. This conditional sampling can help uncover the geometry of the reduced slow-manifold and/or approximately “fill in” missing segments of steady states in a bifurcation diagram. The quantity of interest, which determines how the sampling is conditioned, is either known <em>a priori</em> or identified using manifold learning-based dimensionality reduction techniques applied to the training data.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"207 ","pages":"Article 109544"},"PeriodicalIF":3.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923461","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 : 2026-04-01Epub Date: 2026-01-09DOI: 10.1016/j.compchemeng.2026.109563
Xianming Lang , Yibing Wang , Jiangtao Cao , Qiang Liu , Edith C.H. Ngai
Urban water distribution networks face significant challenges from pipeline leakage, which leads to water loss and operational inefficiencies. Existing data-driven detection methods often neglect inherent hydraulic principles, resulting in poor model generalizability and a lack of quantitative leakage severity assessment. To address these issues, this paper proposes a physics-informed graph transformer fusion (PI-GTF) framework that integrates hydraulic mechanisms with deep learning for leakage detection and grading. The model embeds hydraulic governing equations and signal propagation rules into a graph convolutional network (GCN) and a transformer to capture spatial pipeline topology and long-term temporal dependencies of leakage signals. A novel physics-aware hierarchical adversarial gating attention (PHAGA) module is designed to align and fuse these heterogeneous features effectively. Furthermore, a five-level leakage grading system is established by combining hydraulic model outputs with sensor-based features such as pressure fluctuations and abnormal flow durations. The experimental results of a high-fidelity simulation model of Shenyang’s water network show that PI-GTF outperforms existing methods in terms of accuracy, precision, and F1 score, with zero cross-level misclassification. Migration tests on real residential networks demonstrate strong generalizability, with performance degradation within 2%. This study provides a reliable dual-driven framework for end-to-end leakage management and supports intelligent decision-making in water network maintenance.
{"title":"Physics-informed graph transformer fusion for leakage detection and grading in water distribution networks","authors":"Xianming Lang , Yibing Wang , Jiangtao Cao , Qiang Liu , Edith C.H. Ngai","doi":"10.1016/j.compchemeng.2026.109563","DOIUrl":"10.1016/j.compchemeng.2026.109563","url":null,"abstract":"<div><div>Urban water distribution networks face significant challenges from pipeline leakage, which leads to water loss and operational inefficiencies. Existing data-driven detection methods often neglect inherent hydraulic principles, resulting in poor model generalizability and a lack of quantitative leakage severity assessment. To address these issues, this paper proposes a physics-informed graph transformer fusion (PI-GTF) framework that integrates hydraulic mechanisms with deep learning for leakage detection and grading. The model embeds hydraulic governing equations and signal propagation rules into a graph convolutional network (GCN) and a transformer to capture spatial pipeline topology and long-term temporal dependencies of leakage signals. A novel physics-aware hierarchical adversarial gating attention (PHAGA) module is designed to align and fuse these heterogeneous features effectively. Furthermore, a five-level leakage grading system is established by combining hydraulic model outputs with sensor-based features such as pressure fluctuations and abnormal flow durations. The experimental results of a high-fidelity simulation model of Shenyang’s water network show that PI-GTF outperforms existing methods in terms of accuracy, precision, and F1 score, with zero cross-level misclassification. Migration tests on real residential networks demonstrate strong generalizability, with performance degradation within 2%. This study provides a reliable dual-driven framework for end-to-end leakage management and supports intelligent decision-making in water network maintenance.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"207 ","pages":"Article 109563"},"PeriodicalIF":3.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974184","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 : 2026-04-01Epub Date: 2025-12-07DOI: 10.1016/j.compchemeng.2025.109517
Muhammad Shoaib , Rongjian Yu , Hassan Ali , Amin Ullah Khan , Ahmad Fraz
Globalization makes the supply chain for toxic materials (TOM’s) complex and extensive, affecting their reliability and potentially causing disruptions across various operations. The toxic materials supply chain is a delicate industrial operation that requires significant attention. Integrating the circular economy and blockchain technology into the supply chain provides a substantial solution to this issue, as previous literature lacks a secure and efficient digital system. To achieve this, the state-of-the-art Technology-Based Intervention (TBI) was a cutting-edge methodology that often incorporated the latest technologies and tools, enabling more innovative research validation and efficient problem-solving. This study initially explored the hidden potential of blockchain and the detailed process of the circular supply chain, aiming to provide deep insights. Later, a blockchain-based circular-economy taxonomy model was proposed to enable a secure and efficient toxic-materials supply chain. This model comprises a blockchain design layout, with its key features implemented in the toxic materials circular supply chain (CSC) processes, aiming to achieve a secure and efficient supply chain by addressing key performance indicators (KPIs). Moreover, this paper examines YongTaiyun (永泰运) Chemical Logistics Co., Ltd. as a real-world case study to explore how conventional chemical logistics companies are transforming and upgrading in the digital era by integrating blockchain technology. This approach enables rigorous analysis of complex real-world phenomena, particularly the nexus of technology and industry practices. The results illustrate that blockchain mitigates toxic materials supply chain risks through digital automation and promotes zero waste by reusing, recycling, reprocessing, and remanufacturing used products. It enables governance agencies, traffic controllers, and transportation management to develop standards and policies to eliminate risks related to toxic chemicals, especially nuclear reactors, radiation leakage, irregularities, and illegal access, while providing secure and efficient documentation, handling, storage, and transportation systems. This research provides a comprehensive understanding and roadmap for academic scholars and researchers, aiming to help industrial practitioners, policymakers, and authorised agencies implement blockchain technology and develop informed rules on secure and efficient practices.
{"title":"A blockchain-based circular economy taxonomy model for secure & efficient toxic materials supply chain: A technology-based intervention and case study approach","authors":"Muhammad Shoaib , Rongjian Yu , Hassan Ali , Amin Ullah Khan , Ahmad Fraz","doi":"10.1016/j.compchemeng.2025.109517","DOIUrl":"10.1016/j.compchemeng.2025.109517","url":null,"abstract":"<div><div>Globalization makes the supply chain for toxic materials (TOM’s) complex and extensive, affecting their reliability and potentially causing disruptions across various operations. The toxic materials supply chain is a delicate industrial operation that requires significant attention. Integrating the circular economy and blockchain technology into the supply chain provides a substantial solution to this issue, as previous literature lacks a secure and efficient digital system. To achieve this, the state-of-the-art Technology-Based Intervention (TBI) was a cutting-edge methodology that often incorporated the latest technologies and tools, enabling more innovative research validation and efficient problem-solving. This study initially explored the hidden potential of blockchain and the detailed process of the circular supply chain, aiming to provide deep insights. Later, a blockchain-based circular-economy taxonomy model was proposed to enable a secure and efficient toxic-materials supply chain. This model comprises a blockchain design layout, with its key features implemented in the toxic materials circular supply chain (CSC) processes, aiming to achieve a secure and efficient supply chain by addressing key performance indicators (KPIs). Moreover, this paper examines YongTaiyun (永泰运) Chemical Logistics Co., Ltd. as a real-world case study to explore how conventional chemical logistics companies are transforming and upgrading in the digital era by integrating blockchain technology. This approach enables rigorous analysis of complex real-world phenomena, particularly the nexus of technology and industry practices. The results illustrate that blockchain mitigates toxic materials supply chain risks through digital automation and promotes zero waste by reusing, recycling, reprocessing, and remanufacturing used products. It enables governance agencies, traffic controllers, and transportation management to develop standards and policies to eliminate risks related to toxic chemicals, especially nuclear reactors, radiation leakage, irregularities, and illegal access, while providing secure and efficient documentation, handling, storage, and transportation systems. This research provides a comprehensive understanding and roadmap for academic scholars and researchers, aiming to help industrial practitioners, policymakers, and authorised agencies implement blockchain technology and develop informed rules on secure and efficient practices.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"207 ","pages":"Article 109517"},"PeriodicalIF":3.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923401","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}
The adoption of continuous pharmaceutical manufacturing has driven increased use of modeling, simulation, and advanced process control strategies. Artificial intelligence (AI) model-based approaches, like neural network predictive control (NNPC), offer advantages in providing insights, predictions, and process adjustments. However, evaluating the credibility of such models and accurately quantifying their impact on product quality remains challenging. In this study, a digital twin model of a continuous direct compression (CDC) line was developed based on residence time distribution theory. A two-layer neural network model was trained using data from the digital twin to predict system outputs. The NNPC model combined the trained neural network with an optimization block to adjust control signals and minimize tracking error and control effort. A proportional-integral-derivative (PID) controller was also developed for comparison. The developed neural network model accurately represented the dynamics of the nonlinear system. The tuned NNPC outperformed PID in setpoint tracking (zero overshoot, shorter settling times) and disturbance rejection (≤1.6% peak deviation, settling time of zero) for ±20% and ±50% changes. In conclusion, the NNPC model demonstrated remarkable performance in setpoint tracking and disturbance rejection for the simulated CDC line, underscoring the potential of AI-based control strategies in enhancing product quality and regulatory assessment.
{"title":"Advanced control of continuous pharmaceutical manufacturing processes: A case study on the application of artificial neural network for predictive control of a CDC line","authors":"Jianan Zhao, Geng Tian, Wei Yang, Das Jayanti, Abdollah Koolivand, Xiaoming Xu","doi":"10.1016/j.compchemeng.2026.109560","DOIUrl":"10.1016/j.compchemeng.2026.109560","url":null,"abstract":"<div><div>The adoption of continuous pharmaceutical manufacturing has driven increased use of modeling, simulation, and advanced process control strategies. Artificial intelligence (AI) model-based approaches, like neural network predictive control (NNPC), offer advantages in providing insights, predictions, and process adjustments. However, evaluating the credibility of such models and accurately quantifying their impact on product quality remains challenging. In this study, a digital twin model of a continuous direct compression (CDC) line was developed based on residence time distribution theory. A two-layer neural network model was trained using data from the digital twin to predict system outputs. The NNPC model combined the trained neural network with an optimization block to adjust control signals and minimize tracking error and control effort. A proportional-integral-derivative (PID) controller was also developed for comparison. The developed neural network model accurately represented the dynamics of the nonlinear system. The tuned NNPC outperformed PID in setpoint tracking (zero overshoot, shorter settling times) and disturbance rejection (≤1.6% peak deviation, settling time of zero) for ±20% and ±50% changes. In conclusion, the NNPC model demonstrated remarkable performance in setpoint tracking and disturbance rejection for the simulated CDC line, underscoring the potential of AI-based control strategies in enhancing product quality and regulatory assessment.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"207 ","pages":"Article 109560"},"PeriodicalIF":3.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974224","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 : 2026-04-01Epub Date: 2025-12-30DOI: 10.1016/j.compchemeng.2025.109547
Guoxi He , Jing Tian , Dezhi Tang , Fei Zhao , Shuhua Li , Chao Li , Kexi Liao , XiaoFei Chen , Wen Yang
Accurate prediction of corrosion rates is of great significance for ensuring pipeline integrity and operational safety. This study proposes a novel hybrid prediction model—GAN-QPSO-XGBoost—which integrates a Generative Adversarial Network (GAN), Quantum-behaved Particle Swarm Optimization (QPSO), and the XGBoost algorithm. This study used GAN to augment 100 field data sets with 50 high-quality synthetic samples, forming an enhanced dataset of 150. The Kolmogorov-Smirnov test showed p greater than 0.05 and MAPE around 5%, confirming the synthetic data’s statistical consistency and numerical reliability. QPSO, by introducing quantum behavior mechanisms, effectively overcomes the issues of local optima and premature convergence commonly found in traditional optimization algorithms, further optimizing the predictive performance of XGBoost.To comprehensively evaluate model performance, this study adopts multiple standard metrics for validation and introduces the SHAP (Shapley Additive exPlanations) method to enhance model interpretability. Experimental results demonstrate that the GAN-QPSO-XGBoost hybrid model significantly outperforms existing benchmark models in corrosion rate prediction, with the following evaluation metrics: R² = 0.922, MAPE = 1.24%, MAE = 0.036, MSE = 0.0018, and RMSE = 0.042, fully proving its excellent predictive accuracy and stability. SHAP analysis further reveals that temperature, liquid holdup, flow velocity, CO2 partial pressure, gas-wall shear stress, and liquid-wall shear stress are the most significant factors influencing corrosion rate.In conclusion, the GAN-QPSO-XGBoost hybrid model not only significantly improves the accuracy and reliability of corrosion rate prediction but also provides a scientific basis and operational guidance for pipeline maintenance, safety assessment, and protection strategy formulation in practical engineering.
{"title":"Research on natural gas pipeline corrosion prediction by integrating extreme gradient boosting and generative adversarial network","authors":"Guoxi He , Jing Tian , Dezhi Tang , Fei Zhao , Shuhua Li , Chao Li , Kexi Liao , XiaoFei Chen , Wen Yang","doi":"10.1016/j.compchemeng.2025.109547","DOIUrl":"10.1016/j.compchemeng.2025.109547","url":null,"abstract":"<div><div>Accurate prediction of corrosion rates is of great significance for ensuring pipeline integrity and operational safety. This study proposes a novel hybrid prediction model—GAN-QPSO-XGBoost—which integrates a Generative Adversarial Network (GAN), Quantum-behaved Particle Swarm Optimization (QPSO), and the XGBoost algorithm. This study used GAN to augment 100 field data sets with 50 high-quality synthetic samples, forming an enhanced dataset of 150. The Kolmogorov-Smirnov test showed p greater than 0.05 and MAPE around 5%, confirming the synthetic data’s statistical consistency and numerical reliability. QPSO, by introducing quantum behavior mechanisms, effectively overcomes the issues of local optima and premature convergence commonly found in traditional optimization algorithms, further optimizing the predictive performance of XGBoost.To comprehensively evaluate model performance, this study adopts multiple standard metrics for validation and introduces the SHAP (Shapley Additive exPlanations) method to enhance model interpretability. Experimental results demonstrate that the GAN-QPSO-XGBoost hybrid model significantly outperforms existing benchmark models in corrosion rate prediction, with the following evaluation metrics: R² = 0.922, MAPE = 1.24%, MAE = 0.036, MSE = 0.0018, and RMSE = 0.042, fully proving its excellent predictive accuracy and stability. SHAP analysis further reveals that temperature, liquid holdup, flow velocity, CO<sub>2</sub> partial pressure, gas-wall shear stress, and liquid-wall shear stress are the most significant factors influencing corrosion rate.In conclusion, the GAN-QPSO-XGBoost hybrid model not only significantly improves the accuracy and reliability of corrosion rate prediction but also provides a scientific basis and operational guidance for pipeline maintenance, safety assessment, and protection strategy formulation in practical engineering.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"207 ","pages":"Article 109547"},"PeriodicalIF":3.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974183","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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