Pub Date : 2025-11-01DOI: 10.1016/j.jprocont.2025.103574
Shanshan Chen , Xiaodong Wang , Xuejun Bi , Zakhar Maletskyi
Sensor faults in wastewater treatment plants (WWTPs) significantly impact the data quality of online monitoring and further affect process operation. The reliability of online sensor data remains the key barrier which obstacles the digitalization of the water sector. Advances in machine learning (ML) and artificial intelligence (AI) offer new opportunities to improve fault detection and diagnosis. Using CiteSpace, this review analyzes literature from 2008 to 2024, highlighting the increasing adoption of hybrid fault detection models that integrate statistical, model-based, and data-driven methods. It categorizes sensor faults, examines their impact on WWTP monitoring, and evaluates mathematical approaches used for fault detection. While AI-driven models enhance detection accuracy, challenges persist in real-time implementation and adaptability to dynamic WWTP conditions. The review further explores strategies for enhancing fault resilience, emphasizing hybrid models, soft sensors, and advanced sensor networks as effective solutions for maintaining system functionality and ensuring continuous monitoring.
{"title":"Sensor fault characteristics and fault detection in wastewater treatment plants: Current status and trend analysis","authors":"Shanshan Chen , Xiaodong Wang , Xuejun Bi , Zakhar Maletskyi","doi":"10.1016/j.jprocont.2025.103574","DOIUrl":"10.1016/j.jprocont.2025.103574","url":null,"abstract":"<div><div>Sensor faults in wastewater treatment plants (WWTPs) significantly impact the data quality of online monitoring and further affect process operation. The reliability of online sensor data remains the key barrier which obstacles the digitalization of the water sector. Advances in machine learning (ML) and artificial intelligence (AI) offer new opportunities to improve fault detection and diagnosis. Using CiteSpace, this review analyzes literature from 2008 to 2024, highlighting the increasing adoption of hybrid fault detection models that integrate statistical, model-based, and data-driven methods. It categorizes sensor faults, examines their impact on WWTP monitoring, and evaluates mathematical approaches used for fault detection. While AI-driven models enhance detection accuracy, challenges persist in real-time implementation and adaptability to dynamic WWTP conditions. The review further explores strategies for enhancing fault resilience, emphasizing hybrid models, soft sensors, and advanced sensor networks as effective solutions for maintaining system functionality and ensuring continuous monitoring.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103574"},"PeriodicalIF":3.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-31DOI: 10.1016/j.jprocont.2025.103579
Johannes Reinhard , Klaus Löhe , Sebastian Kallabis , Knut Graichen
This paper introduces an advanced approach for dynamic speed drop compensation during threading in rolling processes. The approach combines a data-driven machine learning procedure with a recently presented flatness-based feedforward control to robustly compensate for the speed drop. The feedforward control design accelerates both the rolls and the drivetrain, ensuring that the acceleration torque matches the rolling torque during threading, while maintaining the roll at the desired target speed. Ideally, this prevents the speed drop and enhances the quality and stability of the rolling process. The dynamic speed drop compensation approach is extended in this paper to optimize all stands of a rolling mill, finishing mill as well as roughing mill. To achieve this, the flatness-based feedforward trajectories are adapted to account for uncertainties in the threading event. Moreover, a cost function dependent on optimization parameters is established to optimize the dynamic speed drop compensation. This optimization is carried out using Bayesian Optimization with a Gaussian Process as surrogate model. Both the feedforward control and the Bayesian Optimization run in real-time on an industrial Programmable Logic Controller (PLC). Extensive experimental validation on a hot strip finishing mill, including both the roughing and finishing mill, demonstrates the superior performance of this approach across various key performance indicators in comparison to standard compensation methods.
{"title":"Dynamic compensation of the threading speed drop in rolling processes: Bayesian optimization of the roughing and finishing mill","authors":"Johannes Reinhard , Klaus Löhe , Sebastian Kallabis , Knut Graichen","doi":"10.1016/j.jprocont.2025.103579","DOIUrl":"10.1016/j.jprocont.2025.103579","url":null,"abstract":"<div><div>This paper introduces an advanced approach for dynamic speed drop compensation during threading in rolling processes. The approach combines a data-driven machine learning procedure with a recently presented flatness-based feedforward control to robustly compensate for the speed drop. The feedforward control design accelerates both the rolls and the drivetrain, ensuring that the acceleration torque matches the rolling torque during threading, while maintaining the roll at the desired target speed. Ideally, this prevents the speed drop and enhances the quality and stability of the rolling process. The dynamic speed drop compensation approach is extended in this paper to optimize all stands of a rolling mill, finishing mill as well as roughing mill. To achieve this, the flatness-based feedforward trajectories are adapted to account for uncertainties in the threading event. Moreover, a cost function dependent on optimization parameters is established to optimize the dynamic speed drop compensation. This optimization is carried out using Bayesian Optimization with a Gaussian Process as surrogate model. Both the feedforward control and the Bayesian Optimization run in real-time on an industrial Programmable Logic Controller (PLC). Extensive experimental validation on a hot strip finishing mill, including both the roughing and finishing mill, demonstrates the superior performance of this approach across various key performance indicators in comparison to standard compensation methods.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"156 ","pages":"Article 103579"},"PeriodicalIF":3.9,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145420506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-30DOI: 10.1016/j.jprocont.2025.103571
Estefania Yap , Viet Huynh , Calvin Vong , Peter Vogel , Viv Louzado , Thomas Barnes , Buser Say , Michael Burke , Dana Kulić , Aldeida Aleti
The automation of liquid handling has become integral in speeding up pharmaceutical development for faster drug development and more affordable treatments. However, the optimal parameters which define the aspirate and dispense procedures vary between liquids and liquid volumes, limiting transfer accuracy and precision. Even state-of-the-art liquid handling devices offer predefined parameters for only a handful of liquids and volumes, resulting in novel parameter sets being defined via a manual, time-consuming process. In this study, we propose an experimental framework for automating the optimisation of liquid class parameters for arbitrary liquids. Within our framework, we propose an optimisation and segmentation algorithm, OptAndSeg, to identify the optimal parameters by automatically grouping volumes into volume ranges and optimising parameters for these volume range subsets. Our method was validated on three live experiments: glycerol, a solution of 25% purified human serum albumin, and human serum. The results showed that OptAndSeg outperformed existing benchmarks for glycerol and human serum. By optimising in non-overlapping volume range segments, we were also able to increase the accuracy and precision of liquid transfer for the 25% purified human serum albumin solution and human serum, achieving relative errors of 5% and 6% or less for volumes as small as 30 L. This methodology can be rapidly applied to any arbitrary liquid, therefore enhancing efficiency and throughput of liquid handling in research and development settings.
{"title":"A Bayesian Optimisation with segmentation approach to optimising liquid handling parameters","authors":"Estefania Yap , Viet Huynh , Calvin Vong , Peter Vogel , Viv Louzado , Thomas Barnes , Buser Say , Michael Burke , Dana Kulić , Aldeida Aleti","doi":"10.1016/j.jprocont.2025.103571","DOIUrl":"10.1016/j.jprocont.2025.103571","url":null,"abstract":"<div><div>The automation of liquid handling has become integral in speeding up pharmaceutical development for faster drug development and more affordable treatments. However, the optimal parameters which define the aspirate and dispense procedures vary between liquids and liquid volumes, limiting transfer accuracy and precision. Even state-of-the-art liquid handling devices offer predefined parameters for only a handful of liquids and volumes, resulting in novel parameter sets being defined via a manual, time-consuming process. In this study, we propose an experimental framework for automating the optimisation of liquid class parameters for arbitrary liquids. Within our framework, we propose an optimisation and segmentation algorithm, OptAndSeg, to identify the optimal parameters by automatically grouping volumes into volume ranges and optimising parameters for these volume range subsets. Our method was validated on three live experiments: glycerol, a solution of 25% purified human serum albumin, and human serum. The results showed that OptAndSeg outperformed existing benchmarks for glycerol and human serum. By optimising in non-overlapping volume range segments, we were also able to increase the accuracy and precision of liquid transfer for the 25% purified human serum albumin solution and human serum, achieving relative errors of 5% and 6% or less for volumes as small as 30 <span><math><mi>μ</mi></math></span>L. This methodology can be rapidly applied to any arbitrary liquid, therefore enhancing efficiency and throughput of liquid handling in research and development settings.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"156 ","pages":"Article 103571"},"PeriodicalIF":3.9,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145420504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-30DOI: 10.1016/j.jprocont.2025.103576
Qiang Zhu, Zhonggai Zhao, Fei Liu
Ensuring on-spec product quality that satisfies both customer and regulatory requirements is a fundamental objective in batch manufacturing. Over the years, various data-driven strategies have been proposed for batch quality control, involving batch-to-batch and within-batch approaches. While the former is often implemented using offline optimization, maintaining consistent product quality within a batch remains challenging due to unanticipated disturbances that can lead to off-spec products. Existing within-batch strategies, such as latent-variable-based tracking control, mainly address disturbances that affect batch trajectories, potentially overlooking quality-related variations that do not manifest in the trajectory. To address this gap, we proposed a new within-batch control strategy, quality-by-design latent-variable model predictive control (QbD-LV-MPC), which extends the conventional LV-MPC framework. This strategy dynamically updates the reference trajectories within a predefined design space (DS), ensuring all adjustments remain quality-compliant. Two latent variable models, namely principal component analysis and partial least-squares, are calibrated in parallel to construct the LV-MPC and calculate the DS. Upon detecting quality-related disturbances, QbD-LV-MPC promptly adjusts the reference profiles within the DS and computes optimal inputs using LV-MPC. By confining control actions to the DS, the strategy ensures product quality and enhances process flexibility. The proposed strategy has been validated using a benchmark simulator, IndPensim, and the case study results show that it outperforms the conventional LV-MPC in reducing quality deviations.
{"title":"Implementing quality-by-design latent-variable model predictive control (QbD-LV-MPC) for batch processes: An updating policy for batch profiles","authors":"Qiang Zhu, Zhonggai Zhao, Fei Liu","doi":"10.1016/j.jprocont.2025.103576","DOIUrl":"10.1016/j.jprocont.2025.103576","url":null,"abstract":"<div><div>Ensuring on-spec product quality that satisfies both customer and regulatory requirements is a fundamental objective in batch manufacturing. Over the years, various data-driven strategies have been proposed for batch quality control, involving batch-to-batch and within-batch approaches. While the former is often implemented using offline optimization, maintaining consistent product quality within a batch remains challenging due to unanticipated disturbances that can lead to off-spec products. Existing within-batch strategies, such as latent-variable-based tracking control, mainly address disturbances that affect batch trajectories, potentially overlooking quality-related variations that do not manifest in the trajectory. To address this gap, we proposed a new within-batch control strategy, quality-by-design latent-variable model predictive control (QbD-LV-MPC), which extends the conventional LV-MPC framework. This strategy dynamically updates the reference trajectories within a predefined design space (DS), ensuring all adjustments remain quality-compliant. Two latent variable models, namely principal component analysis and partial least-squares, are calibrated in parallel to construct the LV-MPC and calculate the DS. Upon detecting quality-related disturbances, QbD-LV-MPC promptly adjusts the reference profiles within the DS and computes optimal inputs using LV-MPC. By confining control actions to the DS, the strategy ensures product quality and enhances process flexibility. The proposed strategy has been validated using a benchmark simulator, IndPensim, and the case study results show that it outperforms the conventional LV-MPC in reducing quality deviations.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"156 ","pages":"Article 103576"},"PeriodicalIF":3.9,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145420503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-28DOI: 10.1016/j.jprocont.2025.103577
Jiamin Xu , Nazli Demirer , Vy Pho , Kaixiao Tian , He Zhang , Ketan Bhaidasna , Robert Darbe , Dongmei Chen
This paper presents a multi-input, multi-output (MIMO) economic model predictive control (MPC) approach for directional drilling using an efficient model with state and parameter estimation using sensor fusion. The MPC framework coordinates weight-on-bit (WOB) and pad force to ensure the bit follows the planned well trajectory while maintaining high WOB, implying a high rate of penetration (ROP). The simulation studies, conducted under scenarios with initial bit positions both ahead of and behind the well plan, demonstrate the robustness and effectiveness of the proposed MPC strategy. The results show that the controller can maintain the bit on the well plan despite various disturbances and noise, indicating its potential for practical application in the field.
{"title":"Real time multi-inputs multi-outputs economic model predictive control for directional drilling based on fast modeling and sensor fusion","authors":"Jiamin Xu , Nazli Demirer , Vy Pho , Kaixiao Tian , He Zhang , Ketan Bhaidasna , Robert Darbe , Dongmei Chen","doi":"10.1016/j.jprocont.2025.103577","DOIUrl":"10.1016/j.jprocont.2025.103577","url":null,"abstract":"<div><div>This paper presents a multi-input, multi-output (MIMO) economic model predictive control (MPC) approach for directional drilling using an efficient model with state and parameter estimation using sensor fusion. The MPC framework coordinates weight-on-bit (WOB) and pad force to ensure the bit follows the planned well trajectory while maintaining high WOB, implying a high rate of penetration (ROP). The simulation studies, conducted under scenarios with initial bit positions both ahead of and behind the well plan, demonstrate the robustness and effectiveness of the proposed MPC strategy. The results show that the controller can maintain the bit on the well plan despite various disturbances and noise, indicating its potential for practical application in the field.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"156 ","pages":"Article 103577"},"PeriodicalIF":3.9,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145371409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-25DOI: 10.1016/j.jprocont.2025.103573
Chuan Wang , Haojie Liao , Kui Xie , Chao Yu
This study proposes a robust control framework that integrates sliding mode control (SMC) with a novel hybrid observer (UKF-LSTM in series) to stabilize separator level and pressure. The stability of the control system is ensured by the Lyapunov method. A significant innovation is a hybrid observer that combines an Unscented Kalman Filter (UKF) and a Long Short-Term Memory (LSTM) network in series to accurately estimate the unmeasurable multiphase inflow. In OLGA plug flow simulations, the framework reduced flow estimation Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 73.9 % and 64.7 % over the baseline. The Control tests showed Integral of Squared Error (ISE), Integral of Absolute Error (IAE), and Integral of Time-weighted Absolute Error (ITAE) were 49.8 %, 24.8 %, and 18.0 %, with convergence accelerated by at least 250 s. Results demonstrate that the method achieves a practical balance between accuracy, robustness, and computational efficiency, making it suitable for real-time industrial separator control under variable conditions.
{"title":"Research on control methods for gas-liquid separators based on UKF-LSTM hybrid observation and sliding mode control","authors":"Chuan Wang , Haojie Liao , Kui Xie , Chao Yu","doi":"10.1016/j.jprocont.2025.103573","DOIUrl":"10.1016/j.jprocont.2025.103573","url":null,"abstract":"<div><div>This study proposes a robust control framework that integrates sliding mode control (SMC) with a novel hybrid observer (UKF-LSTM in series) to stabilize separator level and pressure. The stability of the control system is ensured by the Lyapunov method. A significant innovation is a hybrid observer that combines an Unscented Kalman Filter (UKF) and a Long Short-Term Memory (LSTM) network in series to accurately estimate the unmeasurable multiphase inflow. In OLGA plug flow simulations, the framework reduced flow estimation Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 73.9 % and 64.7 % over the baseline. The Control tests showed Integral of Squared Error (ISE), Integral of Absolute Error (IAE), and Integral of Time-weighted Absolute Error (ITAE) were 49.8 %, 24.8 %, and 18.0 %, with convergence accelerated by at least 250 s. Results demonstrate that the method achieves a practical balance between accuracy, robustness, and computational efficiency, making it suitable for real-time industrial separator control under variable conditions.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103573"},"PeriodicalIF":3.9,"publicationDate":"2025-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-21DOI: 10.1016/j.jprocont.2025.103533
Zhengang Zhong, Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis
This paper presents a novel data-driven stochastic MPC design for discrete-time nonlinear systems with additive disturbances by leveraging the Koopman operator and a distributionally robust optimization (DRO) framework. By lifting the dynamical system into a linear space, we achieve a finite-dimensional approximation of the Koopman operator. We explicitly account for the modeling approximation and additive disturbance error by a mixed stochastic–deterministic tube for the lifted linear model. This ensures the regulation of the original nonlinear system while complying with the prespecified constraints. Stochastic and deterministic tubes are constructed using a DRO and a hyper-cube hull, respectively. We provide finite sample error bounds for both types of tubes. The effectiveness of the proposed approach is demonstrated through numerical simulations.
{"title":"Data-driven Koopman MPC using mixed stochastic–deterministic tubes","authors":"Zhengang Zhong, Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis","doi":"10.1016/j.jprocont.2025.103533","DOIUrl":"10.1016/j.jprocont.2025.103533","url":null,"abstract":"<div><div>This paper presents a novel data-driven stochastic MPC design for discrete-time nonlinear systems with additive disturbances by leveraging the Koopman operator and a distributionally robust optimization (DRO) framework. By lifting the dynamical system into a linear space, we achieve a finite-dimensional approximation of the Koopman operator. We explicitly account for the modeling approximation and additive disturbance error by a mixed stochastic–deterministic tube for the lifted linear model. This ensures the regulation of the original nonlinear system while complying with the prespecified constraints. Stochastic and deterministic tubes are constructed using a DRO and a hyper-cube hull, respectively. We provide finite sample error bounds for both types of tubes. The effectiveness of the proposed approach is demonstrated through numerical simulations.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103533"},"PeriodicalIF":3.9,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-21DOI: 10.1016/j.jprocont.2025.103572
Chongyang Liu , Jinxu Cui , Jianzhi Wu , Zhaohua Gong
Optimal control of biochemical processes remains an open research and industrial challenge due to intrinsic system nonlinearity, unsteady dynamics and stringent operation constraints. Although reinforcement learning has recently gained attention, its direct application in biochemical process control has been hindered by the presence of multiple conflicting control objectives. To address this, we formulate a multi-objective optimal control problem in biochemical processes with both control inputs and terminal time as decision variables and subject to path and terminal inequality constraints. For this problem, a time-scaling transformation and an exact penalty method are exploited to convert it into the one with fixed terminal time and simple box constraints. Furthermore, the problem is transformed to a set of single-objective problems by using the scalarization techniques of weighted sum and normalized norm constraint. Then, based on an improved proximal policy optimization algorithm with dynamic clipping threshold, we develop a reinforcement learning algorithm to solve the resulting problems. Finally, two case studies on glucose batch fermentation and lysine fed-batch fermentation show that the proposed reinforcement algorithm can achieve more uniform distribution of optimal solution sets and faster convergence.
{"title":"Multi-objective optimal control of biochemical processes based on reinforcement learning","authors":"Chongyang Liu , Jinxu Cui , Jianzhi Wu , Zhaohua Gong","doi":"10.1016/j.jprocont.2025.103572","DOIUrl":"10.1016/j.jprocont.2025.103572","url":null,"abstract":"<div><div>Optimal control of biochemical processes remains an open research and industrial challenge due to intrinsic system nonlinearity, unsteady dynamics and stringent operation constraints. Although reinforcement learning has recently gained attention, its direct application in biochemical process control has been hindered by the presence of multiple conflicting control objectives. To address this, we formulate a multi-objective optimal control problem in biochemical processes with both control inputs and terminal time as decision variables and subject to path and terminal inequality constraints. For this problem, a time-scaling transformation and an exact penalty method are exploited to convert it into the one with fixed terminal time and simple box constraints. Furthermore, the problem is transformed to a set of single-objective problems by using the scalarization techniques of weighted sum and normalized norm constraint. Then, based on an improved proximal policy optimization algorithm with dynamic clipping threshold, we develop a reinforcement learning algorithm to solve the resulting problems. Finally, two case studies on glucose batch fermentation and lysine fed-batch fermentation show that the proposed reinforcement algorithm can achieve more uniform distribution of optimal solution sets and faster convergence.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103572"},"PeriodicalIF":3.9,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-18DOI: 10.1016/j.jprocont.2025.103567
Mingyu Liang, Yi Zheng, Shaoyuan Li
This paper addresses the hybrid modeling challenges arising from incomplete mechanistic models and operational data noise interference in process industries, proposing a two-layer joint iterative optimization framework for updating parameters of hybrid models integrating mechanistic and data-driven models. The framework achieves real-time anomaly elimination through an outlier screening algorithm, while employing a bidirectional feedback algorithm to enable continuous collaboration and mutual constraints between mechanistic and data-driven models during parameter identification and iterative updates, ensuring robust hybrid model predictions. The proposed method resolves hybrid modeling and updating under conditions of mechanistic model information deficiency. Additionally, by incorporating model uncertainty and prior knowledge, it accomplishes a knowledge-incorporated hybrid modeling process, demonstrating significant practical value. Unlike conventional hybrid modeling approaches where mechanistic knowledge merely guides the modeling process, our method achieves dynamic co-evolution between mechanistic and data-driven models. This paper elaborates on three key aspects: (1) using mechanistic models to screen anomalous data; (2) incorporating mechanistic parameter uncertainty and prior knowledge through Bayesian methods to design knowledge-guided parameter updating method; (3) implementation details of the two-layer joint iterative optimization algorithm. Comparative experiments validate the method’s superior performance under multiple operating conditions and anomalies, demonstrating its scientific validity and practical value in dynamic optimization processes.
{"title":"Towards hybrid modeling with mechanistic and real-time data embed iterative co-optimization for industrial processes","authors":"Mingyu Liang, Yi Zheng, Shaoyuan Li","doi":"10.1016/j.jprocont.2025.103567","DOIUrl":"10.1016/j.jprocont.2025.103567","url":null,"abstract":"<div><div>This paper addresses the hybrid modeling challenges arising from incomplete mechanistic models and operational data noise interference in process industries, proposing a two-layer joint iterative optimization framework for updating parameters of hybrid models integrating mechanistic and data-driven models. The framework achieves real-time anomaly elimination through an outlier screening algorithm, while employing a bidirectional feedback algorithm to enable continuous collaboration and mutual constraints between mechanistic and data-driven models during parameter identification and iterative updates, ensuring robust hybrid model predictions. The proposed method resolves hybrid modeling and updating under conditions of mechanistic model information deficiency. Additionally, by incorporating model uncertainty and prior knowledge, it accomplishes a knowledge-incorporated hybrid modeling process, demonstrating significant practical value. Unlike conventional hybrid modeling approaches where mechanistic knowledge merely guides the modeling process, our method achieves dynamic co-evolution between mechanistic and data-driven models. This paper elaborates on three key aspects: (1) using mechanistic models to screen anomalous data; (2) incorporating mechanistic parameter uncertainty and prior knowledge through Bayesian methods to design knowledge-guided parameter updating method; (3) implementation details of the two-layer joint iterative optimization algorithm. Comparative experiments validate the method’s superior performance under multiple operating conditions and anomalies, demonstrating its scientific validity and practical value in dynamic optimization processes.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103567"},"PeriodicalIF":3.9,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-17DOI: 10.1016/j.jprocont.2025.103568
C. Bisset , R. Coetzer , PVZ. Venter
Optimising boiler operations is challenging due to fluctuating conditions in complex thermo-fluid systems. This study introduces a novel approach to improve efficiency in coal-fired boilers by developing and validating an artificial neural network (ANN) model that provides both statistically accurate and scientifically feasible predictions. Three multi-layer perceptron (MLP) feedforward ANN models were developed, with variable selection supported by principal component analysis (PCA) and hyperparameter optimisation performed using Latin hypercube sampling (LHS). The best ANN achieved test root mean square errors (RMSEs) of 2.11 t/h for steam flow, 2.11 t/h for blowdown, 4.98 °C for superheated steam temperature, 0.69 bar for steam pressure, and 0.86 % for efficiency. The mean absolute percentage error (MAPE) for efficiency remained below 1.25 %, with deviations constrained within ±4.25 %. Statistical and thermodynamic validations were applied, including bootstrap aggregation of prediction variance and mass and energy balance checks. Results showed that 96.76 % of samples achieved water mass balance deviations of less than 0.01 %. Furthermore, 100 % of predictions for efficiency and energy output fell within a 5 % absolute error range. The novelty of this work lies in integrating ANN predictions with thermo-fluid validation. Theoretically, it advances current literature by bridging the gap between statistical accuracy and physical feasibility. Practically, it provides a reliable framework for evaluating efficiency in operational settings and lays the foundation for a machine learning (ML)–aided decision-support framework (DSF) for energy efficiency optimisation in coal-fired boilers.
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