Journal of Process Control最新文献_第10页

A physics-based multi-regime approach for estimation of head losses in operating hydropower plants 运行水电站水头损失估算的基于物理的多状态方法

IF 3.9 2区计算机科学

Journal of Process Control Pub Date : 2025-10-01 Epub Date: 2025-09-03 DOI: 10.1016/j.jprocont.2025.103528

Augustin Alonso , Gerard Robert , Gildas Besançon

{"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":"2025-10-01","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}

引用次数: 0

An offline-to-online reinforcement learning framework with trajectory-guided exploration for industrial process control 基于轨迹导向探索的工业过程控制离线到在线强化学习框架

IF 3.9 2区计算机科学

Journal of Process Control Pub Date : 2025-10-01 Epub Date: 2025-08-30 DOI: 10.1016/j.jprocont.2025.103535

Jiyang Chen, Na Luo

{"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":"2025-10-01","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}

引用次数: 0

Model selection and parameter optimization of model predictive control for building radiant systems 建筑辐射系统模型预测控制的模型选择与参数优化

IF 3.9 2区计算机科学

Journal of Process Control Pub Date : 2025-10-01 Epub Date: 2025-08-05 DOI: 10.1016/j.jprocont.2025.103512

Qiong Chen , Wenjing Wang , Nan Li

{"title":"Model selection and parameter optimization of model predictive control for building radiant systems","authors":"Qiong Chen , Wenjing Wang , Nan Li","doi":"10.1016/j.jprocont.2025.103512","DOIUrl":"10.1016/j.jprocont.2025.103512","url":null,"abstract":"<div><div>In this work, we present a comprehensive parametric investigation quantifying the influence of reduced-order models (ROMs) of varying fidelity on both dynamic and steady-state performance of a Model Predictive Control (MPC) loop for building thermal regulation. Through simulations using the full-order model and ROMs of orders 1–6, we systematically determined that ROMs of order 4 or higher achieve temperature overshoots within 0.2 °C, settling times under 15 min, and steady-state errors below 0.5 °C when paired with prediction horizons of 12–24 steps and control horizons ≥ 2, thus matching full-order MPC performance while reducing computation by up to 70 %. In contrast, the lowest-order ROM (ROM1) requires a prediction horizon ≤ 12 and a control horizon ≥ 3 to limit overshoot to 1.0 °C and static error to 1.2 °C. Furthermore, the original model and high-order ROMs maintain robust control (overshoot < 0.5 °C, settling time < 10 min) across manipulated-variable rate weights from 0.1 to 1.0 and manipulated-output weights from 0.5 to 2.0, whereas ROM1 exhibits strong sensitivity, operating acceptably only near MV-rate ≈ 0.2 and MO weight ≈ 1.0. These quantitative guidelines enable practitioners to balance computational cost and control accuracy by selecting an appropriately ordered ROM and tuning horizons and weightings within the identified numerical ranges.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"154 ","pages":"Article 103512"},"PeriodicalIF":3.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144770853","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}

引用次数: 0

Performance grade similarity-based generalized zero-shot operating performance assessment of industrial processes with insufficient samples 样本不足情况下基于性能等级相似性的工业过程广义零弹运行性能评价

IF 3.9 2区计算机科学

Journal of Process Control Pub Date : 2025-10-01 Epub Date: 2025-08-14 DOI: 10.1016/j.jprocont.2025.103523

Siqi Wang , Yan Liu , Lulu Fu , Fei Chu , Fuli Wang , Chenhui Bao

{"title":"Performance grade similarity-based generalized zero-shot operating performance assessment of industrial processes with insufficient samples","authors":"Siqi Wang , Yan Liu , Lulu Fu , Fei Chu , Fuli Wang , Chenhui Bao","doi":"10.1016/j.jprocont.2025.103523","DOIUrl":"10.1016/j.jprocont.2025.103523","url":null,"abstract":"<div><div>The process operating performance assessment (POPA) is vital for enhancing economic production in industrial processes. This study addresses the challenge in POPA of assessment unseen performance grades with zero samples, while also dealing with insufficient data for seen performance grades. We propose PGSGZSIS, a performance grade similarity-based generalized zero-shot method that integrates accessible superficial expert knowledge with a multi-expert voting mechanism to construct a performance grade similarity matrix (PGSM). The PGSM is validated by seen-data-driven expert reliability calculation, reducing dependency on deep expert knowledge while enhancing objectivity through data quantification. Additionally, an auxiliary set augmentation strategy based on feature similarity is introduced, constructing an auxiliary dataset by screening samples from similar operational conditions to address scarce seen samples. By constructing the PGSM and augmenting seen samples with auxiliary data, our approach not only alleviates the issue of insufficient seen samples but also tackles the generalized zero-shot learning (GZSL) problem for POPA. Experimental results validate the effectiveness of the proposed method in a hydrometallurgical process.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"154 ","pages":"Article 103523"},"PeriodicalIF":3.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841765","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}

引用次数: 0

Memory-guided reconstruction for generalized zero-shot industrial fault diagnosis 广义零距工业故障诊断的记忆引导重构

IF 3.9 2区计算机科学

Journal of Process Control Pub Date : 2025-10-01 Epub Date: 2025-08-25 DOI: 10.1016/j.jprocont.2025.103531

Zhengwei Hu , Wei Xiang , Jingchao Peng , Haitao Zhao

{"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":"2025-10-01","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}

引用次数: 0

Addressing external distorted heterogeneity: Input–output disentangled causal representation for mixed time series 处理外部扭曲异质性：混合时间序列的输入-输出解纠缠因果表示

IF 3.9 2区计算机科学

Journal of Process Control Pub Date : 2025-10-01 Epub Date: 2025-08-11 DOI: 10.1016/j.jprocont.2025.103521

Liujiayi Zhao , Baoxue Li , Chunhui Zhao

{"title":"Addressing external distorted heterogeneity: Input–output disentangled causal representation for mixed time series","authors":"Liujiayi Zhao , Baoxue Li , Chunhui Zhao","doi":"10.1016/j.jprocont.2025.103521","DOIUrl":"10.1016/j.jprocont.2025.103521","url":null,"abstract":"<div><div>In causal analysis, it is common for industrial systems to have a mixture of continuous and discrete variables, called distribution heterogeneity. In fact, discrete variables typically serve as external inputs to modulate continuous variables in these systems. Existing methods for causal discovery encounter the External Distorted Heterogeneity challenge. The challenge is defined as the difficulty of correcting the statistical relationships distorted by discrete inputs, interfering with the identification of causality within systems. To overcome the challenge, we propose a method called Input–Output Disentangled Causal Representation. The key idea is to reveal the continuous external control effects from discrete inputs and exclude the control effects from observed outputs to decouple the inference of causality. Technically, a reversible external control converter is designed to recover the continuous control effects from discrete input signals through affine processes, bridging the heterogeneity. In addition, we construct an additive causal model to distinguish between causal effects from inputs and outputs, capturing disentangled representations in a unified space through feature distribution alignment and discrimination. Dual predictions are designed to exclude the regulatory influences from observed outputs using gradient truncation, thereby decoupling the inference of causality. The proposed method demonstrates robust causal identification accuracy across diverse datasets and scenarios, outperforming existing approaches in high-dimensional input–output systems. These results highlight its potential for industrial applications in the causal discovery of input–output systems.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"154 ","pages":"Article 103521"},"PeriodicalIF":3.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144809632","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}

引用次数: 0

Harnessing monotonicity to design an adaptive PI passivity-based controller for a fuel-cell system 利用单调性设计燃料电池系统自适应PI无源控制器

IF 3.9 2区计算机科学

Journal of Process Control Pub Date : 2025-10-01 Epub Date: 2025-08-06 DOI: 10.1016/j.jprocont.2025.103511

Carlo A. Beltrán , Rafael Cisneros , Diego Langarica-Cordoba , Romeo Ortega , Luis H. Díaz-Saldierna

引用次数: 0

A contrastive generative network with feature-attribute consistency for zero-shot fault diagnosis in process industries 面向过程工业零爆故障诊断的特征-属性一致性对比生成网络

IF 3.9 2区计算机科学

Journal of Process Control Pub Date : 2025-10-01 Epub Date: 2025-08-25 DOI: 10.1016/j.jprocont.2025.103529

Lin Sha , Jiaqi Li , Min Wang , Shihang Yu , Sibo Qiao

{"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":"2025-10-01","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}

引用次数: 0

Robust online identification for hybrid multirate systems based on recursive EM algorithm 基于递归EM算法的混合多速率系统鲁棒在线辨识

IF 3.9 2区计算机科学

Journal of Process Control Pub Date : 2025-09-01 Epub Date: 2025-07-29 DOI: 10.1016/j.jprocont.2025.103514

Fan Guo , Biao Huang

引用次数: 0

Optimal controlled variable switching for global self-optimizing control of active-set change processes 活动集变化过程全局自优化控制的最优控制变量切换

IF 3.9 2区计算机科学

Journal of Process Control Pub Date : 2025-09-01 Epub Date: 2025-07-31 DOI: 10.1016/j.jprocont.2025.103509

Lingjian Ye , Feifan Shen , Zeyu Yang , Xiaofeng Yuan

{"title":"Optimal controlled variable switching for global self-optimizing control of active-set change processes","authors":"Lingjian Ye , Feifan Shen , Zeyu Yang , Xiaofeng Yuan","doi":"10.1016/j.jprocont.2025.103509","DOIUrl":"10.1016/j.jprocont.2025.103509","url":null,"abstract":"<div><div>For global self-optimizing control (SOC) of active-set change processes, we propose two approaches for optimal switching of the controlled variables (CVs), namely, the <em>descriptor function method</em> and the <em>partial switching method</em>. The descriptor function method designs self-optimizing CVs for each critical region, the change of operating region is monitored by the descriptor function, such that whether to the switch CVs is decided. This method is an extension of the previous switching strategy based on the local SOC, but constructs the descriptor function using the generalized global SOC (g<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>SOC) approach. In the second partial switching method, however, only part of the CVs that relate to varying active constraints are switched, while others are kept invariant over all critical regions, which are also solved using the g<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>SOC approach. In this method, common max/min selectors are employed to automatically switch the CVs, whenever necessary. Finally, practical design procedure and optimality of the proposed switching methods are illustrated using three simulated examples.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"153 ","pages":"Article 103509"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739222","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}

引用次数: 0