Journal of Process Control最新文献

{"title":"A knowledge transfer-based intelligent decision support method for fault management","authors":"Chang Tian ,&nbsp;Pengcheng Gao ,&nbsp;Feng Yin ,&nbsp;Haidong Fan ,&nbsp;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}

{"title":"Adaptive model predictive control with one free control move for uncertain discrete-time linear systems with bounded disturbance","authors":"Yingjie Hu ,&nbsp;Jian Ye ,&nbsp;Herbert Ho-Ching Iu ,&nbsp;Tyrone Fernando ,&nbsp;Xinan Zhang","doi":"10.1016/j.jprocont.2025.103450","DOIUrl":"10.1016/j.jprocont.2025.103450","url":null,"abstract":"<div><div>This paper proposes an adaptive model predictive control (MPC) with one free control move for uncertain discrete-time linear systems with additive bounded disturbance. With the set-based parameter estimation strategy, the estimated parameters and uncertainty set are simultaneously updated to adapt the true system model online. Then the parameter uncertainty set is used by the modified MPC with one free control move to enhance the control performance. By utilizing the quadratic boundedness (QB) condition, the robust min–max MPC optimization formulation with the infinite horizon is converted into the form of a series of standard linear matrix inequalities (LMIs). Furthermore, the recursive feasibility and robust stability of the proposed method are rigorously proven, respectively. In addition, a numerical simulation example is considered to verify the validity of the designed algorithm.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"151 ","pages":"Article 103450"},"PeriodicalIF":3.3,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144099245","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":"Noncollocated feedback stabilization of a heat conduction process on a nonuniform ring","authors":"Xiu-Fang Yu ,&nbsp;Jun-Min Wang ,&nbsp;Jun-Jun Liu","doi":"10.1016/j.jprocont.2025.103447","DOIUrl":"10.1016/j.jprocont.2025.103447","url":null,"abstract":"<div><div>In this article, we study the stabilization of a 1-d heat conduction process on a nonuniform ring, where the heat flux at <span><math><mrow><mi>z</mi><mo>=</mo><mn>1</mn></mrow></math></span> is fed back to <span><math><mrow><mi>z</mi><mo>=</mo><mn>0</mn></mrow></math></span> through a recycle loop, and two noncollocated point temperatures at <span><math><mrow><mi>z</mi><mo>=</mo><mn>1</mn></mrow></math></span> and <span><math><mrow><msub><mrow><mi>z</mi></mrow><mrow><mn>0</mn></mrow></msub><mo>∈</mo><mrow><mo>(</mo><mn>0</mn><mo>,</mo><mn>1</mn><mo>)</mo></mrow></mrow></math></span> are available to be measured. The instability of the heat system comes from two parts: one is the boundary recycle, and the other is the distributed terms of the heat equation. The static output control is designed at the left boundary <span><math><mrow><mi>z</mi><mo>=</mo><mn>0</mn></mrow></math></span> to overcome the instability, and the admissible value ranges of feedback gains are concluded by spectral analysis so that the closed-loop system is shown to be well-posed and exponentially stable. The numerical simulations are carried out to demonstrate the effectiveness of the proposed controller.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"151 ","pages":"Article 103447"},"PeriodicalIF":3.3,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072695","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":"Nonlinear principal component analysis with random Bernoulli features for process monitoring","authors":"Ke Chen,&nbsp;Dandan Jiang","doi":"10.1016/j.jprocont.2025.103449","DOIUrl":"10.1016/j.jprocont.2025.103449","url":null,"abstract":"<div><div>The process generates substantial amounts of data with highly complex structures, leading to the development of numerous nonlinear statistical methods. However, most of these methods rely on computations involving large-scale dense kernel matrices. This dependence poses significant challenges in meeting the high computational demands and real-time responsiveness required by online monitoring systems. To alleviate the computational burden of dense large-scale matrix multiplication, we incorporate the bootstrap sampling concept into random feature mapping and propose a novel random Bernoulli principal component analysis method to efficiently capture nonlinear patterns in the process. We derive a convergence bound for the kernel matrix approximation constructed using random Bernoulli features, ensuring theoretical robustness. Subsequently, we design four fast process monitoring methods based on random Bernoulli principal component analysis to extend its nonlinear capabilities for handling diverse fault scenarios. Finally, numerical experiments and real-world data analyses are conducted to evaluate the performance of the proposed methods. Results demonstrate that the proposed methods offer excellent scalability and reduced computational complexity, achieving substantial cost savings with minimal performance loss compared to traditional kernel-based approaches.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"151 ","pages":"Article 103449"},"PeriodicalIF":3.3,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143934511","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 predictive tracking fault-tolerant control for multiphase switched systems with asynchronous switching: A Lyapunov–Razumikhin method","authors":"Huiyuan Shi ,&nbsp;Pu Jiang ,&nbsp;Hui Li ,&nbsp;Chengli Su ,&nbsp;Ping Li","doi":"10.1016/j.jprocont.2025.103451","DOIUrl":"10.1016/j.jprocont.2025.103451","url":null,"abstract":"<div><div>This paper develops a robust predictive tracking fault-tolerant control approach for a class typical of multiphase switched systems, i.e., multiphase batch processes, accompanied by asynchronous switching, small time delays, partial actuator faults and disturbances. First, an equivalent extended asynchronous switching model, including a match sub-model and a mismatch sub-model, is built. In this model, the Lyapunov–Razumikhin function method is chosen to handle time delays due to its ability to make the original states of the systems remain invariant set characteristics. Meanwhile, this method has the characteristics of small computation and low conservativeness in solving the linear matrix inequalities, which is appropriate for systems with small delays. Next, according to the stable sufficient conditions based on robust positively invariant sets and terminal constraint sets, the controller gains, the minimum and maximum dwell time are solved online to eliminate the asynchronous switching situation. Moreover, unlike the iterative learning method with globally constant controller gain, its system state cannot change in real time with the action of the desired controller gain, making state deviations occur over time. In contrast, the controller gain in this method can be corrected and updated to avoid state deviation issue in real time. Finally, a simulation case of injection molding process is used to demonstrate the feasibility of the proposed approach.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"151 ","pages":"Article 103451"},"PeriodicalIF":3.3,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922371","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":"Design and comparison of PI and boundary-based predictive controller for control of aeration in activated sludge bioreactor – Simulation and laboratory research","authors":"Tomasz Ujazdowski ,&nbsp;Robert Piotrowski ,&nbsp;Witold Nocoń ,&nbsp;Krzysztof Stebel ,&nbsp;Jakub Pośpiech","doi":"10.1016/j.jprocont.2025.103446","DOIUrl":"10.1016/j.jprocont.2025.103446","url":null,"abstract":"<div><div>In this paper, a classical PI and an on/off predictive boundary-based predictive controller (BBPC) algorithms are compared and verified using an ASM3-based model of an activated sludge system with a reactor and secondary settler. A laboratory-scale activated sludge setup is modelled in MATLAB/Simulink and verified using experimental data. BBPC and PI algorithms are compared in two scenarios of batch and continuous operation of the activated sludge process. To accommodate the on/off nature of the actuator, a pulse-width modulation (PWM) module is added to the PI controller, but a modification in the computation of control error is still needed for the PI to control the process properly. The BBPC, on the other hand, while its implementation is complex, proves to be superior in its ability to limit the control costs, the number of switching of the actuator and most importantly, in its ability to instantaneously compensate for the changes in process load.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"151 ","pages":"Article 103446"},"PeriodicalIF":3.3,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887691","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":"Multi-objective optimization method for cement calcination system based on dual population differential evolution algorithm","authors":"Xunian Yang,&nbsp;Liteng An,&nbsp;Yong Gao,&nbsp;Xiaochen Hao","doi":"10.1016/j.jprocont.2025.103448","DOIUrl":"10.1016/j.jprocont.2025.103448","url":null,"abstract":"<div><div>The cement calcination system (CCS) demonstrates a high degree of coupling among operational indicators and experiences significant dynamic variations in its operating conditions. Traditional parameter‑setting methods based on empirical experience are insufficient for achieving coordinated optimization of energy consumption and product quality. To address these challenges, this study proposes a multi-objective optimization approach based on the Dual-Population Differential Evolution (DP-DE) algorithm, intended to ensure the CCS operates stably and efficiently in terms of energy consumption, while concurrently enhancing product quality. The proposed approach initially formulates a multi-objective optimization model that accounts for electricity consumption, coal consumption, and clinker quality, and integrates electricity and coal prices to weight the energy cost component. For the optimization process, a two-stage differential evolution algorithm employing a “decision-first, optimization-later” strategy is developed, in conjunction with a dynamic search-space partitioning mechanism to facilitate multi-step, smooth adjustments of controlled variable setpoints. To accommodate the nonlinear characteristics of complex industrial processes, Convolutional Neural Network(CNN) and Convolutional Neural Network-Long Short-Term Memory Network(CNN-LSTM)-based neural network fitness functions are constructed to capture relationships between process variables and target indicators from historical data, thereby enabling effective mappings from the solution space to the objective space. Experimental results indicate that, under stable operating conditions, this approach reduces energy costs by 3.1 % while maintaining clinker quality within acceptable limits. Furthermore, robustness experiments, which involve repeated trials with randomly initialized populations and minor input perturbations, confirm that the algorithm maintains consistent optimization trajectories and yields stable results under uncertainty, thereby demonstrating favorable engineering deployability.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"151 ","pages":"Article 103448"},"PeriodicalIF":3.3,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887690","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 and constrained tracking of PSV interface using convolutional neural networks and optimistic moving horizon estimation","authors":"Junyao Xie ,&nbsp;Huiping Liang ,&nbsp;Mahmut Berat Tatlici ,&nbsp;Biao Huang","doi":"10.1016/j.jprocont.2025.103432","DOIUrl":"10.1016/j.jprocont.2025.103432","url":null,"abstract":"<div><div>This manuscript proposes a novel video-based robust and constrained estimation framework using the convolutional neural network and optimistic moving horizon estimation, with applications in interface estimation of oil sand primary separation vessels (PSV). Although convolutional neural networks have achieved notable success across various computer vision and image analysis tasks, image outliers (such as blocking, blurriness, and lighting variations) would inevitably affect recognition/tracking performance. To address this issue, this manuscript proposes a robust estimation approach by leveraging a convolutional neural network and moving horizon estimation. Along this line, the interface recognition results by the convolutional neural network can be modeled as the measurements corrupted by disturbances and outliers, and the internal states can be modeled through a discrete-time finite-dimensional state space model. More importantly, the ubiquitously present constraints in the estimation task can be explicitly and readily handled by the moving horizon estimation. The stability analysis of the proposed method is provided in the presence of disturbances and model-plant mismatch. The effectiveness of the proposed method is validated through a pilot-scale laboratory study and an industrial primary separation vessel case study.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"151 ","pages":"Article 103432"},"PeriodicalIF":3.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878717","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":"Data-driven predictive adaptive iterative learning fault-tolerant control for networked batch processes","authors":"Chengyu Zhou ,&nbsp;Li Jia ,&nbsp;Feng Li ,&nbsp;Jianfang Li","doi":"10.1016/j.jprocont.2025.103431","DOIUrl":"10.1016/j.jprocont.2025.103431","url":null,"abstract":"<div><div>This article studies the fault-tolerant control (FTC) problem for a class of networked nonlinear batch processes. Firstly, the controlled batch process is converted to an adaptive data-driven model equivalent to the original system by using the iterative dynamic linearization technique, with actuator faults and fading communication phenomena considered in the control input and output channel, respectively. Among them, the fading communication phenomenon is modeled as an independent identically distributed over the iteration and time domains with known mathematical expectation and variance. Then, by fully combining the idea of predictive control and the output fading compensation algorithm, the data-driven predictive adaptive iterative learning FTC (DDPAILFTC) scheme is designed based on the dual-domain (iteration and time domains) compensation mechanism, which can avoid a short-sighted control decision and suppress the adverse effect brought by fading communication. Next, the strict convergence analysis of the presented DDPAILFTC approach is carried out by using the contraction mapping principle. The design and analysis process of the control scheme is completely data-driven and does not require any explicit model information. Ultimately, the effectiveness of the developed control method is demonstrated with a temperature tracking control example of a nonlinear batch reactor. The results show that the proposed DDPAILFTC strategy reduces the average MAE, average MSE, and calculation time by 20%, 21 %, and 31%, respectively, compared with ILFTC, and 18%, 15%, and 52%, respectively, compared with PILFTC.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"151 ","pages":"Article 103431"},"PeriodicalIF":3.3,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868397","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":"Tube MPC for a two-tank system based on Eigensystem Realization Algorithm","authors":"Mathias Dyvik,&nbsp;Damiano Rotondo","doi":"10.1016/j.jprocont.2025.103434","DOIUrl":"10.1016/j.jprocont.2025.103434","url":null,"abstract":"<div><div>This paper presents the design of a linear, data-driven, tube-based robust model predictive control (MPC) for level control in a coupled nonlinear two-tank system. Two state-space models are identified from step responses using the eigensystem realization algorithm (ERA): one from a high-fidelity nonlinear process simulator and the other using data from the physical plant. The obtained models have states that lack physical meaning, necessitating a state observer to estimate the states from the level sensor measurements. The paper shows that a proportional-integral Kalman filter provides more robust state estimates than a standard Kalman filter and is thus used for controller implementation. The proposed ERA-based tube MPC demonstrated robust performance and constraint satisfaction compared to a conventional MPC in both simulation and experimental settings. However, it violated constraints under certain disturbances within the predefined bounds because of modeling mismatches caused by applying a linear control strategy to a nonlinear system. Addressing these violations by incorporating parametric uncertainty in the disturbance bounds and using more aggressive tuning mitigates the issue but increases conservatism and control effort. These findings offer insights into the tuning of Tube MPC for desired trade-offs in industrial applications.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"151 ","pages":"Article 103434"},"PeriodicalIF":3.3,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868386","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}