Journal of Process Control最新文献

{"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}

{"title":"Data-driven soft constrained model predictive control for sludge bulking in wastewater treatment process","authors":"Hong-Gui Han ,&nbsp;Yan Wang ,&nbsp;Hao-Yuan Sun ,&nbsp;Zheng Liu ,&nbsp;Jun-Fei Qiao","doi":"10.1016/j.jprocont.2025.103445","DOIUrl":"10.1016/j.jprocont.2025.103445","url":null,"abstract":"<div><div>The complex causes of sludge bulking, strict system constraints, and dynamic operating conditions increase the challenges of controlling wastewater treatment process. To address this issue, a data-driven soft constrained model predictive control (DD-SCMPC) strategy is proposed, which can adaptively adjust the control law in response to the identified fault cause. First, an intelligent diagnosis algorithm is utilized to identify the key cause variable according to the relative reconstruction contribution of process variables. Consequently, the priority control order of the controlled variables can be determined based on the correlation between the cause variable and output variables. Second, a soft constrained MPC strategy is designed to regulate the concentrations of dissolved oxygen and nitrate nitrogen in accordance with the predetermined control order, thereby avoid sludge bulking caused by abnormal process variables. The incorporation of soft constraints alleviates the strict constraints on system outputs, enhancing the adaptability of the controller. Third, a predictive control barrier function is designed to obtain an enlarged attractive domain, ensuring the stability of the system under soft constraints. Then, the feasibility and stability analysis provide theoretical support for the application of DD-SCMPC. Finally, the effectiveness of the proposed DD-SCMPC strategy is verified on the benchmark simulation model 1.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"151 ","pages":"Article 103445"},"PeriodicalIF":3.3,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860006","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 fuzzy-bilateral prescribed performance control for nonlinear systems with uncertain time delays and its application","authors":"Qiyu Yang ,&nbsp;Litian Wei ,&nbsp;Ming Li","doi":"10.1016/j.jprocont.2025.103435","DOIUrl":"10.1016/j.jprocont.2025.103435","url":null,"abstract":"<div><div>For a class of nonlinear systems with uncertain time delays, this paper proposes an adaptive fuzzy-bilateral prescribed performance control method. The bilateral prescribed performance control introduces a novel barrier function that provides a more constrained allowable set for system output errors, circumventing potential performance degradation caused by limited performance curve parameter settings. An adaptive fuzzy logic system parameter tuning strategy is designed to approximate unknown nonlinear functions and satisfy the prerequisite conditions of bilateral prescribed performance control. The synergistic integration of these two approaches addresses critical challenges in industrial scenarios, such as temperature control systems where system model parameters are difficult to obtain and control parameters require manual online adjustment in response to environmental variations. Finally, simulation experiments and practical industrial temperature control experiments were conducted, with multiple temperature target groups used to verify heating and cooling control performance. Experimental results demonstrate the effectiveness and superiority of the proposed method.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"150 ","pages":"Article 103435"},"PeriodicalIF":3.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851462","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":"Fault estimation and self-healing control for actuator fault in dissolved oxygen control of wastewater treatment","authors":"Hongyang Zan ,&nbsp;Haozhou Wang ,&nbsp;Xinyu Yu ,&nbsp;Hongguang Pan ,&nbsp;Li Li","doi":"10.1016/j.jprocont.2025.103433","DOIUrl":"10.1016/j.jprocont.2025.103433","url":null,"abstract":"<div><div>Wastewater treatment processes (WWTPs) are inherently complex, characterized by various dynamic operations such as aerobic digestion, which critically depends on maintaining optimal dissolved oxygen (DO) levels. Actuator faults in WWTPs, particularly those affecting oxygen transfer systems, can disrupt this balance, leading to inefficiencies and safety hazards. This paper addresses the issue of fault estimation and self-healing control, specifically in the presence of additive actuator faults affecting the DO regulation. First, a low-order state-space model is introduced as a mechanistic alternative to the Benchmark Simulation Model No. 1 (BSM1) to model the dynamics of WWTPs. Second, the additive actuator fault is incorporated into the system state, and an adaptive proportional-integral observer (APIO) is designed to estimate these faults. Third, a self-healing controller based on sliding-mode control (SMC) is developed to restore the system’s performance and ensure stable DO levels. Finally, the performance of the proposed strategy is evaluated through simulations, which demonstrate its ability to accurately estimate faults and effectively restore system stability in the presence of actuator failures.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"150 ","pages":"Article 103433"},"PeriodicalIF":3.3,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835300","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":"Studying the effect of dynamic operation conditions on green ammonia production synthesis loop","authors":"Raj Patel ,&nbsp;Amin Soleimani Mehr ,&nbsp;Jimena Incer Valverde ,&nbsp;Günter Scheffknecht ,&nbsp;Jörg Maier ,&nbsp;Reihaneh Zohourian","doi":"10.1016/j.jprocont.2025.103436","DOIUrl":"10.1016/j.jprocont.2025.103436","url":null,"abstract":"<div><div>The global challenges meeting hydrogen demands due to limited renewable resources urge the need for low-cost imports. Green ammonia, promising for its existing infrastructure, encounters inflexibility challenges in large-scale production with renewables. This study delves into ammonia synthesis loop flexibility amid renewable intermittencies. Utilizing an Aspen Plus® model of 1223 tonnes per day ammonia production capacity, the transient behavior under varied feed flow was investigated with Aspen Dynamics™ simulations. The findings indicated that effective strategies enabled managing a minimum load of 10 % or lower under stoichiometric conditions, constrained by the electrolysis system's lower load. The study also concluded that the ammonia synthesis unit's 20 %/hr feed flow ramp rate is restricted by the thermal cycling of the reactor catalyst; the consequences of fast ramp-up and ramp-down of the operational parameters such as feed flow or stoichiometric ratio are the primary limits in green ammonia production or dynamic operation of the plant.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"150 ","pages":"Article 103436"},"PeriodicalIF":3.3,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838878","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, robust, interval state estimation for distribution systems based on fixed-point expansion considering uncertainties","authors":"Zhengmei Lu ,&nbsp;Hong Tan ,&nbsp;Mohamed A. Mohamed","doi":"10.1016/j.jprocont.2025.103427","DOIUrl":"10.1016/j.jprocont.2025.103427","url":null,"abstract":"<div><div>Interval state estimation (ISE) is widely used due to its ability to handle uncertainty and the simple parameters that are required. Existing ISE methods have some problems that need improvements, such as conservatism of results, lack of completeness, and limitations in the error range. Therefore, this paper proposes a nonlinear robust ISE method for distribution systems. First, the quadratic Taylor-series expansions of measurement equations are transformed into fixed-point expansions without truncation errors, which reduces errors introduced by measurement conversion and the approximation process. Second, an exponentially weighted least-squares ISE model considering power-flow constraints is proposed based on the fixed-point expansion (FPE), which avoids calculating inverse matrices of the Jacobian matrices containing interval numbers and improves the estimation accuracy. To improve the model’s robustness, an interval weight correction strategy is proposed. Then, the interval Taylor-series method is used to calculate the range of interval functions to reduce the expansion of the interval arithmetic, thereby obtaining narrower intervals for the state variables. Finally, based on an analysis of the 34-bus and the 123-bus systems, it can be seen that the proposed method has good performance for different error ranges and poor measurement ranges.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"150 ","pages":"Article 103427"},"PeriodicalIF":3.3,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835299","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 second-order iterative sliding mode control for cyber–physical systems under prescribed performance and DoS attacks","authors":"Yijie Yang ,&nbsp;Dong Liu ,&nbsp;Xin Wang ,&nbsp;Zhujun Wang","doi":"10.1016/j.jprocont.2025.103422","DOIUrl":"10.1016/j.jprocont.2025.103422","url":null,"abstract":"<div><div>This work investigates the second-order iterative sliding mode control problem of cyber–physical systems under prescribed performance and denial of service (DoS) attacks. An equivalent model of the controlled system is derived utilizing the dynamic linearization methodology, solely relying on process data. Through the novel tangent-type error transformation function, the confined error is equivalently transformed into the unconfined error. On this basis, a new second-order sliding function is devised to ensure that the error converges to the prearranged asymmetric region from the traditional time axis to the iteration axis. Based upon historical iterative data, an attack compensation mechanism is constructed to eliminate the negative impacts of attacks on the sensor. Finally, the effectiveness of the presented approach is validated via two examples.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"150 ","pages":"Article 103422"},"PeriodicalIF":3.3,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815051","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}