Journal of Process Control最新文献

Towards hybrid modeling with mechanistic and real-time data embed iterative co-optimization for industrial processes 面向工业过程机械与实时数据融合的混合建模与迭代协同优化

IF 3.9 2区计算机科学

Journal of Process Control Pub Date : 2025-10-18 DOI: 10.1016/j.jprocont.2025.103567

Mingyu Liang, Yi Zheng, Shaoyuan Li

{"title":"Towards hybrid modeling with mechanistic and real-time data embed iterative co-optimization for industrial processes","authors":"Mingyu Liang, Yi Zheng, Shaoyuan Li","doi":"10.1016/j.jprocont.2025.103567","DOIUrl":"10.1016/j.jprocont.2025.103567","url":null,"abstract":"<div><div>This paper addresses the hybrid modeling challenges arising from incomplete mechanistic models and operational data noise interference in process industries, proposing a two-layer joint iterative optimization framework for updating parameters of hybrid models integrating mechanistic and data-driven models. The framework achieves real-time anomaly elimination through an outlier screening algorithm, while employing a bidirectional feedback algorithm to enable continuous collaboration and mutual constraints between mechanistic and data-driven models during parameter identification and iterative updates, ensuring robust hybrid model predictions. The proposed method resolves hybrid modeling and updating under conditions of mechanistic model information deficiency. Additionally, by incorporating model uncertainty and prior knowledge, it accomplishes a knowledge-incorporated hybrid modeling process, demonstrating significant practical value. Unlike conventional hybrid modeling approaches where mechanistic knowledge merely guides the modeling process, our method achieves dynamic co-evolution between mechanistic and data-driven models. This paper elaborates on three key aspects: (1) using mechanistic models to screen anomalous data; (2) incorporating mechanistic parameter uncertainty and prior knowledge through Bayesian methods to design knowledge-guided parameter updating method; (3) implementation details of the two-layer joint iterative optimization algorithm. Comparative experiments validate the method’s superior performance under multiple operating conditions and anomalies, demonstrating its scientific validity and practical value in dynamic optimization processes.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103567"},"PeriodicalIF":3.9,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324807","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

Boiler operation predictions by integrating thermo-fluid principles within an artificial neural network framework 在人工神经网络框架内集成热流体原理的锅炉运行预测

IF 3.9 2区计算机科学

Journal of Process Control Pub Date : 2025-10-17 DOI: 10.1016/j.jprocont.2025.103568

C. Bisset , R. Coetzer , PVZ. Venter

{"title":"Boiler operation predictions by integrating thermo-fluid principles within an artificial neural network framework","authors":"C. Bisset , R. Coetzer , PVZ. Venter","doi":"10.1016/j.jprocont.2025.103568","DOIUrl":"10.1016/j.jprocont.2025.103568","url":null,"abstract":"<div><div>Optimising boiler operations is challenging due to fluctuating conditions in complex thermo-fluid systems. This study introduces a novel approach to improve efficiency in coal-fired boilers by developing and validating an artificial neural network (ANN) model that provides both statistically accurate and scientifically feasible predictions. Three multi-layer perceptron (MLP) feedforward ANN models were developed, with variable selection supported by principal component analysis (PCA) and hyperparameter optimisation performed using Latin hypercube sampling (LHS). The best ANN achieved test root mean square errors (RMSEs) of 2.11 t/h for steam flow, 2.11 t/h for blowdown, 4.98 °C for superheated steam temperature, 0.69 bar for steam pressure, and 0.86 % for efficiency. The mean absolute percentage error (MAPE) for efficiency remained below 1.25 %, with deviations constrained within ±4.25 %. Statistical and thermodynamic validations were applied, including bootstrap aggregation of prediction variance and mass and energy balance checks. Results showed that 96.76 % of samples achieved water mass balance deviations of less than 0.01 %. Furthermore, 100 % of predictions for efficiency and energy output fell within a 5 % absolute error range. The novelty of this work lies in integrating ANN predictions with thermo-fluid validation. Theoretically, it advances current literature by bridging the gap between statistical accuracy and physical feasibility. Practically, it provides a reliable framework for evaluating efficiency in operational settings and lays the foundation for a machine learning (ML)–aided decision-support framework (DSF) for energy efficiency optimisation in coal-fired boilers.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103568"},"PeriodicalIF":3.9,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324809","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

Canonical correlation analysis-aided design of Kalman filter-based residual generator for monitoring of industrial control systems 典型相关分析辅助设计基于卡尔曼滤波的工业控制系统监测残差发生器

IF 3.9 2区计算机科学

Journal of Process Control Pub Date : 2025-10-14 DOI: 10.1016/j.jprocont.2025.103569

Long Gao , Donghua Zhou , Steven X. Ding

{"title":"Canonical correlation analysis-aided design of Kalman filter-based residual generator for monitoring of industrial control systems","authors":"Long Gao , Donghua Zhou , Steven X. Ding","doi":"10.1016/j.jprocont.2025.103569","DOIUrl":"10.1016/j.jprocont.2025.103569","url":null,"abstract":"<div><div>Kalman filters are widely applied for residual generation thanks to the property that the generated residual is white and of minimum covariance. This enables an optimal monitoring. However, the explicit mathematical model is difficult to achieve in a real industrial automation system, and the effect of the feedback has not been explicitly considered in the existing data-driven design method, which degrades the monitoring performance of a Kalman filter-based monitoring system. To deal with such an issue, this paper proposes a purely data-driven realization of the Kalman filter-based residual generator for process monitoring of industrial control systems with a closed-loop configuration. Firstly, a least-mean-square interpretation of canonical correlation analysis (CCA) is introduced, which is helpful to explore the relationships between inputs and outputs of industrial control systems. Then, a CCA-aided Kalman filter-based residual generator is constructed, which is realized by identifying the Kalman gain matrix and the data-driven stable kernel representation. Different from the existing method, the proposed one achieves superior monitoring performance by considering closed-loop dynamics and the correlation between inputs and noises, which is caused by the feedback control structure of systems. The effectiveness of the proposed method is demonstrated and compared through an experimental three-tank system.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103569"},"PeriodicalIF":3.9,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324810","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

Nonlinear model predictive control with an infinite horizon approximation 具有无限水平逼近的非线性模型预测控制

IF 3.9 2区计算机科学

Journal of Process Control Pub Date : 2025-10-13 DOI: 10.1016/j.jprocont.2025.103565

San Dinh, Yao Tong, Zhenyu Wei, Owen Gerdes, L.T. Biegler

{"title":"Nonlinear model predictive control with an infinite horizon approximation","authors":"San Dinh, Yao Tong, Zhenyu Wei, Owen Gerdes, L.T. Biegler","doi":"10.1016/j.jprocont.2025.103565","DOIUrl":"10.1016/j.jprocont.2025.103565","url":null,"abstract":"<div><div>Current nonlinear model predictive control (NMPC) strategies are formulated as finite predictive horizon nonlinear programs (NLPs), which maintain NMPC stability and recursive feasibility through the construction of terminal cost functions and/or terminal constraints. However, computing these terminal properties may pose formidable challenges with a fixed horizon, particularly in the context of nonlinear dynamic processes. Motivated by these issues, we introduce an alternate moving horizon approach where the final element in the horizon is constructed from an infinite-horizon time transformation. The key feature of this approach lies in solving the proposed NMPC formulation as an extended boundary value problem, using orthogonal collocation on finite elements. Numerical stability is ensured through a dichotomy property for an infinite horizon optimal control problem, which pins down the unstable modes, extending beyond open-loop stable dynamic systems, and leads to both asymptotic and robust stability guarantees. The efficacy of the proposed NMPC formulation is demonstrated on three case studies, which validate the practical application and robustness of the developed approach on real-world problems.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103565"},"PeriodicalIF":3.9,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324811","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

Spatiotemporal integrated control for ballast water heat treatment via the kernel learning and model predictive path integral 基于核学习和模型预测路径积分的压载水热处理时空集成控制

IF 3.9 2区计算机科学

Journal of Process Control Pub Date : 2025-10-13 DOI: 10.1016/j.jprocont.2025.103564

Guoqing Zhang , Yang Xu , Jiqiang Li , Zehua Jia , Weidong Zhang

{"title":"Spatiotemporal integrated control for ballast water heat treatment via the kernel learning and model predictive path integral","authors":"Guoqing Zhang , Yang Xu , Jiqiang Li , Zehua Jia , Weidong Zhang","doi":"10.1016/j.jprocont.2025.103564","DOIUrl":"10.1016/j.jprocont.2025.103564","url":null,"abstract":"<div><div>In this article, a spatiotemporal integrated control scheme for ballast water heat treatment is proposed that utilizes an improved nonlinear predictive control algorithm relying on a kernel-learning-based model to lower the concentration of microorganisms by manipulating the temperature of heated water indirectly. Firstly, multiple heat exchangers treating process is simplified into a plug flow reactor model with the properties of distributed parameter systems (DPSs). Based on the simplified model, the kernel-learning-based model is derived by using kernel principal component analysis (KPCA) and kernel extreme learning machine (KELM) for modeling the spatiotemporal temperature data. Further, the hyperparameters of the KELM involved therein are determined by a numerical optimization approach. The superiority of this design is to accurately explore the nonlinear dynamics and uncertainties of the actual system. Associated with the modeling method, the nonlinear predictive control strategy is designed to control and maintain the heating temperature. The remarkable trait is that a model predictive path integral (MPPI) is introduced to avoid the problem of “sinking into the local optimal solution”, which often emerges searching for the optimal control sequence. Finally, the stability analysis and numerical experiments support the effectiveness of the proposed scheme.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103564"},"PeriodicalIF":3.9,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324808","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

Causal-geometry joint dictionary embedding learning for distributed monitoring and root cause analysis 面向分布式监控和根本原因分析的因果几何联合字典嵌入学习

IF 3.9 2区计算机科学

Journal of Process Control Pub Date : 2025-10-11 DOI: 10.1016/j.jprocont.2025.103566

Xue Xu , Chaomin Luo , Yuanjian Fu

{"title":"Causal-geometry joint dictionary embedding learning for distributed monitoring and root cause analysis","authors":"Xue Xu , Chaomin Luo , Yuanjian Fu","doi":"10.1016/j.jprocont.2025.103566","DOIUrl":"10.1016/j.jprocont.2025.103566","url":null,"abstract":"<div><div>Interactions across process variables are complicated in large-scale industrial processes characterized with multiple operating units, posing significant challenges for fault detection and root cause analysis. In this work, a distributed modeling approach termed causal-geometry joint dictionary embedding learning (CGDE) is proposed to monitor large-scale industrial processes and identify the root cause. An information decomposition based block division algorithm is proposed to divide the entire process into blocks that account for unique, redundant, and synergistic information among variables. Meanwhile, a geometry similarity matrix derived by the minimum spanning tree is constructed to exploit the underlying structure of data. Furthermore, a causal consistency matrix is developed to characterize the causality among variables such that the intrinsic and stable information of industrial processes can be effectively captured. The CGDE approach provides an in-depth and faithful process analysis with consideration of causalities and geometry similarity of data, enhancing the distributed monitoring and root cause analysis performance. The effectiveness of CGDE is illustrated through a simulated platform and a real fluid catalytic cracking application.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103566"},"PeriodicalIF":3.9,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268431","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

Adaptive error feedback regulation problem of a first-order hyperbolic PDE system with unknown exosystem 未知外系的一阶双曲PDE系统的自适应误差反馈调节问题

IF 3.9 2区计算机科学

Journal of Process Control Pub Date : 2025-10-10 DOI: 10.1016/j.jprocont.2025.103562

Xin Wang, Feng-Fei Jin

{"title":"Adaptive error feedback regulation problem of a first-order hyperbolic PDE system with unknown exosystem","authors":"Xin Wang, Feng-Fei Jin","doi":"10.1016/j.jprocont.2025.103562","DOIUrl":"10.1016/j.jprocont.2025.103562","url":null,"abstract":"<div><div>This paper studies the output regulation problem for a first-order hyperbolic PDE system with disturbances generated by an unknown finite-dimensional exosystem. The main challenges arise from unbounded control and observation operators, as well as non-collocated input–output configuration. We first introduce a coordinate transformation that simplifies the system dynamics. Next, based on the transformed system, we design an observer and apply an adaptive internal model principle to estimate the unknown harmonic frequencies of the exosystem. We present a controller that achieves exponentially stable output regulation for the resulting closed-loop system. Finally, the effectiveness of the controller is demonstrated through numerical simulations which demonstrate effective parameter tracking, <span><math><mrow><mi>g</mi><mrow><mo>(</mo><mn>0</mn><mo>,</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span> (the regulated output) achieves accurate tracking of <span><math><mrow><msub><mrow><mi>Φ</mi></mrow><mrow><mi>r</mi><mi>e</mi><mi>f</mi></mrow></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span> (the reference signal), and the solution remain uniformly bounded of the <span><math><mi>g</mi></math></span>-part in closed-loop system.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103562"},"PeriodicalIF":3.9,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267022","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

Fault detection and isolation for a class of nonlinear systems based on a bundle of observers and zonotope analysis 一类基于观测器束和共格分析的非线性系统故障检测与隔离

IF 3.9 2区计算机科学

Journal of Process Control Pub Date : 2025-10-09 DOI: 10.1016/j.jprocont.2025.103561

Chi Xu , Zhenhua Wang , Nacim Meslem , Tarek Raïssi , Yi Shen

引用次数: 0

Optimal inventory control for bottleneck isolation in general processes 一般工艺中瓶颈隔离的最优库存控制

IF 3.9 2区计算机科学

Journal of Process Control Pub Date : 2025-10-07 DOI: 10.1016/j.jprocont.2025.103557

Archana Kumaraswamy, Evren Mert Turan, Johannes Jäschke

引用次数: 0

Real-time identification of most critical alarms for alarm flood reduction 实时识别最关键的报警，减少报警洪水

IF 3.9 2区计算机科学

Journal of Process Control Pub Date : 2025-10-03 DOI: 10.1016/j.jprocont.2025.103563

Md Habibur Rahaman, Haniyeh Seyed Alinezhad, Tongwen Chen

{"title":"Real-time identification of most critical alarms for alarm flood reduction","authors":"Md Habibur Rahaman, Haniyeh Seyed Alinezhad, Tongwen Chen","doi":"10.1016/j.jprocont.2025.103563","DOIUrl":"10.1016/j.jprocont.2025.103563","url":null,"abstract":"<div><div>In complex processes, the activation of a single alarm can trigger a cascade of consequences that affect multiple interconnected components. This can lead to a rapid increase in the number of active alarms. This sudden surge in alarms is often referred to as an alarm flood. Alarm floods are a common source of operational burden for operators, overwhelming them with a high volume of alarm notifications. If critical alarms are not promptly and accurately identified, decision-making processes can be undermined. This paper addresses these challenges by introducing a novel approach for identifying and prioritizing critical alarms from each alarm flood. The contributions of this work are twofold: First, hidden Markov models (HMMs) are employed to construct a likelihood matrix that uncovers relationships among alarm variables and identifies the most critical alarms through a directed acyclic graph (DAG). Second, expectation-maximization (EM) algorithm is applied to update the likelihood matrix dynamically and generate time-evolving plots for real-time identification of critical alarms. Case studies are conducted using a vinyl acetate monomer simulator to demonstrate the effectiveness of the proposed approach. The results highlight accurate identification and prioritization of critical alarms, enabling operators to focus on the most important process abnormalities.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103563"},"PeriodicalIF":3.9,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221412","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