Journal of Process Control最新文献

{"title":"Optimal controlled variable switching for global self-optimizing control of active-set change processes","authors":"Lingjian Ye ,&nbsp;Feifan Shen ,&nbsp;Zeyu Yang ,&nbsp;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":"2025-07-31","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}

{"title":"Enhancing injection molding barrel temperature control performance using a data-guided simplex search method","authors":"Haipeng Zou ,&nbsp;Yongkuan Yang ,&nbsp;Quanxiang Ye ,&nbsp;Xiangsong Kong ,&nbsp;Yi Liu ,&nbsp;Zhijiang Shao","doi":"10.1016/j.jprocont.2025.103508","DOIUrl":"10.1016/j.jprocont.2025.103508","url":null,"abstract":"<div><div>The barrel temperature control system is one of the key components for process control of the injection molding machine, with its performance heavily influenced by the control parameter settings. However, the tuning process for these parameters is often both costly and cumbersome. Currently, data-driven techniques for parameter tuning are increasingly widespread, but the existing methods generally fail to exploit the information embedded in the previous iterations or datasets. To improve optimization efficiency through more effective data utilization, a Data-Guided Simplex Search method based on adjacent historical Centroid information (CDG-SS) is proposed. By reformulating the simplex iteration mechanism to establish the concept of quasi-gradient estimation, this method uncovers the intrinsic similarity between the gradient-free simplex search algorithm and conventional gradient-based methods in terms of their shared approximate gradient search properties. Building upon the concept of quasi-gradient estimation, this method utilizes historical centroid data from adjacent simplices to identify the current trend states of the optimization progress. Based on these states, a dynamic compensation mechanism is then designed according to distinct trend states, enabling adaptive adjustment of the optimization step sizes. This approach thereby improves the efficiency of the barrel temperature parameter tuning for injection molding machines. The simulation results demonstrate that the CDG-SS method significantly improves the efficiency of optimization for control of the barrel temperature. Compared to the original simplex search method, CDG-SS reduces the average number of iterations required for the Integral of Time multiplied by Absolute Error (ITAE) by 16.6% and for steady-state error by 12.1%, while maintaining comparable accuracy.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"153 ","pages":"Article 103508"},"PeriodicalIF":3.9,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739223","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}

{"title":"Robust online identification for hybrid multirate systems based on recursive EM algorithm","authors":"Fan Guo ,&nbsp;Biao Huang","doi":"10.1016/j.jprocont.2025.103514","DOIUrl":"10.1016/j.jprocont.2025.103514","url":null,"abstract":"<div><div>This paper focuses on robust identification for both linear time-invariant and time-variant multirate systems with time delays subject to outliers. The time delays are time varying and modeled by a Markov chain. Furthermore, the collected output data, which is corrupted by outliers, is described through a Laplace distribution. Parameters for the time-invariant model are estimated utilizing the batch expectation maximization (BEM) algorithm, whereas the recursive EM (REM) algorithm is employed for parameter estimation of the time-variant model. Upon receiving new data, the BEM first incorporates it in the historical batch data set and then iteratively recalculates parameter estimation using the updated data set. In contrast, the REM algorithm uses the parameter values obtained from the preceding step to recursively refine its estimates according to the new data sample. The efficacy of the proposed methods is demonstrated through a numerical example and a simulated continuous fermentation reactor process.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"153 ","pages":"Article 103514"},"PeriodicalIF":3.9,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144723267","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}

{"title":"Trustworthy data-driven model predictive control using a hybrid model with monitoring and adaptation of the domain of validity","authors":"Mohamed Elsheikh ,&nbsp;Yak Ortmanns ,&nbsp;Felix Hecht ,&nbsp;Volker Roßmann ,&nbsp;Stefan Krämer ,&nbsp;Sebastian Engell","doi":"10.1016/j.jprocont.2025.103496","DOIUrl":"10.1016/j.jprocont.2025.103496","url":null,"abstract":"<div><div>The quality of the plant model is a key factor for a successful implementation of model predictive control schemes. An inaccurate process model can result in unsatisfactory dynamics of the controlled system and may lead to violations of quality and safety constraints or even instability. Building reliable models that are based on physics and chemistry can be challenging in practice due to the difficulty of accurately modeling all aspects of the real system, and there will always be discrepancies between the model and the behavior of the real plant. Recently, there has been a renewed research trend to use or incorporate data-driven models that are obtained by machine learning algorithms into model predictive control (MPC). However, developing reliable standalone data-driven models needs large sets of training data that are obtained for sufficiently rich excitation of the system which are not often available in practice. A promising direction to overcome this issue is the use of hybrid process models which combine models based on first-principles and data-based elements. In this work, we present and evaluate a nonlinear model predictive control approach based on a hybrid model that is formed of a simplified first-principles model and a data-based model to capture the dynamics that are not adequately represented in the semi-rigorous model. In order to increase the reliability of the hybrid model, the domain of validity of the data-based model element is monitored and the contribution of the data-based model component is faded out when the plant is not operated in the region where sufficient data had been collected. Moreover it is proposed to adapt the domain of validity of the data-based component based on the measured data during operation. An extensive simulation study of an industrial control problem using a faithful simulation model is performed to investigate the potential of the approach for a typical complex application. The use of the hybrid model with and without adaptation of the domain of validity is compared to conventional nonlinear model predictive control using the simplified physics-based model, and a nonlinear model predictive controller based on a standalone data-driven model for different situations regarding the available data set for model training and the operating conditions of the plant.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"153 ","pages":"Article 103496"},"PeriodicalIF":3.3,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679154","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}

{"title":"Robust identification of linear parameter-varying dual-rate system with non-stationary heavy-tailed noise","authors":"Xiang Chen ,&nbsp;Ke Li ,&nbsp;Fei Liu","doi":"10.1016/j.jprocont.2025.103500","DOIUrl":"10.1016/j.jprocont.2025.103500","url":null,"abstract":"<div><div>This paper addresses the identification of linear parameter-varying (LPV) dual-rate systems with non-stationary heavy-tailed measurement noise using a variational Bayesian (VB) approach. It provides a comprehensive analysis of dual-rate sampling and noise distribution variations commonly found in system data. To model the outliers, the Student’s <em>t</em> distribution is employed, and a Bernoulli variable is introduced to construct a Gaussian-Student’s <em>t</em> mixture (GTM) distribution that accounts for non-stationary heavy-tailed noise. The GTM distribution is then transformed into a Gaussian hierarchical model to develop a probabilistic representation of the system. Given the unknown process outputs in the regression vector, this study employs a modified Kalman filter for estimation. Based on the obtained estimates and observed data, a prior distribution is defined to establish a Bayesian framework, allowing for iterative parameter estimation via the VB approach. Finally, the effectiveness of this algorithm is validated through a numerical example and a cascaded tank system.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"153 ","pages":"Article 103500"},"PeriodicalIF":3.3,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672331","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}

{"title":"Causality-driven sequence-to-sequence gated recurrent unit for performance-indicator-related root cause diagnosis","authors":"Chengtian Wang,&nbsp;Hongbo Shi,&nbsp;Bing Song,&nbsp;Yang Tao,&nbsp;Kun Wang","doi":"10.1016/j.jprocont.2025.103428","DOIUrl":"10.1016/j.jprocont.2025.103428","url":null,"abstract":"<div><div>Process monitoring and root cause diagnosis (RCD) are significant for maintaining process safety and ensuring product quality in industrial processes. Existing RCD methods have achieved great success under linear and stationary assumptions, which limits their application in complex industrial processes. In addition, causality analysis of the performance indicator (PI) helps to precisely identify the path of propagation of faults and locate the root cause that leads to performance degradation. However, PI is not online measurable, which makes it difficult to achieve PI-related RCD in time. A novel causality-driven sequence-to-sequence gated recurrent unit (CSGRU) is proposed for PI-related RCD to address these issues. The proposed method is built under the distributed process monitoring framework based on a PI-related process decomposition strategy to first locate the faulty unit. CSGRU is integrated with the concept of Granger causality (GC) to learn nonlinear and dynamic causal dependencies. Sequence-to-sequence multi-task learning is introduced to avoid time-consuming pairwise causal analysis and improves the predictive accuracy even under strong sparsity constraints. The predictive contribution statistic is built to obtain the real-time faulty causal graph, which contains the causal impacts on PI without using online PI data. Finally, through a reverse causal inference from effect to cause, the fault propagation path is identified backward from PI, and the root cause is subsequently located, which leads to the degradation of PI. The effectiveness of the proposed method is validated on two benchmarks, the Tennessee Eastman process (TEP) and the vinyl acetate monomer (VAM) plant model, and a real industrial application on the three-phase flow facility (TPF).</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"153 ","pages":"Article 103428"},"PeriodicalIF":3.3,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632254","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}

{"title":"A Metropolis–Hastings-within-Gibbs approach for nonlinear state–space system estimation","authors":"Wenxin Sun ,&nbsp;Hongtian Chen ,&nbsp;Chao Shang ,&nbsp;Weili Xiong ,&nbsp;Biao Huang","doi":"10.1016/j.jprocont.2025.103490","DOIUrl":"10.1016/j.jprocont.2025.103490","url":null,"abstract":"<div><div>This paper presents a new Metropolis–Hastings-within-Gibbs (MH–Gibbs) sampling method for state-estimation and parameter-identification in nonlinear state–space systems. Compared to the conventional filtering and smoothing approaches, the proposed method offers substantial improvements in both time efficiency and memory usage, while maintaining effective estimation accuracy. Furthermore, owing to the high efficiency of the proposed state-estimation method, a new approach is proposed to approximate the gradient of the log-likelihood function with respect to the system-parameters, which facilitates parameter-identification. Case studies on three benchmark systems show that: (1) compared to the forward-filtering–backward-smoothing approach, the proposed state-estimation method achieves comparable accuracy with only one-tenth the computational time; and (2) the proposed parameter-identification method has reasonable accuracy.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"153 ","pages":"Article 103490"},"PeriodicalIF":3.3,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632253","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}

{"title":"On optimal control of hybrid dynamical systems using complementarity constraints","authors":"Saif R. Kazi ,&nbsp;Kexin Wang ,&nbsp;Lorenz Biegler","doi":"10.1016/j.jprocont.2025.103492","DOIUrl":"10.1016/j.jprocont.2025.103492","url":null,"abstract":"<div><div>Optimal control for switch-based dynamical systems is a challenging problem in the process control literature. In this study, we model these systems as hybrid dynamical systems with finite number of unknown switching points and reformulate them using non-smooth and non-convex complementarity constraints as a mathematical program with complementarity constraints (MPCC). We utilize a moving finite element based strategy to discretize the differential equation system to accurately locate the unknown switching points at the finite element boundary and achieve high-order accuracy at intermediate non-collocation points. We propose a globalization approach to solve the discretized MPCC problem using a mixed NLP/MILP-based strategy to converge to a non-spurious first-order optimal solution. The method is tested on three dynamic optimization examples, including a gas–liquid tank model and an optimal control problem with a sliding mode solution.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"153 ","pages":"Article 103492"},"PeriodicalIF":3.3,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572506","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}

{"title":"Capturing principal features in slow industrial processes for anomaly detection application","authors":"Kai Wang ,&nbsp;Xinlong Yuan ,&nbsp;Zihui Cao ,&nbsp;Gecheng Chen ,&nbsp;Xiaofeng Yuan ,&nbsp;Chunhua Yang ,&nbsp;Yalin Wang ,&nbsp;Le Zhou","doi":"10.1016/j.jprocont.2025.103487","DOIUrl":"10.1016/j.jprocont.2025.103487","url":null,"abstract":"<div><div>Dynamics is the fundamental characteristic of running industrial processes, and online industrial anomaly detection requires satisfactory real-time property and precision. Uncertain noise and outliers are also common in industrial data, sharply decreasing the performance of data models. Moreover, the embedded dimension has been a consensus for high-dimensional processes because several factors drive the plant. Based on these rationales, we propose a new dynamic anomaly detection strategy named robust principal slow feature analysis (RPSFA). This method could preserve the local geometric structure and is robust to outliers. Moreover, the proposed method effectively realizes information-noise separation to improve detection performance. Two pairs of detection statistics are constructed to concurrently monitor the process’s steady state deviation, dynamic characteristics change, noise anomaly, and the breakdown of variable correlations. A numerical case and a simulated industrial cascaded continuous stirred tank heater process are used to present the superiority of the proposed method.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"153 ","pages":"Article 103487"},"PeriodicalIF":3.3,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144571147","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}

{"title":"A novel few-shot fault diagnosis model for addressing nonstationarity in the ironmaking process","authors":"Xujie Zhang ,&nbsp;Chunjie Yang ,&nbsp;Ming Ge ,&nbsp;Siwei Lou ,&nbsp;Yuelin Yang ,&nbsp;Ping Wu","doi":"10.1016/j.jprocont.2025.103491","DOIUrl":"10.1016/j.jprocont.2025.103491","url":null,"abstract":"<div><div>The fourth industrial revolution is a green industrial revolution represented by artificial intelligence, clean energy, and other fields, which is both a challenge and an opportunity for the blast furnace ironmaking process (BFIP). Considering the dynamics, nonlinearity, nonstationarity, few shots, and many outliers in BFIP fault diagnosis, we proposed a novel method called Slow Feature Constrained-Least Squares Improved Generative Adversarial Network (SFC-LSIGAN). First, the sliding window is used to explore the process dynamics, while the deep learning model could better extract the deep nonlinearity between variables. Secondly, aiming at the properties of few shots and nonstationarity in BFIP, a new model was proposed based on the similar training process of Auxiliary Classifier GAN (ACGAN) and Deep Slow Feature Analysis (DSFA). Therefore, while completing the task of few-shot fault diagnosis, the proposed method further extracts the nonstationarity to improve the accuracy of the model. Furthermore, many outliers in the BFIP data are likely to have an impact on the quality of the generated samples. The least squares loss form function was introduced to enhance the quality of the generated samples and alleviate the mode collapse problem during the proposed model training process. Experiments on a real BFIP showed that, compared with the state-of-the-art methods, our SFC-LSIGAN method achieved superior performance in both data enhancement and fault diagnosis.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"152 ","pages":"Article 103491"},"PeriodicalIF":3.3,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557290","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}