Journal of Process Control最新文献

{"title":"Causal-geometry joint dictionary embedding learning for distributed monitoring and root cause analysis","authors":"Xue Xu ,&nbsp;Chaomin Luo ,&nbsp;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}

{"title":"Adaptive error feedback regulation problem of a first-order hyperbolic PDE system with unknown exosystem","authors":"Xin Wang,&nbsp;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}

{"title":"Fault detection and isolation for a class of nonlinear systems based on a bundle of observers and zonotope analysis","authors":"Chi Xu ,&nbsp;Zhenhua Wang ,&nbsp;Nacim Meslem ,&nbsp;Tarek Raïssi ,&nbsp;Yi Shen","doi":"10.1016/j.jprocont.2025.103561","DOIUrl":"10.1016/j.jprocont.2025.103561","url":null,"abstract":"<div><div>This paper introduces a novel fault detection and isolation (FDI) approach for nonlinear systems subject to unknown but bounded disturbances. The proposed approach combines a bundle of fault detection observers (FDOs), tuned by a peak-to-peak performance technique, with an offline reachability method to generate reliable actuator fault detection and isolation thresholds. Moreover, a sliding-window algorithm, based on zonotopic computation, is designed to be able to provide dynamical fault detection thresholds. This allows one to reduce the conservatism and, by the way, enhance the efficiency of the proposed approach. A quadruple-tank system is considered as a case study, where the theoretical findings of this work are supported by simulation results. In addition, on this example, the performance of the proposed method is compared to that of another method selected from the literature.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103561"},"PeriodicalIF":3.9,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267021","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":"Optimal inventory control for bottleneck isolation in general processes","authors":"Archana Kumaraswamy,&nbsp;Evren Mert Turan,&nbsp;Johannes Jäschke","doi":"10.1016/j.jprocont.2025.103557","DOIUrl":"10.1016/j.jprocont.2025.103557","url":null,"abstract":"<div><div>Optimal inventory control seeks to isolate the economic effect of bottlenecks and maximise the throughput of processes. This is challenging in complex topologies with disturbances causing shifting bottlenecks. Decentralised and model predictive control schemes have been proposed for bottleneck isolation of sequential processes. Although decentralised control schemes work well for sequential processes, they are difficult to apply to more complex topologies such as parallel arrangement of units, flow splits, mergers, and recycles that are common in the industry. In contrast, such multi-input multi-output systems can be naturally handled with model predictive control schemes. This work extends a preliminary model predictive control scheme in the literature to achieve bottleneck isolation in general process topologies. In particular, a seriatim amongst inventories and system outflows is created using weights in the objective function. Our approach is simple to implement and is shown to optimally isolate bottlenecks on a wide range of case studies and topologies.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103557"},"PeriodicalIF":3.9,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267024","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":"Real-time identification of most critical alarms for alarm flood reduction","authors":"Md Habibur Rahaman,&nbsp;Haniyeh Seyed Alinezhad,&nbsp;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}

{"title":"Enhancing hierarchical learning of real-time optimization and model predictive control for operational performance","authors":"Rui Ren,&nbsp;Shaoyuan Li","doi":"10.1016/j.jprocont.2025.103559","DOIUrl":"10.1016/j.jprocont.2025.103559","url":null,"abstract":"<div><div>In process control, the integration of Real-Time Optimization (RTO) and Model Predictive Control (MPC) enables the system to achieve optimal control over both long-term and short-term horizons, thereby enhancing operational efficiency and economic performance. However, this integration still faces several challenges. In the two-layer structure, the upper layer RTO involves solving nonlinear programming problems with significant computational complexity, making it difficult to obtain feasible solutions in real-time within the limited optimization horizon. Simultaneously, the lower layer MPC must solve rolling optimization problems within a constrained time frame, placing higher demands on real-time performance. Additionally, uncertainties in the system affect both optimization and control performance. To address these issues, this paper proposes a noval hierarchical learning approach for RTO and MPC controller using reinforcement learning. This method learns the optimal strategies for RTO and MPC across different time scales, effectively mitigating the high computational costs associated with online computations. Through reward design and experience replay during the hierarchical learning process, efficient training of the upper and lower layer strategies is achieved. Offline training under various uncertainty scenarios, combined with online learning, effectively reduces performance degradation due to model uncertainties. The proposed approach demonstrates excellent performance in two representative chemical engineering case studies.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103559"},"PeriodicalIF":3.9,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221413","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 multi-model fault-tolerant control method for concurrent faults in wastewater treatment processes based on semi-supervised learning and physical constraints","authors":"Huan Luo,&nbsp;Ying Tian","doi":"10.1016/j.jprocont.2025.103560","DOIUrl":"10.1016/j.jprocont.2025.103560","url":null,"abstract":"<div><div>Wastewater treatment processes (WWTP) is one of the most essential means to achieve water resource protection and sustainable utilization, with dissolved oxygen and nitrate serving as main factors limiting effluent quality through their direct involvement in carbon consumption, nitrification, and denitrification processes. Existing fault-tolerant control strategies primarily focus on single sensor anomalies, while practical operations frequently encounter concurrent faults across multiple measurement channels. Moreover, the scarcity of labeled operational data in industrial settings poses significant challenges for developing reliable fault-tolerant control systems. This paper presents a passive fault-tolerant control approach using an innovative semi-supervised deep learning framework to address simultaneous failures in critical dissolved oxygen and nitrate sensors. The proposed methodology features four key innovations: (1) A novel SAE-MNN architecture that integrates stacked autoencoders with multi-output neural networks for simultaneous multi-parameter prediction through hierarchical feature extraction. (2) A confidence-based pseudo-labeling semi-supervised co-training mechanism that effectively leverages limited labeled data and abundant unlabeled operational data under data scarcity conditions. (3) Physics-constrained learning that enforces biochemical principles and mass conservation laws to ensure physically plausible predictions. (4) A multi-sensor passive fault-tolerant control strategy that handles simultaneous failures across multiple critical measurement channels without hardware redundancy or controller reconfiguration. This integrated framework enables robust operation during concurrent sensor failures, where predicted values seamlessly replace multiple faulty sensor measurements while maintaining stable control performance. The effectiveness is validated using the Benchmark Simulation Model No. 1 (BSM1), demonstrating superior system performance during multi-sensor fault scenarios compared to conventional methods, thereby enhancing the reliability and resilience of wastewater treatment systems.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103560"},"PeriodicalIF":3.9,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221455","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":"Melt viscosity control in polymer extrusion using nonlinear model predictive control with neural state space modelling and soft sensor feedback","authors":"Yasith S. Perera ,&nbsp;Jie Li ,&nbsp;Chamil Abeykoon","doi":"10.1016/j.jprocont.2025.103556","DOIUrl":"10.1016/j.jprocont.2025.103556","url":null,"abstract":"<div><div>Melt viscosity is a key quality indicator in polymer extrusion processes, as it directly influences the mechanical properties, dimensional stability, and surface finish of the final product. However, real-time melt viscosity monitoring and control remain a major challenge in industrial polymer extrusion, due to the limitations of physical viscosity monitoring techniques, such as disturbances to the melt flow, reduced throughputs, and measurement delays. To address this issue, this study proposes a nonlinear model predictive control framework that enables direct, real-time control of melt viscosity using non-invasive feedback from a deep neural network-based soft sensor. A neural state-space model is trained on real experimental data to learn the underlying process dynamics and serves as the internal model of the controller. The soft sensor provides melt viscosity estimates based on readily available process variables (i.e., screw speed and barrel temperatures). These estimates are used by an extended Kalman filter with state augmentation to correct the internal state predictions. The proposed control system was rigorously evaluated via simulation across a variety of setpoint changes and disturbance scenarios. Results show that the controller maintains the melt viscosity within <span><math><mo>±</mo></math></span>2 % of the setpoint, irrespective of the initial conditions used, with settling times below 20 s. Under step and ramp disturbances applied to the output variable, screw speed, and barrel temperatures, the controller exhibited strong disturbance rejection capabilities. Notably, under step disturbances of <span><math><mo>±</mo></math></span> 100 Pa⋅s acting on the melt viscosity output, the controller quickly restored the viscosity to the setpoint with settling times under 18 s. The real-time closed-loop melt viscosity control framework proposed in this study should be invaluable for advancing process monitoring and control of polymer extrusion processes.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103556"},"PeriodicalIF":3.9,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221456","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":"Real-time freeze point prediction using multirate measurements in the blending process","authors":"Khizer Mohamed ,&nbsp;Om Prakash ,&nbsp;Junyao Xie ,&nbsp;Yanjun Ma ,&nbsp;Haitao Zhang ,&nbsp;Biao Huang","doi":"10.1016/j.jprocont.2025.103550","DOIUrl":"10.1016/j.jprocont.2025.103550","url":null,"abstract":"<div><div>In blending processes, real-time monitoring of product properties is crucial for maintaining quality and optimizing operational efficiency. However, properties such as the freeze point are typically measured using slow and expensive laboratory tests. To enable real-time monitoring, analyzers are developed based on these laboratory measurements. Additionally, there are certain compounds whose freeze point is less than <span><math><mrow><mo>−</mo><mn>70</mn><msup><mrow><mspace></mspace></mrow><mrow><mo>∘</mo></mrow></msup><mtext>C</mtext></mrow></math></span>, which are beyond the detection limits of conventional laboratory techniques. This paper introduces a framework that combines the expectation–maximization algorithm with particle-filtering to estimate the freeze point of a compound used in the fuel-blending process, where conventional laboratory methods struggle to provide measurements. The method integrates multirate data, by combining high-frequency sensor data with low-frequency laboratory measurements, to estimate the freeze point. The soft sensor parameters are then identified using the estimated freeze point and directly measured input features such as the true boiling point. The proposed model allows estimation of the freeze point, particularly for components whose properties are not readily measurable using standard laboratory techniques. The proposed approach is compared against two other approaches: (1) a estimation using only high-frequency sensor data and (2) a estimation using only slow laboratory measurements. The soft sensor developed using the proposed framework reduces dependence on offline testing, providing a cost-effective and operationally viable alternative, while validation with industrial data confirms its applicability and effectiveness in real time, achieving an <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> value of 0.4074 that demonstrates reasonable predictive performance under industrial conditions.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103550"},"PeriodicalIF":3.9,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145119249","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":"Prior model identification for stochastic optimal control of continuous aqueous two-phase flotation","authors":"Katrin Baumgärtner ,&nbsp;Kim Carina Lohfink ,&nbsp;Hermann Nirschl ,&nbsp;Moritz Diehl","doi":"10.1016/j.jprocont.2025.103524","DOIUrl":"10.1016/j.jprocont.2025.103524","url":null,"abstract":"<div><div>In chemical process control, where an accurate model of the system dynamics is often not available, advanced control strategies such as stochastic optimal control promise superior control performance as opposed to nominal approaches neglecting the – often significant – uncertainty associated with the model predictions. A crucial prerequisite for stochastic optimal control is a suitable description of the uncertainty associated with the available model as well as a computational description of how this uncertainty evolves as more measurements become available. In this work, we exemplify how a stochastic model might be identified from experimental data and illustrate how non-stochastic models fail to describe the available data in the presence of high inter-experimental variation within the dataset. To this end, model identification from experimental data of the continuous aqueous two-phase flotation serves as a case study. In a second step, we showcase the performance of an optimization-based control strategy which is based on the identified stochastic model in closed-loop experiments.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103524"},"PeriodicalIF":3.9,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145119250","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}