Journal of Process Control最新文献

{"title":"Novel observer-based fault-tolerant tracking control of input-constrained polymerization reactor with statistical analysis","authors":"Zahra Ahangari Sisi ,&nbsp;Mehdi Mirzaei ,&nbsp;Sadra Rafatnia ,&nbsp;Somayeh Jamshidi ,&nbsp;Maryam Farbodi","doi":"10.1016/j.jprocont.2025.103541","DOIUrl":"10.1016/j.jprocont.2025.103541","url":null,"abstract":"<div><div>The polymerization reaction within a continuous stirred tank reactor is modeled as a multivariable, nonlinear control process with input constraints. This study proposes a novel optimization-based approach for fault diagnosis and compensation, despite the uncertainties and disturbances present in the dynamic model of the polymerization reactor. This approach facilitates the design of a reliable model-based controller through the estimation of system perturbations. The proposed strategy mitigates external disturbances, time-varying uncertainties, and faults by incorporating complementary terms, calculated in real-time from output measurements, into the initial process model. To ensure robust performance of the fault detection mechanism, the threshold bounds for external disturbances and other uncertainties are determined stochastically using the Monte Carlo simulation approach. A continuous predictive controller is designed in closed form based on the updated reactor model, accounting for the presence of control input limitations. The constrained controller is formulated by solving an optimization problem using the Karush–Kuhn–Tucker (KKT) conditions. The boundedness of the tracking errors is established under the constrained multivariable controller. The results demonstrate that the proposed method exhibits high sensitivity, accuracy, and robustness in fault detection and isolation for a nonlinear uncertain reactor. Simulations confirm the superior performance of the proposed observer-based fault-tolerant control system over existing passive and active actuator fault-tolerant control methods.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103541"},"PeriodicalIF":3.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027379","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":"Observer based dual-layer sliding mode fault-tolerant control for dissolved oxygen concentration in wastewater treatment process","authors":"Hongyan Yang,&nbsp;Qi Zou,&nbsp;Honggui Han","doi":"10.1016/j.jprocont.2025.103538","DOIUrl":"10.1016/j.jprocont.2025.103538","url":null,"abstract":"<div><div>Fault-tolerant control (FTC) of dissolved oxygen concentration is the core technology to ensure the robustness of wastewater treatment process (WWTP). However, the dynamic characteristics of microbial community are difficult to be modeled accurately, and external disturbances such as fluctuations in influent water quality and equipment failures further increase the control difficulty. Therefore, how to effectively compensate for the unmodeled dynamics and improve the system robustness is still a key problem to be solved in the field of WWTP control. In order to address this problem, this paper proposes an FTC method for dissolved oxygen concentration that integrates a dual sliding mode observation mechanism and an intelligent optimization strategy. Firstly, a state observer with an adaptive compensation mechanism is constructed based on the sliding mode control (SMC) method to realize the simultaneous estimation of dissolved oxygen concentration and unmodeled dynamics. Secondly, an adaptive robust fault-tolerant controller is designed by combining the Lyapunov stability theory. Then, a double sliding mode surface containing observation error and control error is established. Thirdly, a differential evolutionary algorithm is introduced to perform a global optimization of the robust gain parameters, which transforms the complex robustness problem into an optimal gain solving problem. Simulation experiments are conducted to compare the fault-tolerant control effect of different control methods with the proposed method, and the results verify the superiority of the method proposed in this paper.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103538"},"PeriodicalIF":3.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021014","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":"Operational zone-specific univariate alarm design for incipient faults","authors":"Mohsen Asaadi ,&nbsp;Fan Yang ,&nbsp;Weichi Wu","doi":"10.1016/j.jprocont.2025.103536","DOIUrl":"10.1016/j.jprocont.2025.103536","url":null,"abstract":"<div><div>Alarm systems are essential components of industrial process monitoring, supporting both safety and operational efficiency by detecting deviations from normal conditions. Traditional alarm design methods often assume stationary, which limits their ability to reflect the evolving nature of incipient faults. These faults develop gradually and, if not properly addressed, can lead to critical failures. Timely and accurate detection is therefore vital to minimize false alarms, reduce missed detections, and improve response effectiveness. This study proposes a time-variant statistical modeling framework to characterize the behavior of process variables affected by incipient faults. A new alarm system design methodology is introduced, guided by three key performance indices: Missed Alarm Rate (MAR), False Alarm Rate (FAR), and Average Alarm Delay (AAD). The methodology uses the Narrowest Over Threshold change-point detection technique to segment the process into distinct operational zones, including the Normal Operating Zone (NOZ), Rising Zone (RZ), Fault Zone (FZ), and Return to Normal (RTN). By employing a piecewise time-variant model, the alarm system’s performance is assessed in a manner that captures local trends and transitions. The resulting indices are dynamic, offering a more detailed projection of the process variable’s behavior over time. In particular, the AAD metric reflects realistic delay patterns and avoids the misleading interpretations often associated with stationary models. The proposed method is validated through Monte Carlo simulations and demonstrated using the Tennessee Eastman Process benchmark. Results show that the time-variant model provides a more accurate and interpretable representation of process dynamics and alarm behavior than traditional approaches.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103536"},"PeriodicalIF":3.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145007709","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":"2025-11-01","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":"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":"2025-11-01","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":"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":"2025-11-01","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":"Deep learning-based model predictive control with exponential weighting strategy and its application in energy management systems","authors":"Dan Cui ,&nbsp;Yanfang Mo ,&nbsp;Xiaofeng Yuan ,&nbsp;Lingjian Ye ,&nbsp;Kai Wang ,&nbsp;Feifan Shen ,&nbsp;Yalin Wang ,&nbsp;Chunhua Yang ,&nbsp;Weihua Gui","doi":"10.1016/j.jprocont.2025.103542","DOIUrl":"10.1016/j.jprocont.2025.103542","url":null,"abstract":"<div><div>Building energy management plays an important role in improving the overall system efficiency and reducing energy consumption. To achieve this goal, it is significant and challenging for the optimization of energy consumption and the utilization of renewable energy sources. This work presents a deep learning-based model predictive control with exponential weighting (DLEMPC) strategy to control and optimize Energy Management Systems (EMS). First, an exponential weighting technique with decreasing characteristic is introduced to the cost function over the timeslots in the receding horizon of the MPC to improve the control performance of the system, which aims to obtain the control actions by paying more importance on recent timeslots in the finite time-horizon. Second, a controller based on the deep belief network (DBN) model is proposed to reduce computational complexity of the rolling horizon optimization in practical applications. The deep learning controller is obtained by training it with a large number of input and output data pairs that are generated from a well-defined MPC designed with the new cost function. Finally, the DLEMPC strategy is used to control and optimize an EMS, connected to a grid, battery, HVAC, and solar panel. The results demonstrate that DLEMPC strategy can significantly improve the energy efficiency of buildings and reduce energy consumption compared to the traditional MPC formula.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103542"},"PeriodicalIF":3.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097508","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":"Advances in modeling and control of nonlinear distributed parameter systems and their applications: A review","authors":"Bowen Xu ,&nbsp;Weiqi Yang ,&nbsp;Xinjiang Lu ,&nbsp;Yunxu Bai ,&nbsp;Yajun Wang","doi":"10.1016/j.jprocont.2025.103549","DOIUrl":"10.1016/j.jprocont.2025.103549","url":null,"abstract":"<div><div>Numerous processes in various fields including engineering, physics, and chemistry, etc., belong to distributed parameter systems (DPSs). These systems are strongly spatiotemporal coupled, possessing complex time-varying dynamics and infinite-dimensional spatial distribution characteristics. Additionally, there are unknown initial/ boundary conditions and parameter variation during the interaction of information or energy exchange, especially in complex application scenarios (i.e., large operation range, large spatial region, etc.). These factors make the modeling, prediction and control of spatiotemporal dynamics extremely difficult and challenging. With the enrichment of computational resources and data-driven/ intelligent methods, many new frameworks and strategies are designed and applied for nonlinear DPSs, which promotes the research diversity and maturity of DPS theory. Meanwhile, the development also gives rise to new problems. From the perspective of review, this paper starts from the practical modeling and control problems in combination with several application cases of nonlinear DPSs, and summarizes the research and application progress, including traditional methods, data-driven methods, intelligent modeling methods etc., and looks forward to the future development trends, providing guidance for related research and practical problem-solving of nonlinear DPSs.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103549"},"PeriodicalIF":3.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097511","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":"Research on control methods for gas-liquid separators based on UKF-LSTM hybrid observation and sliding mode control","authors":"Chuan Wang ,&nbsp;Haojie Liao ,&nbsp;Kui Xie ,&nbsp;Chao Yu","doi":"10.1016/j.jprocont.2025.103573","DOIUrl":"10.1016/j.jprocont.2025.103573","url":null,"abstract":"<div><div>This study proposes a robust control framework that integrates sliding mode control (SMC) with a novel hybrid observer (UKF-LSTM in series) to stabilize separator level and pressure. The stability of the control system is ensured by the Lyapunov method. A significant innovation is a hybrid observer that combines an Unscented Kalman Filter (UKF) and a Long Short-Term Memory (LSTM) network in series to accurately estimate the unmeasurable multiphase inflow. In OLGA plug flow simulations, the framework reduced flow estimation Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 73.9 % and 64.7 % over the baseline. The Control tests showed Integral of Squared Error (ISE), Integral of Absolute Error (IAE), and Integral of Time-weighted Absolute Error (ITAE) were 49.8 %, 24.8 %, and 18.0 %, with convergence accelerated by at least 250 s. Results demonstrate that the method achieves a practical balance between accuracy, robustness, and computational efficiency, making it suitable for real-time industrial separator control under variable conditions.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"155 ","pages":"Article 103573"},"PeriodicalIF":3.9,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362338","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":"2025-11-01","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}