Pub Date : 2026-04-01Epub Date: 2025-12-24DOI: 10.1016/j.compchemeng.2025.109540
Daniel Mayfrank , Kayra Dernek , Laura Lang , Alexander Mitsos , Manuel Dahmen
With our recently proposed method based on reinforcement learning (Mayfrank et al., 2024), Koopman surrogate models can be trained for optimal performance in specific (economic) nonlinear model predictive control ((e)NMPC) applications. So far, our method has exclusively been demonstrated on a small-scale case study. Herein, we show that our method scales well to a more challenging demand response case study built on a large-scale model of a single-product (nitrogen) air separation unit. Across all numerical experiments, we assume observability of only a few realistically measurable plant variables. Compared to a purely system identification-based Koopman eNMPC, which generates small economic savings but frequently violates constraints, our method delivers similar economic performance while avoiding constraint violations.
{"title":"End-to-end reinforcement learning of Koopman models for eNMPC of an air separation unit","authors":"Daniel Mayfrank , Kayra Dernek , Laura Lang , Alexander Mitsos , Manuel Dahmen","doi":"10.1016/j.compchemeng.2025.109540","DOIUrl":"10.1016/j.compchemeng.2025.109540","url":null,"abstract":"<div><div>With our recently proposed method based on reinforcement learning (Mayfrank et al., 2024), Koopman surrogate models can be trained for optimal performance in specific (economic) nonlinear model predictive control ((e)NMPC) applications. So far, our method has exclusively been demonstrated on a small-scale case study. Herein, we show that our method scales well to a more challenging demand response case study built on a large-scale model of a single-product (nitrogen) air separation unit. Across all numerical experiments, we assume observability of only a few realistically measurable plant variables. Compared to a purely system identification-based Koopman eNMPC, which generates small economic savings but frequently violates constraints, our method delivers similar economic performance while avoiding constraint violations.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"207 ","pages":"Article 109540"},"PeriodicalIF":3.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923402","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: 2026-01-06DOI: 10.1016/j.compchemeng.2026.109558
Jinqiu Hu , Mingjun Ma , Laibin Zhang
For pipeline corrosion-rate prediction in refinery units characterized by scarce high-corrosion-rate samples, numerous operating variables, and strong temporal perturbations in process parameters, this study proposes a hybrid framework that integrates structural diagnosis, feature selection, and improved ensemble learning. First, kernel principal component analysis (KPCA) is employed to identify nonlinear and redundant structures in the data, and a subset of operating-condition features with high relevance and low redundancy is constructed using mutual information–minimum redundancy maximum relevance (MI–mRMR). Then, Dropout meets Multiple Additive Regression Trees (DART) is incorporated into XGBoost to mitigate overfitting, while a hybrid dynamic perturbation strategy grey wolf optimizer (HDPSGWO) is used to perform global optimization of the hyperparameters. Using multi-loop data from the purification section of a sulfuric acid alkylation unit as a case study, the proposed model achieves RMSE=0.005876, MAE=0.004282, and R²=0.9648 on the test set, and maintains the best performance in a systematic comparison against five benchmark models. Based on TreeSHAP, the model interpretation further reveals the dominant factors driving corrosion-rate variations as well as the interval effects between operating parameters and corrosion rate. Reproduction of an engineering corrosion event verifies the early-warning capability of the proposed model. The results demonstrate that the hybrid framework can provide reliable corrosion-rate prediction under complex, non-stationary operating conditions, offering quantitative support for corrosion management and maintenance decision-making in refinery and petrochemical units.
{"title":"Application and interpretability of a hybrid-enhanced XGBoost model for corrosion-rate prediction in alkylation unit piping","authors":"Jinqiu Hu , Mingjun Ma , Laibin Zhang","doi":"10.1016/j.compchemeng.2026.109558","DOIUrl":"10.1016/j.compchemeng.2026.109558","url":null,"abstract":"<div><div>For pipeline corrosion-rate prediction in refinery units characterized by scarce high-corrosion-rate samples, numerous operating variables, and strong temporal perturbations in process parameters, this study proposes a hybrid framework that integrates structural diagnosis, feature selection, and improved ensemble learning. First, kernel principal component analysis (KPCA) is employed to identify nonlinear and redundant structures in the data, and a subset of operating-condition features with high relevance and low redundancy is constructed using mutual information–minimum redundancy maximum relevance (MI–mRMR). Then, Dropout meets Multiple Additive Regression Trees (DART) is incorporated into XGBoost to mitigate overfitting, while a hybrid dynamic perturbation strategy grey wolf optimizer (HDPSGWO) is used to perform global optimization of the hyperparameters. Using multi-loop data from the purification section of a sulfuric acid alkylation unit as a case study, the proposed model achieves RMSE=0.005876, MAE=0.004282, and R²=0.9648 on the test set, and maintains the best performance in a systematic comparison against five benchmark models. Based on TreeSHAP, the model interpretation further reveals the dominant factors driving corrosion-rate variations as well as the interval effects between operating parameters and corrosion rate. Reproduction of an engineering corrosion event verifies the early-warning capability of the proposed model. The results demonstrate that the hybrid framework can provide reliable corrosion-rate prediction under complex, non-stationary operating conditions, offering quantitative support for corrosion management and maintenance decision-making in refinery and petrochemical units.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"207 ","pages":"Article 109558"},"PeriodicalIF":3.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923403","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: 2026-01-13DOI: 10.1016/j.compchemeng.2026.109564
E N Pistikopoulos , Rafiqul Gani
Process Systems Engineering (PSE) is the scientific discipline of integrating scales and components describing the behavior of various systems via mathematical modeling, data analytics, synthesis, design, optimization, monitoring, control, and many more. The emergence of Artificial Intelligence (AI) has provided an opportunity to re-assess the role of data, models and algorithms in the context of the evolving role of PSE. This article provides a critical guide in understanding and unlocking the potential opportunities and synergies that AI can offer empowering the next generation of PSE developments towards truly Augmented Intelligence driven methods and tools.
{"title":"Data, models, algorithms, AI and the role of PSE – the generation next","authors":"E N Pistikopoulos , Rafiqul Gani","doi":"10.1016/j.compchemeng.2026.109564","DOIUrl":"10.1016/j.compchemeng.2026.109564","url":null,"abstract":"<div><div>Process Systems Engineering (PSE) is the scientific discipline of integrating scales and components describing the behavior of various systems via mathematical modeling, data analytics, synthesis, design, optimization, monitoring, control, and many more. The emergence of Artificial Intelligence (AI) has provided an opportunity to re-assess the role of data, models and algorithms in the context of the evolving role of PSE. This article provides a critical guide in understanding and unlocking the potential opportunities and synergies that AI can offer empowering the next generation of PSE developments towards truly Augmented Intelligence driven methods and tools.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"207 ","pages":"Article 109564"},"PeriodicalIF":3.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034930","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: 2026-01-14DOI: 10.1016/j.compchemeng.2026.109567
Xiaofan Zhou, Li Feng, Aihua Zhu, Haoxu Shi
In global supply chain management, optimizing joint inventory-transportation decisions remains a critical challenge. Existing approaches often rely on deterministic assumptions or oversimplified stochastic models, which fail to adequately capture the dynamic uncertainties and multimodal variability inherent in replenishment lead times. This limitation severely restricts the robustness and coordination efficiency of decision policies in real-world complex environments. To address these issues, this paper proposes an uncertainty-aware decision framework, termed Diffusion model with Entropy-guided Multi-Agent Proximal Policy Optimization (DE-MAPPO). Our method employs a diffusion model to generate probabilistic lead time forecasting, leverages Monte Carlo sampling to quantify uncertainty, and introduces an entropy-guided adaptive strategy that enables agents to dynamically adjust inventory and transportation decisions based on forecast confidence. The effectiveness of the proposed framework is validated through experiments conducted in a simulated global chemical supply chain environment. The experimental results demonstrate that DE-MAPPO framework significantly outperforms the baseline methods across key performance metrics.
{"title":"Uncertainty-aware joint inventory-transportation decisions in supply chain: A diffusion model-based multi-agent reinforcement learning approach with lead times estimation","authors":"Xiaofan Zhou, Li Feng, Aihua Zhu, Haoxu Shi","doi":"10.1016/j.compchemeng.2026.109567","DOIUrl":"10.1016/j.compchemeng.2026.109567","url":null,"abstract":"<div><div>In global supply chain management, optimizing joint inventory-transportation decisions remains a critical challenge. Existing approaches often rely on deterministic assumptions or oversimplified stochastic models, which fail to adequately capture the dynamic uncertainties and multimodal variability inherent in replenishment lead times. This limitation severely restricts the robustness and coordination efficiency of decision policies in real-world complex environments. To address these issues, this paper proposes an uncertainty-aware decision framework, termed Diffusion model with Entropy-guided Multi-Agent Proximal Policy Optimization (DE-MAPPO). Our method employs a diffusion model to generate probabilistic lead time forecasting, leverages Monte Carlo sampling to quantify uncertainty, and introduces an entropy-guided adaptive strategy that enables agents to dynamically adjust inventory and transportation decisions based on forecast confidence. The effectiveness of the proposed framework is validated through experiments conducted in a simulated global chemical supply chain environment. The experimental results demonstrate that DE-MAPPO framework significantly outperforms the baseline methods across key performance metrics.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"207 ","pages":"Article 109567"},"PeriodicalIF":3.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974182","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.2026.109559
Maaz Ahmad, Iftekhar A Karimi
Global optimization of large-scale, complex systems such as multi-physics black-box simulations and real-world industrial systems is important but challenging. This work presents a novel Surrogate-Based Optimization framework based on Clustering (SBOC) for global optimization of such systems, which can be used with any surrogate modeling technique. At each iteration, it uses a single surrogate model for the entire domain, employs k-means clustering to identify unexplored domain, and exploits a local region around the surrogate’s optimum to potentially add three new sample points in the domain. SBOC has been tested against sixteen promising benchmarking algorithms using 52 analytical test functions of varying input dimensionalities and shape profiles. It successfully identified a global minimum for most test functions with substantially lower computational effort than other algorithms. It worked especially well on test functions with four or more input variables. It was also among the top six algorithms in approaching a global minimum closely. Overall, SBOC is a robust, reliable, and efficient algorithm for global optimization of box-constrained systems.
{"title":"Surrogate-based optimization via clustering for box-constrained problems","authors":"Maaz Ahmad, Iftekhar A Karimi","doi":"10.1016/j.compchemeng.2026.109559","DOIUrl":"10.1016/j.compchemeng.2026.109559","url":null,"abstract":"<div><div>Global optimization of large-scale, complex systems such as multi-physics black-box simulations and real-world industrial systems is important but challenging. This work presents a novel <u>S</u>urrogate-<u>B</u>ased <u>O</u>ptimization framework based on <u>C</u>lustering (SBOC) for global optimization of such systems, which can be used with any surrogate modeling technique. At each iteration, it uses a single surrogate model for the entire domain, employs k-means clustering to identify unexplored domain, and exploits a local region around the surrogate’s optimum to potentially add three new sample points in the domain. SBOC has been tested against sixteen promising benchmarking algorithms using 52 analytical test functions of varying input dimensionalities and shape profiles. It successfully identified a global minimum for most test functions with substantially lower computational effort than other algorithms. It worked especially well on test functions with four or more input variables. It was also among the top six algorithms in approaching a global minimum closely. Overall, SBOC is a robust, reliable, and efficient algorithm for global optimization of box-constrained systems.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"207 ","pages":"Article 109559"},"PeriodicalIF":3.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974185","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.2025.109546
Xue Xu , Wei Zhao , Dong Lv , Yuanjian Fu , Chaomin Luo , Chengyi Xia
Due to load changes, unit aging, or other causes, industrial processes are in general time variant condition and characterized by nonstationarity, challenging conventional monitoring methods. A manifold-aware stationary subspace and divergence analysis (MSSDA) is proposed for monitoring nonstationary processes, which aims at capturing the underlying low-dimensional representations of data from geometric and statistical perspectives. Specifically, an across-epoch similarity term induced by Gromov-Wasserstein distance is developed to align the manifold structures across different epochs such that MSSDA faithfully explores the intrinsic geometric characteristics of data. An adaptive neighbor strategy is designed to learn the neighborhood relationship among data and tailor appropriate neighbors for each sample with conditions of data density. Afterwards, a maximizing-minimizing divergence analysis is also investigated to match the intra-epoch and inter-epoch statistical information. In this way, the learned reduced-dimensional representations of data provide an in-depth analysis into the operation process, enhancing the monitoring capabilities. To demonstrate its effectiveness, the MSSDA approach is applied to two complicated industrial processes including a wastewater treatment process and a real-world fluid catalytic cracking process.
{"title":"Manifold-aware stationary subspace and divergence analysis for nonstationary process monitoring","authors":"Xue Xu , Wei Zhao , Dong Lv , Yuanjian Fu , Chaomin Luo , Chengyi Xia","doi":"10.1016/j.compchemeng.2025.109546","DOIUrl":"10.1016/j.compchemeng.2025.109546","url":null,"abstract":"<div><div>Due to load changes, unit aging, or other causes, industrial processes are in general time variant condition and characterized by nonstationarity, challenging conventional monitoring methods. A manifold-aware stationary subspace and divergence analysis (MSSDA) is proposed for monitoring nonstationary processes, which aims at capturing the underlying low-dimensional representations of data from geometric and statistical perspectives. Specifically, an across-epoch similarity term induced by Gromov-Wasserstein distance is developed to align the manifold structures across different epochs such that MSSDA faithfully explores the intrinsic geometric characteristics of data. An adaptive neighbor strategy is designed to learn the neighborhood relationship among data and tailor appropriate neighbors for each sample with conditions of data density. Afterwards, a maximizing-minimizing divergence analysis is also investigated to match the intra-epoch and inter-epoch statistical information. In this way, the learned reduced-dimensional representations of data provide an in-depth analysis into the operation process, enhancing the monitoring capabilities. To demonstrate its effectiveness, the MSSDA approach is applied to two complicated industrial processes including a wastewater treatment process and a real-world fluid catalytic cracking process.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"207 ","pages":"Article 109546"},"PeriodicalIF":3.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883379","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: 2025-12-26DOI: 10.1016/j.compchemeng.2025.109538
Artemis Tsochatzidi , Georgios I. Liapis , Francesca Cenci , Magdalini Aroniada , Lazaros G. Papageorgiou
Modern industries rely on advanced modelling techniques to enhance process efficiency, yet the computational complexity of these models often limits their direct use in optimisation. To tackle this issue, surrogate-based approaches for optimising manufacturing flowsheets can be used. In this work, we introduce a multi-objective tree regression approach for surrogate-based optimisation, integrating a multi-target tree regression model to approximate complex process dynamics. The proposed approach can be extended and formulated as a strategic decision-making problem, to reveal optimal trade-offs between conflicting objectives such as yield, process mass intensity, and purity in Pharmaceutical Manufacturing. By combining Pareto-fronts with game-theoretic and/or compromise solutions, the methodology offers a systematic way to define the limits of the feasible space and identify optimal operational strategies in the absence of decision making preferences. The proposed approach enhances interpretability, computational efficiency, and practical applicability, offering a powerful tool for decision-making in pharmaceutical manufacturing and beyond.
{"title":"Surrogate-based multi-objective optimisation via tree regression","authors":"Artemis Tsochatzidi , Georgios I. Liapis , Francesca Cenci , Magdalini Aroniada , Lazaros G. Papageorgiou","doi":"10.1016/j.compchemeng.2025.109538","DOIUrl":"10.1016/j.compchemeng.2025.109538","url":null,"abstract":"<div><div>Modern industries rely on advanced modelling techniques to enhance process efficiency, yet the computational complexity of these models often limits their direct use in optimisation. To tackle this issue, surrogate-based approaches for optimising manufacturing flowsheets can be used. In this work, we introduce a multi-objective tree regression approach for surrogate-based optimisation, integrating a multi-target tree regression model to approximate complex process dynamics. The proposed approach can be extended and formulated as a strategic decision-making problem, to reveal optimal trade-offs between conflicting objectives such as yield, process mass intensity, and purity in Pharmaceutical Manufacturing. By combining Pareto-fronts with game-theoretic and/or compromise solutions, the methodology offers a systematic way to define the limits of the feasible space and identify optimal operational strategies in the absence of decision making preferences. The proposed approach enhances interpretability, computational efficiency, and practical applicability, offering a powerful tool for decision-making in pharmaceutical manufacturing and beyond.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"207 ","pages":"Article 109538"},"PeriodicalIF":3.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923463","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}
This study introduces a unified data-driven feedforward–feedback control framework for a four-column natural gas liquids (NGL) separation system. A soft sensor estimates upstream feed composition and flow disturbances, while predictive neural networks forecast the required control-action adjustments one step ahead, enabling early compensation of disturbances as they propagate through the column train. Unlike conventional approaches, the framework captures disturbance propagation effects through data-driven intercolumn relationships, without relying on state estimation or rigorous process models. The hybrid controller, implemented in an Aspen Dynamics–Simulink environment, combines predictive compensation with local PI feedback for regulatory stability. Simulation results demonstrate significant performance improvements, reducing integral absolute error (IAE) by over 50 % and integral time absolute error (ITAE) by up to 67 % across the distillation train. The proposed framework provides a generalizable and computationally efficient strategy for coordinated control of multicolumn and other cascade-type process systems.
{"title":"Data-driven hybrid control for coordinated operation of multicolumn NGL separation systems","authors":"Sahar Shahriari , Norollah Kasiri , Javad Ivakpour","doi":"10.1016/j.compchemeng.2025.109548","DOIUrl":"10.1016/j.compchemeng.2025.109548","url":null,"abstract":"<div><div>This study introduces a unified data-driven feedforward–feedback control framework for a four-column natural gas liquids (NGL) separation system. A soft sensor estimates upstream feed composition and flow disturbances, while predictive neural networks forecast the required control-action adjustments one step ahead, enabling early compensation of disturbances as they propagate through the column train. Unlike conventional approaches, the framework captures disturbance propagation effects through data-driven intercolumn relationships, without relying on state estimation or rigorous process models. The hybrid controller, implemented in an Aspen Dynamics–Simulink environment, combines predictive compensation with local PI feedback for regulatory stability. Simulation results demonstrate significant performance improvements, reducing integral absolute error (IAE) by over 50 % and integral time absolute error (ITAE) by up to 67 % across the distillation train. The proposed framework provides a generalizable and computationally efficient strategy for coordinated control of multicolumn and other cascade-type process systems.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"207 ","pages":"Article 109548"},"PeriodicalIF":3.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923462","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: 2025-12-15DOI: 10.1016/j.compchemeng.2025.109531
Min Yin , Youqing Wang , Xin Ma , Yining Dong
In recent years, intelligent fault diagnosis—particularly the feasibility of handling hybrid variables—has garnered increasing research attention due to the growing complexity of chemical processes. In many industrial settings, hybrid variables that include both continuous and discrete elements are frequently observed, reflecting the complexity of modern process systems. However, the scarcity of fault samples in such systems has led to the emergence of the few-shot learning problem. Insights from continuous-variable systems suggest that information augmentation is an effective strategy for addressing this issue. To this end, this study proposes a novel information augmentation approach based on Multi-Feature Fusion Networks (MFNets). Specifically, numerical, trend, and manipulation features are extracted from hybrid data using sliding time windows, the Gramian Angular Field (GAF) algorithm, and Gaussian blur techniques, respectively. These multi-view features are then integrated through a shared fully connected layer designed to capture complex interdependencies across views. Furthermore, an independent cross-fusion learning loss function is introduced to model both the consistency and complementarity among feature interactions. Experimental results confirm that the proposed MFNets method demonstrates superior adaptability to few-shot scenarios, enhanced noise robustness, and improved fault diagnosis accuracy compared to existing baseline methods.
{"title":"Intelligent fault diagnosis in hybrid chemical processes under limited samples based on multi-feature fusion learning","authors":"Min Yin , Youqing Wang , Xin Ma , Yining Dong","doi":"10.1016/j.compchemeng.2025.109531","DOIUrl":"10.1016/j.compchemeng.2025.109531","url":null,"abstract":"<div><div>In recent years, intelligent fault diagnosis—particularly the feasibility of handling hybrid variables—has garnered increasing research attention due to the growing complexity of chemical processes. In many industrial settings, hybrid variables that include both continuous and discrete elements are frequently observed, reflecting the complexity of modern process systems. However, the scarcity of fault samples in such systems has led to the emergence of the few-shot learning problem. Insights from continuous-variable systems suggest that information augmentation is an effective strategy for addressing this issue. To this end, this study proposes a novel information augmentation approach based on Multi-Feature Fusion Networks (MFNets). Specifically, numerical, trend, and manipulation features are extracted from hybrid data using sliding time windows, the Gramian Angular Field (GAF) algorithm, and Gaussian blur techniques, respectively. These multi-view features are then integrated through a shared fully connected layer designed to capture complex interdependencies across views. Furthermore, an independent cross-fusion learning loss function is introduced to model both the consistency and complementarity among feature interactions. Experimental results confirm that the proposed MFNets method demonstrates superior adaptability to few-shot scenarios, enhanced noise robustness, and improved fault diagnosis accuracy compared to existing baseline methods.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"207 ","pages":"Article 109531"},"PeriodicalIF":3.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923404","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: 2026-01-16DOI: 10.1016/j.compchemeng.2026.109569
Sida Chai , Ece Serenat Köksal , Xiangyin Kong , Winston S.K. Tang , Erdal Aydın , Mehmet Mercangöz
This paper introduces a variable horizon economic model predictive control (EMPC) framework for a twin bed industrial desiccant air drying plant. Hybrid mechanistic and machine learning models are employed to simulate the drying and regeneration processes, providing a realistic representation of system dynamics. A moving horizon state estimation framework, integrated with hybrid models, is utilized to estimate the adsorbed water content in the beds. Based on these estimated values, an algorithm is implemented to estimate the end time of the regeneration process. The EMPC framework uses this end time as the prediction horizon to optimize the manipulated variable trajectories for the drying process. Simulation results show that the proposed EMPC reduces cooling-energy consumption by increasing the average temperature of the inlet wet air by approximately 2°C. At the same time, it improves system performance by increasing the moisture adsorbed in the bed by approximately . Under these new operating conditions, the overall energy consumption is estimated to decrease by about 6.5%, thereby enhancing process profitability.
{"title":"Variable-horizon economic MPC for cyclic industrial air dryers using hybrid models and state estimation","authors":"Sida Chai , Ece Serenat Köksal , Xiangyin Kong , Winston S.K. Tang , Erdal Aydın , Mehmet Mercangöz","doi":"10.1016/j.compchemeng.2026.109569","DOIUrl":"10.1016/j.compchemeng.2026.109569","url":null,"abstract":"<div><div>This paper introduces a variable horizon economic model predictive control (EMPC) framework for a twin bed industrial desiccant air drying plant. Hybrid mechanistic and machine learning models are employed to simulate the drying and regeneration processes, providing a realistic representation of system dynamics. A moving horizon state estimation framework, integrated with hybrid models, is utilized to estimate the adsorbed water content in the beds. Based on these estimated values, an algorithm is implemented to estimate the end time of the regeneration process. The EMPC framework uses this end time as the prediction horizon to optimize the manipulated variable trajectories for the drying process. Simulation results show that the proposed EMPC reduces cooling-energy consumption by increasing the average temperature of the inlet wet air by approximately 2°C. At the same time, it improves system performance by increasing the moisture adsorbed in the bed by approximately <span><math><mrow><mn>6</mn><mtext>–</mtext><mn>10</mn><mtext>%</mtext></mrow></math></span>. Under these new operating conditions, the overall energy consumption is estimated to decrease by about 6.5%, thereby enhancing process profitability.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"207 ","pages":"Article 109569"},"PeriodicalIF":3.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034929","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号