Pub Date : 2025-09-03DOI: 10.1016/j.jprocont.2025.103539
S. Kucherenko , N. Shah , O.V. Klymenko
The primary objective of feasibility analysis is to identify and define the feasibility region, which represents the range of operational conditions (e.g., variations in process parameters) that ensure safe, reliable, and feasible process performance. This work introduces a novel feasibility analysis method that requires only that model constraints (e.g., defining product Critical Quality Attributes or process Key Performance Indicators) be explicitly provided or approximated by a closed-form function, such as a multivariate polynomial model. The method is based on V.L. Rvachev's R-functions, enabling an explicit analytical representation of the feasibility region without relying on complex optimization-based approaches. R-functions offer a framework for describing intricate geometric shapes and performing operations on them using implicit functions and inequality constraints. The theory of R-functions facilitates the identification of feasibility regions through algebraic manipulation, making it a more practical alternative to traditional optimization-based methods. The effectiveness of the proposed approach is demonstrated using a suite of well-known test cases from the literature.
{"title":"Identification of feasible regions using R-functions","authors":"S. Kucherenko , N. Shah , O.V. Klymenko","doi":"10.1016/j.jprocont.2025.103539","DOIUrl":"10.1016/j.jprocont.2025.103539","url":null,"abstract":"<div><div>The primary objective of feasibility analysis is to identify and define the feasibility region, which represents the range of operational conditions (e.g., variations in process parameters) that ensure safe, reliable, and feasible process performance. This work introduces a novel feasibility analysis method that requires only that model constraints (e.g., defining product Critical Quality Attributes or process Key Performance Indicators) be explicitly provided or approximated by a closed-form function, such as a multivariate polynomial model. The method is based on V.L. Rvachev's R-functions, enabling an explicit analytical representation of the feasibility region without relying on complex optimization-based approaches. R-functions offer a framework for describing intricate geometric shapes and performing operations on them using implicit functions and inequality constraints. The theory of R-functions facilitates the identification of feasibility regions through algebraic manipulation, making it a more practical alternative to traditional optimization-based methods. The effectiveness of the proposed approach is demonstrated using a suite of well-known test cases from the literature.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"154 ","pages":"Article 103539"},"PeriodicalIF":3.9,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932536","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 : 2025-09-03DOI: 10.1016/j.jprocont.2025.103528
Augustin Alonso , Gerard Robert , Gildas Besançon
In this paper, the problem of estimating head losses in the hydraulic feeding system of a hydropower plant is considered. Accurate head loss assessment is crucial for performance monitoring, efficiency optimization, and predictive maintenance of these critical energy infrastructures. To this end, a nonlinear state-space model based on fundamental physical principles is first established. Recognizing the challenges of observability with a full complex model, this paper proposes a multi-regime modelling strategy, where the full model is particularized into simplified forms suitable for different operational scenarios (normal operation, quasi-static conditions, and plant shutdown). This approach facilitates the estimation of specific head loss coefficients or their combinations. Various estimation techniques are then explored and applied to these models, primarily based on Kalman filters for state-observer approaches and direct least squares for regression-based methods, all integrating real-time measurements. The efficacy of these methods is validated through comprehensive simulations and tests using operational data collected from an industrial hydropower facility.
{"title":"A physics-based multi-regime approach for estimation of head losses in operating hydropower plants","authors":"Augustin Alonso , Gerard Robert , Gildas Besançon","doi":"10.1016/j.jprocont.2025.103528","DOIUrl":"10.1016/j.jprocont.2025.103528","url":null,"abstract":"<div><div>In this paper, the problem of estimating head losses in the hydraulic feeding system of a hydropower plant is considered. Accurate head loss assessment is crucial for performance monitoring, efficiency optimization, and predictive maintenance of these critical energy infrastructures. To this end, a nonlinear state-space model based on fundamental physical principles is first established. Recognizing the challenges of observability with a full complex model, this paper proposes a multi-regime modelling strategy, where the full model is particularized into simplified forms suitable for different operational scenarios (normal operation, quasi-static conditions, and plant shutdown). This approach facilitates the estimation of specific head loss coefficients or their combinations. Various estimation techniques are then explored and applied to these models, primarily based on Kalman filters for state-observer approaches and direct least squares for regression-based methods, all integrating real-time measurements. The efficacy of these methods is validated through comprehensive simulations and tests using operational data collected from an industrial hydropower facility.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"154 ","pages":"Article 103528"},"PeriodicalIF":3.9,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932537","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 : 2025-09-02DOI: 10.1016/j.jprocont.2025.103537
Lei Xu , Jinxing Lin , Xiang Wu , Rong Fu
The large-scale integration of renewable energy into the power grid introduces strong stochastic disturbances, posing new challenges to the safety of load frequency control (LFC). To deal with this issue, a safety control strategy is proposed for lithium-ion energy storage cluster into LFC. First, to achieve efficient frequency control with the energy storage cluster, a command allocation strategy for energy storage cluster and a control strategy for units are proposed, with comprehensive consideration of the state of charge, state of health and the real-time grid frequency deviation. Next, both the maximum frequency deviation (MFD) and the rate of change of frequency (RoCoF) are picked as dynamic response performance indexes to ensure frequency safety. Then, a novel LFC controller based on Safety Enhanced Deep Deterministic Policy Gradient (SE-DDPG) reinforcement learning algorithm is designed. The safety model of SE-DDPG which integrated with safety prediction network and intrinsic curiosity module (ICM) can enhance the exploratory capability while improving the safety and reliability of the policy. Finally, the effectiveness of the proposed safe LFC strategy is validated by numerical simulation. Compare with conventional proportional integral control, the proposed strategy reduces the MFD and the root mean square frequency deviation by 41.38 % and 22.74 % in the random noise scene. In the step load scene, MFD and the max RoCoF are reduced by 46.88 % and 48.15 %.
{"title":"Safe control strategy for energy storage cluster assisted load frequency control based on reinforcement learning","authors":"Lei Xu , Jinxing Lin , Xiang Wu , Rong Fu","doi":"10.1016/j.jprocont.2025.103537","DOIUrl":"10.1016/j.jprocont.2025.103537","url":null,"abstract":"<div><div>The large-scale integration of renewable energy into the power grid introduces strong stochastic disturbances, posing new challenges to the safety of load frequency control (LFC). To deal with this issue, a safety control strategy is proposed for lithium-ion energy storage cluster into LFC. First, to achieve efficient frequency control with the energy storage cluster, a command allocation strategy for energy storage cluster and a control strategy for units are proposed, with comprehensive consideration of the state of charge, state of health and the real-time grid frequency deviation. Next, both the maximum frequency deviation (MFD) and the rate of change of frequency (RoCoF) are picked as dynamic response performance indexes to ensure frequency safety. Then, a novel LFC controller based on Safety Enhanced Deep Deterministic Policy Gradient (SE-DDPG) reinforcement learning algorithm is designed. The safety model of SE-DDPG which integrated with safety prediction network and intrinsic curiosity module (ICM) can enhance the exploratory capability while improving the safety and reliability of the policy. Finally, the effectiveness of the proposed safe LFC strategy is validated by numerical simulation. Compare with conventional proportional integral control, the proposed strategy reduces the MFD and the root mean square frequency deviation by 41.38 % and 22.74 % in the random noise scene. In the step load scene, MFD and the max RoCoF are reduced by 46.88 % and 48.15 %.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"154 ","pages":"Article 103537"},"PeriodicalIF":3.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925456","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 : 2025-08-30DOI: 10.1016/j.jprocont.2025.103532
Peng Zhang, Wenkai Hu, Yupeng Li, Weihua Cao
In drilling processes, real-time detection of drill bit failure states is essential to mitigate operational risks, reduce downtime, and enhance drilling precision. However, drilling signals often exhibit both long-term degradation and local subtle changes. This coexistence poses great challenges to the accurate detection of drill bit failures. Moreover, models trained on historical data often exhibit significant performance degradation when deployed to new drilling depths. This is because the distributions of drilling process data diverge at these new depths due to lithological heterogeneity. To overcome such limitations, this paper proposes a new drill bit failure detection method for drilling processes by integrating Transformer-Convolutional Selective Fusion Network (TCSFN) with multi-stage incremental learning. The main contributions are twofold: 1) A feature extraction method based on TCSFN is proposed to capture global long-term trend features and local transient fluctuation features; 2) a multi-stage incremental learning strategy is designed for different stages of the drilling processes, and composite losses are devised for these stages separately. Case studies involving real-world data are utilized to demonstrate the effectiveness and superiority of the proposed method.
{"title":"Drill bit failure detection for drilling processes based on global–local feature extraction and multi-stage incremental learning","authors":"Peng Zhang, Wenkai Hu, Yupeng Li, Weihua Cao","doi":"10.1016/j.jprocont.2025.103532","DOIUrl":"10.1016/j.jprocont.2025.103532","url":null,"abstract":"<div><div>In drilling processes, real-time detection of drill bit failure states is essential to mitigate operational risks, reduce downtime, and enhance drilling precision. However, drilling signals often exhibit both long-term degradation and local subtle changes. This coexistence poses great challenges to the accurate detection of drill bit failures. Moreover, models trained on historical data often exhibit significant performance degradation when deployed to new drilling depths. This is because the distributions of drilling process data diverge at these new depths due to lithological heterogeneity. To overcome such limitations, this paper proposes a new drill bit failure detection method for drilling processes by integrating Transformer-Convolutional Selective Fusion Network (TCSFN) with multi-stage incremental learning. The main contributions are twofold: 1) A feature extraction method based on TCSFN is proposed to capture global long-term trend features and local transient fluctuation features; 2) a multi-stage incremental learning strategy is designed for different stages of the drilling processes, and composite losses are devised for these stages separately. Case studies involving real-world data are utilized to demonstrate the effectiveness and superiority of the proposed method.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"154 ","pages":"Article 103532"},"PeriodicalIF":3.9,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144917020","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 : 2025-08-30DOI: 10.1016/j.jprocont.2025.103535
Jiyang Chen, Na Luo
Reinforcement learning (RL) in industrial process control faces critical challenges, including limited data availability, unsafe exploration, and the high cost of high-fidelity simulators. These issues limit the practical adoption of RL in process control systems. To address these limitations, this paper presents a comprehensive framework that combines offline pre-training with online finetuning. Specifically, the framework first employs offline RL method to learn conservative policies from historical data, preventing overestimation of unseen actions. It then transitions to fine-tuning using online RL method with a mixed replay buffer that gradually shifts from offline to online data. To further enhance safety during online exploration, this work introduces a trajectory-guided strategy that leverages timestamped sub-optimal expert demonstrations. Rather than replacing agent actions entirely, the proposed method computes a weighted combination of agent and expert actions based on a decaying intervention rate. Both components are designed as modular additions that can be integrated into existing actor-critic algorithms without structural modifications. Case studies on penicillin fermentation and simulated moving bed (SMB) processes demonstrate that the proposed framework outperforms baseline algorithms in terms of learning efficiency, stability, computation costs, and operational safety.
{"title":"An offline-to-online reinforcement learning framework with trajectory-guided exploration for industrial process control","authors":"Jiyang Chen, Na Luo","doi":"10.1016/j.jprocont.2025.103535","DOIUrl":"10.1016/j.jprocont.2025.103535","url":null,"abstract":"<div><div>Reinforcement learning (RL) in industrial process control faces critical challenges, including limited data availability, unsafe exploration, and the high cost of high-fidelity simulators. These issues limit the practical adoption of RL in process control systems. To address these limitations, this paper presents a comprehensive framework that combines offline pre-training with online finetuning. Specifically, the framework first employs offline RL method to learn conservative policies from historical data, preventing overestimation of unseen actions. It then transitions to fine-tuning using online RL method with a mixed replay buffer that gradually shifts from offline to online data. To further enhance safety during online exploration, this work introduces a trajectory-guided strategy that leverages timestamped sub-optimal expert demonstrations. Rather than replacing agent actions entirely, the proposed method computes a weighted combination of agent and expert actions based on a decaying intervention rate. Both components are designed as modular additions that can be integrated into existing actor-critic algorithms without structural modifications. Case studies on penicillin fermentation and simulated moving bed (SMB) processes demonstrate that the proposed framework outperforms baseline algorithms in terms of learning efficiency, stability, computation costs, and operational safety.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"154 ","pages":"Article 103535"},"PeriodicalIF":3.9,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144917021","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}
Recently, zero-shot learning (ZSL) has emerged as a promising method in the industrial fault diagnosis. However, restricted by the strong bias problem, unseen class faults tend to be classified as seen class faults in the generalized zero-shot learning (GZSL) task. To address this issue, a novel method called memory-guided reconstruction (MGR) is proposed for generalized zero-shot industrial fault diagnosis. In MGR, memory prototypes of seen classes are first learned by a self-organizing map (SOM) and stored in a memory module. During the training, the encoding of a sample is obtained from the encoder as a query. Instead of directly reconstructing from this query, a support memory aggregated from relevant memory prototypes of the query is delivered to the decoder for reconstruction. A specific memory alignment matrix is designed to measure the consistency between the query and support memory. At the test stage, unseen classes tend to produce higher reconstruction errors than seen classes because the support memory is acquired from seen class memory prototypes. A new “identify-classify” learning paradigm is adopted: identify the domain (i.e. seen or unseen) of the test sample based on the strengthened reconstruction error, and further classifythe sample within the identified domain. Extensive experiments on the benchmark dataset demonstrate the significant superiority of MGR. Moreover, MGR achieves competitive results compared to supervised learning methods. The code of MGR is available at https://github.com/htz-ecust/memory-guided-autoencoder.
{"title":"Memory-guided reconstruction for generalized zero-shot industrial fault diagnosis","authors":"Zhengwei Hu , Wei Xiang , Jingchao Peng , Haitao Zhao","doi":"10.1016/j.jprocont.2025.103531","DOIUrl":"10.1016/j.jprocont.2025.103531","url":null,"abstract":"<div><div>Recently, zero-shot learning (ZSL) has emerged as a promising method in the industrial fault diagnosis. However, restricted by the strong bias problem, unseen class faults tend to be classified as seen class faults in the generalized zero-shot learning (GZSL) task. To address this issue, a novel method called memory-guided reconstruction (MGR) is proposed for generalized zero-shot industrial fault diagnosis. In MGR, memory prototypes of seen classes are first learned by a self-organizing map (SOM) and stored in a memory module. During the training, the encoding of a sample is obtained from the encoder as a query. Instead of directly reconstructing from this query, a support memory aggregated from relevant memory prototypes of the query is delivered to the decoder for reconstruction. A specific <em>memory alignment matrix</em> is designed to measure the consistency between the query and support memory. At the test stage, unseen classes tend to produce higher reconstruction errors than seen classes because the support memory is acquired from seen class memory prototypes. A new “<em>identify-classify</em>” learning paradigm is adopted: <em>identify</em> the domain (i.e. seen or unseen) of the test sample based on the strengthened reconstruction error, and further <em>classify</em>the sample within the identified domain. Extensive experiments on the benchmark dataset demonstrate the significant superiority of MGR. Moreover, MGR achieves competitive results compared to supervised learning methods. The code of MGR is available at <span><span>https://github.com/htz-ecust/memory-guided-autoencoder</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"154 ","pages":"Article 103531"},"PeriodicalIF":3.9,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893754","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 : 2025-08-25DOI: 10.1016/j.jprocont.2025.103529
Lin Sha , Jiaqi Li , Min Wang , Shihang Yu , Sibo Qiao
In fault diagnosis tasks for process industries, comprehensively identifying all potential fault types poses significant challenges. Therefore, zero-shot fault diagnosis has gradually become a research hotspot. Currently, existing zero-shot fault diagnosis methods commonly face domain shift issues, which limit diagnostic performance. To address this shift, this paper proposes a feature-attribute consistency contrastive generative network (FAC-CGNet). This method combines attribute supervision with a contrastive learning mechanism to simultaneously maintain attribute consistency and decouple the feature space during feature generation. FAC-CGNet constructs an attribute-guided feature generation framework that integrates attribute information into the feature transformation process, ensuring that the generated features in the feature space remain consistent with their corresponding attributes. Furthermore, to prevent excessive overlap of generated features with similar attributes in the feature space, the paper designs a contrastive decoupling module. This module optimizes the feature space distribution through feature separation constraints and further enhances feature representation discrimination by combining a feature concatenation strategy. Finally, experiments on the public TEP dataset show that FAC-CGNet achieves an average accuracy of 83.1% in unknown fault diagnosis and significantly optimizes the feature representations in the feature space, confirming the effectiveness and superiority of the proposed method.
{"title":"A contrastive generative network with feature-attribute consistency for zero-shot fault diagnosis in process industries","authors":"Lin Sha , Jiaqi Li , Min Wang , Shihang Yu , Sibo Qiao","doi":"10.1016/j.jprocont.2025.103529","DOIUrl":"10.1016/j.jprocont.2025.103529","url":null,"abstract":"<div><div>In fault diagnosis tasks for process industries, comprehensively identifying all potential fault types poses significant challenges. Therefore, zero-shot fault diagnosis has gradually become a research hotspot. Currently, existing zero-shot fault diagnosis methods commonly face domain shift issues, which limit diagnostic performance. To address this shift, this paper proposes a feature-attribute consistency contrastive generative network (FAC-CGNet). This method combines attribute supervision with a contrastive learning mechanism to simultaneously maintain attribute consistency and decouple the feature space during feature generation. FAC-CGNet constructs an attribute-guided feature generation framework that integrates attribute information into the feature transformation process, ensuring that the generated features in the feature space remain consistent with their corresponding attributes. Furthermore, to prevent excessive overlap of generated features with similar attributes in the feature space, the paper designs a contrastive decoupling module. This module optimizes the feature space distribution through feature separation constraints and further enhances feature representation discrimination by combining a feature concatenation strategy. Finally, experiments on the public TEP dataset show that FAC-CGNet achieves an average accuracy of 83.1% in unknown fault diagnosis and significantly optimizes the feature representations in the feature space, confirming the effectiveness and superiority of the proposed method.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"154 ","pages":"Article 103529"},"PeriodicalIF":3.9,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902696","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 : 2025-08-24DOI: 10.1016/j.jprocont.2025.103513
Susanne Sass , Alexander Mitsos
Active research on optimal control methods comprises the developments of research groups from various fields, including control, mathematics, and process systems engineering. Although there is a consensus on the classification of the main solution methods, different terms are often used for the same method. For example, solving optimal control problems with control discretization and embedded state integration may be called sequential method or direct single shooting. Equally severely, the same term may be used ambiguously: Is control vector parameterization a synonym for control discretization or for direct single shooting? Both misleading distinctions and ambiguity complicate the scientific discourse. Thus, we delineate standard terms from open-loop optimal control in this tutorial. More precisely, we formulate and challenge hypotheses on the terminology of direct methods, i.e., solution methods using control discretization combined with state integration and/or state discretization. In particular, we point out the parallel of the embedded state integration with a numerical integration scheme and the reduced-space formulation of approaches using state discretization. Taking a step further towards integrated scheduling and control problems, we additionally investigate the similarities and differences between the discrete-time solution of optimal control problems and optimal quasi-steady operation. In this context, we also hint on the discrete-time representation in scheduling which refers to the handling of controls rather than the handling of process dynamics. Rather than concluding with the “correct” term to use, this tutorial concludes with recommendations on how to avoid misunderstandings in the versatile research community.
{"title":"Obscured by terminology: Hidden parallels in direct methods for open-loop optimal control","authors":"Susanne Sass , Alexander Mitsos","doi":"10.1016/j.jprocont.2025.103513","DOIUrl":"10.1016/j.jprocont.2025.103513","url":null,"abstract":"<div><div>Active research on optimal control methods comprises the developments of research groups from various fields, including control, mathematics, and process systems engineering. Although there is a consensus on the classification of the main solution methods, different terms are often used for the same method. For example, solving optimal control problems with control discretization and embedded state integration may be called sequential method or direct single shooting. Equally severely, the same term may be used ambiguously: Is control vector parameterization a synonym for control discretization or for direct single shooting? Both misleading distinctions and ambiguity complicate the scientific discourse. Thus, we delineate standard terms from open-loop optimal control in this tutorial. More precisely, we formulate and challenge hypotheses on the terminology of direct methods, i.e., solution methods using control discretization combined with state integration and/or state discretization. In particular, we point out the parallel of the embedded state integration with a numerical integration scheme and the reduced-space formulation of approaches using state discretization. Taking a step further towards integrated scheduling and control problems, we additionally investigate the similarities and differences between the discrete-time solution of optimal control problems and optimal quasi-steady operation. In this context, we also hint on the discrete-time representation in scheduling which refers to the handling of controls rather than the handling of process dynamics. Rather than concluding with the “correct” term to use, this tutorial concludes with recommendations on how to avoid misunderstandings in the versatile research community.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"154 ","pages":"Article 103513"},"PeriodicalIF":3.9,"publicationDate":"2025-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893776","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 : 2025-08-22DOI: 10.1016/j.jprocont.2025.103499
Karim Davari Benam , Hasti Khoshamadi , Marte Kierulf Å m , Sverre Chr. Christiansen , Patrick Christian Bösch , Dag Roar Hjelme , Øyvind Stavdahl , Sven Magnus Carlsen , Sébastien Gros , Anders Lyngvi Fougner
Fully automated regulation of blood glucose levels (BGL) has been the ultimate goal in the treatment of type 1 diabetes (T1D). In this context, full automation refers to a system that operates without requiring any patient interaction, such as meal or exercise announcements or manual insulin adjustments. However, achieving BGL control without such inputs remains a significant challenge for artificial pancreas (AP) systems, primarily due to the unfavorable mismatch between the time constants of meal absorption and the slower absorption kinetics of subcutaneously administered insulin. In this paper, we propose and test a dual-hormone intraperitoneal (IP) artificial pancreas system — delivering both insulin and glucagon — to explore the challenges and feasibility of achieving fully automated glucose regulation. To this, a predictive control approach was developed and tested in animal experiments. Experiments were conducted in six anesthetized pigs for 12–24 h and in an awake (unanaesthetized) pig for five days. The proposed method achieved a time-in-range (TIR, 3.9–10 mmol/L) of 73.1–94.2%, exceeding the average TIR reported for commercially available hybrid closed-loop systems. For comparison, the Medtronic MiniMed 670G reports a TIR of 70%, the Tandem t:slim X2 with Control-IQ achieves 72%, the Omnipod 5 with Horizon reports 70%, and the Diabeloop G7 achieves 74% TIR. The findings demonstrate that the full automation of BGL control using dual-hormone AP with IP injections is feasible. The paper also discusses the challenges and complexities associated with implementing the dual-hormone IP artificial pancreas system from the ground up. These challenges include addressing BGL measurement, estimation, prediction, and surgical considerations.
{"title":"Dual hormone predictive control for a fully automated intraperitoneal artificial pancreas: Preclinical evaluation in pigs","authors":"Karim Davari Benam , Hasti Khoshamadi , Marte Kierulf Å m , Sverre Chr. Christiansen , Patrick Christian Bösch , Dag Roar Hjelme , Øyvind Stavdahl , Sven Magnus Carlsen , Sébastien Gros , Anders Lyngvi Fougner","doi":"10.1016/j.jprocont.2025.103499","DOIUrl":"10.1016/j.jprocont.2025.103499","url":null,"abstract":"<div><div>Fully automated regulation of blood glucose levels (BGL) has been the ultimate goal in the treatment of type 1 diabetes (T1D). In this context, full automation refers to a system that operates without requiring any patient interaction, such as meal or exercise announcements or manual insulin adjustments. However, achieving BGL control without such inputs remains a significant challenge for artificial pancreas (AP) systems, primarily due to the unfavorable mismatch between the time constants of meal absorption and the slower absorption kinetics of subcutaneously administered insulin. In this paper, we propose and test a dual-hormone intraperitoneal (IP) artificial pancreas system — delivering both insulin and glucagon — to explore the challenges and feasibility of achieving fully automated glucose regulation. To this, a predictive control approach was developed and tested in animal experiments. Experiments were conducted in six anesthetized pigs for 12–24 h and in an awake (unanaesthetized) pig for five days. The proposed method achieved a time-in-range (TIR, 3.9–10 mmol/L) of 73.1–94.2%, exceeding the average TIR reported for commercially available hybrid closed-loop systems. For comparison, the Medtronic MiniMed 670G reports a TIR of 70%, the Tandem t:slim X2 with Control-IQ achieves 72%, the Omnipod 5 with Horizon reports 70%, and the Diabeloop G7 achieves 74% TIR. The findings demonstrate that the full automation of BGL control using dual-hormone AP with IP injections is feasible. The paper also discusses the challenges and complexities associated with implementing the dual-hormone IP artificial pancreas system from the ground up. These challenges include addressing BGL measurement, estimation, prediction, and surgical considerations.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"154 ","pages":"Article 103499"},"PeriodicalIF":3.9,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144889317","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 : 2025-08-22DOI: 10.1016/j.jprocont.2025.103525
Anthony W.K. Quarshie , Jose Matias , Christopher L.E. Swartz , Yanan Cao , Yajun Wang , Jesus Flores-Cerrillo
Current volatile electricity market conditions incentivize the adaptation of the operation, including the startup, of cryogenic air separation units (ASUs) which are large consumers of electricity. Improvement in ASU startups using earlier proposed open-loop control strategies may not be fully realized in the presence of uncertainties and disturbances. This paper assesses the potential benefit of using a proposed closed-loop control framework to address uncertainty and disturbances. A rolling-horizon economic nonlinear model predictive control (ENMPC) approach is considered, for which strategies are proposed to improve computation time. Online parameter estimation is performed using a computationally efficient method that is easy to implement. Through the case studies presented, it is shown that the proposed framework outperforms the use of offline pre-computed optimal inputs in response to the disturbance and uncertainty considered.
{"title":"Closed-loop control framework for optimal startup of cryogenic air separation units","authors":"Anthony W.K. Quarshie , Jose Matias , Christopher L.E. Swartz , Yanan Cao , Yajun Wang , Jesus Flores-Cerrillo","doi":"10.1016/j.jprocont.2025.103525","DOIUrl":"10.1016/j.jprocont.2025.103525","url":null,"abstract":"<div><div>Current volatile electricity market conditions incentivize the adaptation of the operation, including the startup, of cryogenic air separation units (ASUs) which are large consumers of electricity. Improvement in ASU startups using earlier proposed open-loop control strategies may not be fully realized in the presence of uncertainties and disturbances. This paper assesses the potential benefit of using a proposed closed-loop control framework to address uncertainty and disturbances. A rolling-horizon economic nonlinear model predictive control (ENMPC) approach is considered, for which strategies are proposed to improve computation time. Online parameter estimation is performed using a computationally efficient method that is easy to implement. Through the case studies presented, it is shown that the proposed framework outperforms the use of offline pre-computed optimal inputs in response to the disturbance and uncertainty considered.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"154 ","pages":"Article 103525"},"PeriodicalIF":3.9,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144889318","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}
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