Journal of Process Control最新文献_第2页

Graph-regularized data-driven control: Simultaneous optimization across multiple operating conditions 图正则化数据驱动控制：跨多个操作条件的同时优化

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-06-21 DOI: 10.1016/j.jprocont.2025.103486

Sanga Takagi , Osamu Kaneko

引用次数: 0

Explorative Policy Optimization for industrial-scale operation of complex process control systems 复杂过程控制系统工业规模运行的探索性政策优化

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-06-19 DOI: 10.1016/j.jprocont.2025.103471

Zengjun Zhang , Shaoyuan Li , Yaru Yang

{"title":"Explorative Policy Optimization for industrial-scale operation of complex process control systems","authors":"Zengjun Zhang , Shaoyuan Li , Yaru Yang","doi":"10.1016/j.jprocont.2025.103471","DOIUrl":"10.1016/j.jprocont.2025.103471","url":null,"abstract":"<div><div>With the advancement of industrial automation, traditional process control methods increasingly struggle to manage the complex operational demands of industrial-scale chemical processes, particularly in the presence of unmodelled dynamics and high nonlinearity. This paper introduces an advanced reinforcement learning algorithm, Explorative Policy Optimization (EPO), specifically developed to optimize operational strategies, focusing on improving both production yield and product quality in such environments. The core innovation of the EPO algorithm is its exploration network, which dynamically adjusts exploration strategies based on discrepancies between predicted and actual values of state–action pairs, enabling more effective exploration. This approach improves decision-making by providing more accurate outcome assessments in complex and unmodelled conditions. EPO also integrates exploration data into the advantage function, ensuring a balance between exploration and exploitation, which is essential for optimizing performance in dynamic environments that require both safety and adaptability. EPO focuses on global optimization in processes with multiple operating conditions and steady states. It surpasses existing RL methods in overall performance while maintaining acceptable computational costs across a wide range of industrial settings. Its effectiveness and practicality are demonstrated through industrial-scale simulation experiments.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"152 ","pages":"Article 103471"},"PeriodicalIF":3.3,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313608","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

Variational masking progressive learning method for multi-rate industrial processes soft sensor 多速率工业过程软传感器的变分掩蔽渐进式学习方法

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-06-17 DOI: 10.1016/j.jprocont.2025.103488

Xuan Hu , Peihao Zheng , Hao Wu , Yongming Han , Zhiqiang Geng

{"title":"Variational masking progressive learning method for multi-rate industrial processes soft sensor","authors":"Xuan Hu , Peihao Zheng , Hao Wu , Yongming Han , Zhiqiang Geng","doi":"10.1016/j.jprocont.2025.103488","DOIUrl":"10.1016/j.jprocont.2025.103488","url":null,"abstract":"<div><div>Deep learning has been widely used in industrial processes to predict critical quality indicators. However, existing methods assume that industrial process data are uniformly sampled, which is far from real industrial scenarios. To solve the problem of multi-rate sampling in industrial processes, a variational masking progressive learning (VMPL) method is proposed for multi-rate industrial processes soft sensor. In the VMPL, a multi-rate decomposition strategy (MDS) is first developed to construct generalized multi-rate data and corresponding masking matrix. Then, based on the MDS, a variational masking network (VMN) is designed to represent the uncertain distribution information of industrial process data. Meanwhile, the progressive learning (PL) algorithm is derived to assist the VMN in transferring process features from high-rate to low-rate data. Therefore, the VMPL can progressively mine features in different rates data without changing the structure of the VMN to improve soft-sensing accuracy. Finally, compared with the state-of-the-art multi-rate soft sensor model on the three key quality variable datasets of the catalytic cracking process, the VMPL achieves more accurate soft sensing results.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"152 ","pages":"Article 103488"},"PeriodicalIF":3.3,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297541","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

A missing data imputation method for industrial soft sensor modeling 工业软传感器建模中的缺失数据输入方法

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-06-17 DOI: 10.1016/j.jprocont.2025.103485

Dongnian Jiang , Haowen Yang , Huichao Cao , Dezhi Xu

{"title":"A missing data imputation method for industrial soft sensor modeling","authors":"Dongnian Jiang , Haowen Yang , Huichao Cao , Dezhi Xu","doi":"10.1016/j.jprocont.2025.103485","DOIUrl":"10.1016/j.jprocont.2025.103485","url":null,"abstract":"<div><div>Data on complex industrial processes are often missing, due to sensor or equipment malfunctions; this poses challenges for the prediction of important quality variables and soft sensor applications, and may have a significant impact on production processes and equipment maintenance. Traditional missing data imputation methods face challenges in terms of acquiring data distributions, structures, etc., and are detached from the downstream soft sensor tasks, as they do not consider the close connections and synergistic relationships between missing data imputation and soft sensors. This affects the filling results for important quality variables, and thus reduces the prediction accuracy of the downstream soft sensors. To address these issues, a missing data imputation method for industrial soft sensor modeling, called PFIDM, is proposed that can realize a customized data imputation process with a progressive feedback strategy. The loss function of the improved diffusion model (IDDPM) is rationally designed to introduce KL dispersion between the noise addition process and the data distribution corresponding to the generation process into the next step of noise prediction, which involves predicting and correcting the noise of the current data state. In addition, a dynamic step decay factor related to the noise intensity is defined in the sampling process, and the sampling step span is adaptively adjusted to reduce the number of sampling steps and to accelerate the sampling time. The superiority of the proposed method is verified by comparing several typical methods and instantiating a dataset.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"152 ","pages":"Article 103485"},"PeriodicalIF":3.3,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297542","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

Methodological and computational framework for model-based design of parallel experiment campaigns under uncertainty 不确定条件下基于模型的并行实验设计方法与计算框架

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-06-17 DOI: 10.1016/j.jprocont.2025.103465

Marco Sandrin , Constantinos C. Pantelides , Benoît Chachuat

{"title":"Methodological and computational framework for model-based design of parallel experiment campaigns under uncertainty","authors":"Marco Sandrin , Constantinos C. Pantelides , Benoît Chachuat","doi":"10.1016/j.jprocont.2025.103465","DOIUrl":"10.1016/j.jprocont.2025.103465","url":null,"abstract":"<div><div>The model-based determination of maximally-informative campaigns involving multiple parallel experimental runs remains a challenging task. Effort-based methodologies are well suited to the design of such experiment campaigns through discretizing the experiment control domain into a finite sample of candidate experiments. However, this approach can lead to suboptimal results if the discretization fails to cover the experiment domain sufficiently well. We present a comprehensive computational framework that combines an effort-based optimization step with a gradient-based refinement as part of an iterative procedure. The convexity of classical design criteria in the effort space allows for a globally optimal effort selection over the discretization, which is exploited to warm-start the gradient-based search for a refined discretization. Our framework also considers parametric model uncertainty by formulating risk-inclined, risk-neutral and risk-averse design criteria, and it enables the solution of exact designs in the effort-based step. Through the case study of a fed-batch fermentation, we show that the integrated effort-based optimization with gradient-based refinement procedure consistently outperforms an effort-only optimization. The results demonstrate the benefits of robust design approaches compared to their local counterparts, and establish the computational tractability of the framework in computing robust experiment campaigns with up to a dozen dimensions.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"152 ","pages":"Article 103465"},"PeriodicalIF":3.3,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297543","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

A cloud–edge collaborative hierarchical diagnosis framework for key performance indicator-related faults in manufacturing industries 制造业关键绩效指标相关故障的云边缘协同分层诊断框架

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-06-11 DOI: 10.1016/j.jprocont.2025.103462

Xueyi Zhang , Liang Ma , Kaixiang Peng , Chuanfang Zhang , Muhammad Asfandyar Shahid , Yangfan Wang

{"title":"A cloud–edge collaborative hierarchical diagnosis framework for key performance indicator-related faults in manufacturing industries","authors":"Xueyi Zhang , Liang Ma , Kaixiang Peng , Chuanfang Zhang , Muhammad Asfandyar Shahid , Yangfan Wang","doi":"10.1016/j.jprocont.2025.103462","DOIUrl":"10.1016/j.jprocont.2025.103462","url":null,"abstract":"<div><div>In the context of intensifying global market competition and the accelerated advancement of industrial intelligence powered by the Industrial Internet of Things, manufacturing enterprises face pressing challenges in achieving sustainable development through quality and efficiency enhancement. Effective key performance indicators (KPIs) related fault diagnosis plays a crucial role in ensuring product quality stability and efficient production within modern manufacturing industries. However, manufacturing industries are characterized by numerous production sub-processes, hierarchical cooperation and interaction, and complex spatio-temporal features, making the implementation of comprehensive KPI-related fault diagnosis methods challenging. To overcome these challenges and fully leverage the hierarchical and multi-scale nature of manufacturing systems, an innovative hierarchical KPI-related fault diagnosis framework based on cloud–edge collaboration is proposed in this paper. First, a hierarchical information enhancement method utilizing dual-scale slow feature analysis and minimal gated units is developed to handle the multi-scale nature of system levels. Second, graph attention networks are combined with minimal gated units to capture the spatio-temporal dynamics across all levels, and KPI constraints are incorporated to fully extract the KPI-related spatio-temporal features. In addition, bottom-up propagation and top-down updation strategies are designed to facilitate information interaction between levels. Building on this, a cloud–edge collaborative architecture is developed, with specific tasks assigned each side. Finally, the framework is applied to a collaborative prototype system for cloud–edge- device in the hot rolling process, and its effectiveness and applicability are thoroughly evaluated.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"152 ","pages":"Article 103462"},"PeriodicalIF":3.3,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255437","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

Late lumping controlled variable design for transport reaction processes 输运反应过程的后期集总控制变量设计

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-06-11 DOI: 10.1016/j.jprocont.2025.103464

Xinhui Tang , Chenchen Zhou , Hongxin Su , Yi Cao , Shuang-Hua Yang

{"title":"Late lumping controlled variable design for transport reaction processes","authors":"Xinhui Tang , Chenchen Zhou , Hongxin Su , Yi Cao , Shuang-Hua Yang","doi":"10.1016/j.jprocont.2025.103464","DOIUrl":"10.1016/j.jprocont.2025.103464","url":null,"abstract":"<div><div>Transport reaction processes (TRPs) are inherently infinite-dimensional in space, and achieving optimal distributions of their physical quantities under disturbances is a common challenge. Discretizing such systems into finite-dimensional models and then devising an optimization scheme, is the mainstream route. Among these strategies, a relatively new distributed parameter self-optimizing control (SOC) yields acceptable losses by maintaining controlled variables (CVs) designed offline at constants, which avoids repeated online optimization and is suitable for sufficiently precise discrete TRPs without incurring significant online computational costs. However, any model reduction can cause the loss of critical system information and compromises the optimality of SOC. In this work, a late lumping SOC method that avoids spatial approximation entirely during design phases is developed for TRPs. Using the late lumping null space theorem and the analytical derived sensitivity operator, optimal CVs can be identified to achieve a near-zero loss. The sensitivity for typical TRPs is determined analytically through the theories of differential equations and adjoint operators. Two simulation experiments involving TRPs with convection and diffusion phenomena demonstrate the effectiveness of the proposed method.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"152 ","pages":"Article 103464"},"PeriodicalIF":3.3,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262298","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

Feature hybrid fusion-based fault diagnosis of multi-scale and multi-stage industrial processes 基于特征混合融合的多尺度多阶段工业过程故障诊断

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-06-10 DOI: 10.1016/j.jprocont.2025.103460

Datong Li, Jun Lu, Tongkang Zhang, Jinliang Ding

{"title":"Feature hybrid fusion-based fault diagnosis of multi-scale and multi-stage industrial processes","authors":"Datong Li, Jun Lu, Tongkang Zhang, Jinliang Ding","doi":"10.1016/j.jprocont.2025.103460","DOIUrl":"10.1016/j.jprocont.2025.103460","url":null,"abstract":"<div><div>The multi-stage production workflows of modern industrial processes pose significant challenges in establishing fault diagnosis models. In addition, the conventional methods struggle to address the inter-stage dependencies and intra-stage multi-scale pattern representations. To this end, this paper proposes a <u>F</u>ault <u>D</u>iagnosis model for <u>M</u>ulti-<u>S</u>tage industrial processes (MSFD) with hybrid domain knowledge and cosine similarity fusion strategies, which can discover a comprehensive plant-wise representation through aggregating the stage-wise multi-scale features of multivariate time-series (MTS) process data. Specifically, the MSFD framework consists of two core components. Firstly, an MTS feature extraction (MTS-FE) module independently captures the multi-scale features of the process data within each production stage on local patterns and broader trends. This module can ensure a more refined stage-wise representation. Following this module, a hybrid feature fusion (HFF) module is proposed based on two distinct strategies – domain knowledge and cosine similarity – to integrate the stage-wise features into a unified representation. This dual strategy bridges semantic process constraints of domain knowledge-guided feature weighting with statistical data patterns cosine similarity-based cross-stage dependency learning, enabling a holistic and context-aware fault diagnosis across sequential workflows. The experiments are conducted on a benchmark dataset and a real-world dataset to demonstrate the effectiveness and superiority of the proposed approach.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"152 ","pages":"Article 103460"},"PeriodicalIF":3.3,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144242463","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

Cross correlation-based blocking whitening slow feature analysis for coupled-data industrial process monitoring 耦合数据工业过程监测中基于交叉相关的分块白化慢特征分析

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-06-06 DOI: 10.1016/j.jprocont.2025.103461

Jiao Meng, Xin Huo, Hewei Gao, Changchun He

{"title":"Cross correlation-based blocking whitening slow feature analysis for coupled-data industrial process monitoring","authors":"Jiao Meng, Xin Huo, Hewei Gao, Changchun He","doi":"10.1016/j.jprocont.2025.103461","DOIUrl":"10.1016/j.jprocont.2025.103461","url":null,"abstract":"<div><div>Modern industrial systems are characterized by high complexity, strong coupling, and multi-source data interactions. Practical industrial process data are mostly nonlinear, dynamical, and manifested as temporal correlation, which makes it challenging for traditional monitoring methods to accurately capture the changes in internal state variables. To this end, a multi-block whitening slow feature analysis (MBW-SFA) approach is proposed in this paper, which utilizes the cross maximum information coefficient (CMIC) for nonlinear correlation analysis, blocking, as well as selective whitening transformation (SWT) for the nonlinear and strongly coupled process variables, so as to avoid the information loss caused by global whitening. The proposed MBW-SFA approach performs manifold mapping on the data with strong correlations, preserving key information while reducing dimensionality, and the selective whitening transformation is applied to prevent information loss across different variables. In addition, for scenarios involving partially known fault data, this study proposes a control limit optimization (CLO) function that balances fault identification and false alarms to calculate control limit thresholds based on slow feature monitoring statistics, achieving objective monitoring of industrial processes. The proposed approach is experimentally validated on Tennessee Eastman process, where the correlation between process variables is investigated, and the results show that the proposed method achieves excellent performance in process monitoring.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"152 ","pages":"Article 103461"},"PeriodicalIF":3.3,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231487","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

A knowledge transfer-based intelligent decision support method for fault management 基于知识转移的故障管理智能决策支持方法

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2025-05-24 DOI: 10.1016/j.jprocont.2025.103452

Chang Tian , Pengcheng Gao , Feng Yin , Haidong Fan , Xiang Gao

{"title":"A knowledge transfer-based intelligent decision support method for fault management","authors":"Chang Tian , Pengcheng Gao , Feng Yin , Haidong Fan , Xiang Gao","doi":"10.1016/j.jprocont.2025.103452","DOIUrl":"10.1016/j.jprocont.2025.103452","url":null,"abstract":"<div><div>In practical operations, fault management often depends on the expertise of onsite operators, yet manual judgments are limited in timeliness and consistency. To support onsite operators, this paper proposes a decision support approach to recommend the optimal intervention action for fault by comparing risk-reward of candidate actions. A significant challenge is quantifying action rewards, due to the unavailability of data on action consequences during the decision stage. In response, we introduce a symptom description-based knowledge transfer to evaluate action rewards without such data. First, risk prototypes are introduced, which are trained by historical fault data to transform risk magnitude into quantifiable distances between the prototypes. Then, fault symptom descriptions are incorporated as risk knowledge, upon which a generalized mapping function between risk prototypes and symptoms is established. This mapping function is realized through a zero-shot learning paradigm, enabling the knowledge transfer from observed symptoms to those not yet seen. Finally, an online recommendation strategy is developed, which identifies residual symptoms post-action and maps these to the risk prototypes in the feature space. By analyzing the distances between post-action risk prototypes, the risk-reward of actions is assessed, allowing for action recommendations based on their risk-reward rankings. The proposed method is validated by the benchmark Tennessee Eastman process. The results show that with a well-designed symptom matrix, it is possible to identify the optimal intervention action for fault management under zero-sample conditions.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"151 ","pages":"Article 103452"},"PeriodicalIF":3.3,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130814","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