Journal of Process Control最新文献

{"title":"Optimization-based network partitioning for distributed and decentralized control","authors":"Alireza Arastou ,&nbsp;Ye Wang ,&nbsp;Erik Weyer","doi":"10.1016/j.jprocont.2024.103357","DOIUrl":"10.1016/j.jprocont.2024.103357","url":null,"abstract":"<div><div>Control of large-scale networks can be challenging due to difficulties in implementation of high-order control systems, data collection, and actuation. Distributed and decentralized control systems are therefore commonly used. This paper proposes an optimization-based partitioning approach for use in decentralized and distributed control. It factors in both computational and communication costs, while also taking into consideration the controllability of the subsystems. An efficient algorithm for solving the optimization problem is also provided. The proposed approach is demonstrated on case studies from water distribution systems.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"146 ","pages":"Article 103357"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nonstationary incipient fault detection based on hybrid supervised trend-period variational autoencoder and its application in thermal power generation","authors":"Zhangming Lan ,&nbsp;Yun Wang ,&nbsp;Yuchen He ,&nbsp;Lijuan Qian","doi":"10.1016/j.jprocont.2024.103371","DOIUrl":"10.1016/j.jprocont.2024.103371","url":null,"abstract":"<div><div>Incipient fault detection has been considered as one of the most efficient approaches to reduce the risks of systematic failures. However, incipient fault signals are often obscured by nonstationary characteristics, such as trend features and periodic features. In this paper, a hybrid-supervised trend-period variational autoencoder (HSTPVAE) is proposed to achieve fault detection for incipient faults in nonstationary processes. The features of trend, period and residual are extracted from a novel trend-period variational autoencoder (TPVAE). Then, these features are optimized by a hybrid supervised strategy, which includes fault trend semi-supervised module and trend-period self-supervised module. The former enhances the distinctiveness between normal and fault trend features, while the latter prevents the overfitting issues. Finally, the effectiveness of the HSTPVAE is demonstrated on a numerical simulation process and real boiler combustion process of thermal power generation. The comparison with state-of-the-art (SOTA) methods proves that the proposed HSTPVAE method can fully utilize the trend and period features of nonstationary process and outperform other comparison methods in incipient fault detection.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"146 ","pages":"Article 103371"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174877","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":"On dual-loop model-free adaptive iterative learning control and its application","authors":"Nasreldin Ibrahim ,&nbsp;Na Dong ,&nbsp;Modawy Adam Ali Abdalla","doi":"10.1016/j.jprocont.2025.103376","DOIUrl":"10.1016/j.jprocont.2025.103376","url":null,"abstract":"<div><div>This study investigates a dual input and dual output Model-Free Adaptive Iterative Learning Control (MFAILC)-based energy-saving control of the refrigeration system to maintain a minimum stable superheat and a constant evaporation temperature. Traditional PID control for superheat control is often unstable due to the complex and high-order nature of the refrigeration systems. Additionally, the presence of nonlinearities and time variations complicates the design of smart controllers. To get around these problems, an advanced control technique MFAILC algorithm was first designed for single input and single output. Subsequently, the proposed MFAILC algorithm was extended to dual-input and dual-output energy-saving control of refrigeration systems. To test the performance of this innovative methodology, a qualitative and quantitative comparisons, as well as a statistical ANOVA test, have been conducted between the proposed method and the Model-Free Adaptive Control (MFAC) algorithm to evaluate the performance. Step signals have been utilized as the given signals for comprehensive performance testing. As a result, the proposed approach demonstrates higher tracking stability and fast response speed, with an average tracking accuracy of 98.10% for superheat and 91.72% for evaporator temperature, among the simulation experiments.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"147 ","pages":"Article 103376"},"PeriodicalIF":3.3,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182259","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":"Model predictive control of feed rate for stabilizing and enhancing biogas production in anaerobic digestion under meteorological fluctuations","authors":"Ali Moradvandi ,&nbsp;Sjoerd Heegstra ,&nbsp;Pamela Ceron-Chafla ,&nbsp;Bart De Schutter ,&nbsp;Edo Abraham ,&nbsp;Ralph E.F. Lindeboom","doi":"10.1016/j.jprocont.2025.103375","DOIUrl":"10.1016/j.jprocont.2025.103375","url":null,"abstract":"<div><div>Temperature plays a critical role in performance and stability of anaerobic digestion processes, subject to frequent meteorological fluctuations. However, state-of-the-art modeling and process control approaches for anaerobic digestion often do not consider the temporal dynamics of the temperature, which can influence microbial communities, kinetics, and chemical equilibrium, and consequently, biogas production efficiency. Therefore, to account for anaerobic digesters operating under fluctuating meteorological conditions, the Anaerobic Digestion Model no. 1 (ADM1) is mechanistically extended in this paper to incorporate temporal changes into temperature-dependent parameters by defining inhibition functions for microbial activities using the cardinal temperature model, and accounting for the lag in microbial adaptation to temperature fluctuations using a time-lag adaptation function. Thereafter, given that temperature fluctuations are a significant disturbance, a control framework based on Model Predictive Control (MPC) is developed to regulate the feeding flow rate and to ensure stable production rates despite temperature disturbances without relying on direct temperature control. An adaptive MPC approach is formulated based on a linear input–output model, where the parameters of the linear model are updated online to capture the nonlinear dynamics of the process and frequent changes in the dynamics accurately. In addition, a fuzzy logic system is employed to assign a reference trajectory for the production rate based on the temperature and its rate of change. Integrating this fuzzy logic system with the MPC controller enhances the production rate on warm days and avoids the operational failure in production on cold days. Additionally, to enhance biogas production rates, the feasibility of utilizing a portion of the produced biogas for external heating purposes is also investigated. It is demonstrated that by utilizing the proposed MPC approach, the additional amount of feed for the digester to produce methane required for a self-consumption biogas-fueled heating system can be calculated according to the meteorological variations. This enhances the process performance and stability. Finally, a thermally optimized dome digester semi-buried in the ground, operating under climate conditions of The Netherlands is considered as a case study to validate the extended model in agreement with biological and physicochemical behaviors of real-world applications, and to demonstrate the effectiveness of the proposed control system in handling temperature changes and enhancing performance.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"147 ","pages":"Article 103375"},"PeriodicalIF":3.3,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A robust fault diagnosis model with interval distribution analysis for industrial processes with data uncertainties","authors":"Shumei Zhang ,&nbsp;Weifeng Mao ,&nbsp;Feng Dong ,&nbsp;Sijia Wang","doi":"10.1016/j.jprocont.2025.103377","DOIUrl":"10.1016/j.jprocont.2025.103377","url":null,"abstract":"<div><div>In industrial processes, sensor aging and harsh field environments often introduce uncertainties into process data. These uncertainties obscure fault symptoms and undermine the precision of fault diagnosis. To address this challenge, this paper proposes a robust fault diagnosis model with interval distribution analysis for abnormal recognition under data uncertainties in complex industrial settings. Specifically, this research first transforms uncertain data collected from complex industrial sites into interval-valued data, which can globally capture the internal structural characteristics of data objects and effectively represent the uncertainty inherent in the single-valued data. Subsequently, a complete information principal component analysis (CIPCA)-based dimensionality reduction model is constructed to exploit the distribution information within the interval and extract interval fault features. Finally, an interval radial basis function neural network (IRBFNN) is developed to handle the interval upper and lower bound matrices through subtractive clustering algorithm, facilitating fault prediction and diagnosis in industrial processes contaminated by uncertainties. The key to discriminate the proposed method from many well-established fault diagnosis methods is its ability to cluster the interval fault features from uncertain data with embedded interval distribution analysis. The superiority of the proposed fault diagnosis model is validated by the Tennessee Eastman process (TEP).</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"147 ","pages":"Article 103377"},"PeriodicalIF":3.3,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182260","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":"Gaussian mixture TimeVAE for industrial soft sensing with deep time series decomposition and generation","authors":"Bingbing Shen ,&nbsp;Xiaoyu Jiang ,&nbsp;Le Yao ,&nbsp;Jiusun Zeng","doi":"10.1016/j.jprocont.2024.103355","DOIUrl":"10.1016/j.jprocont.2024.103355","url":null,"abstract":"<div><div>Most industrial process data is time series data and contains multi-mode characteristics, which poses difficulties and challenges in the establishment of soft sensing models. To address these issues, this paper proposes a Gaussian mixture based time series decomposition model. This model innovatively introduces Gaussian mixture distributions into the latent space and utilizes a time series decomposition module in the decoder to decompose complex distributions. On one hand, the latent variables of the Gaussian mixture distribution can better extract complex features from time series inputs. On the other hand, the time series decomposition module can break down and extract disentangled features from the time series perspective. Furthermore, to tackle the problem of poor fitting in peak or extreme data due to information imbalance, it generates virtual time series data. The generated virtual time series can complement the information of poorly fitted data, supplementing the original data, and contribute to a better soft sensing model. Finally, to validate the effectiveness of the proposed methods, the soft sensors based on the proposed model are applied to two real industrial cases. The experimental results show that the proposed models have superior predictive performance compared to other state-of-the-art methods.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"147 ","pages":"Article 103355"},"PeriodicalIF":3.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182257","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":"Semi-supervised concept drift detection and adaptation based on conformal martingale framework","authors":"Yu Zhang,&nbsp;Ping Zhou,&nbsp;Ruiyao Zhang,&nbsp;Shaowen Lu,&nbsp;Tianyou Chai","doi":"10.1016/j.jprocont.2025.103374","DOIUrl":"10.1016/j.jprocont.2025.103374","url":null,"abstract":"<div><div>In the realm of industrial applications for machine learning, multiple challenges are frequently encountered, such as concept drift (CD) and the prohibitive costs associated with data labeling. CD refers to the scenario where the underlying data distribution of the model shifts over time, potentially deteriorating model performance. Addressing these challenges, this paper proposes an innovative semi-supervised CD detection method, specifically designed to tackle both CD and the high costs of data labeling in regression tasks. Initially, considering the high expense of acquiring labeled data in industrial application scenarios, a semi-supervised learning strategy based on self-training is utilized. In this strategy, prediction intervals generated by Conformal Prediction (CP) are used to select high-reliability pseudo-labels. Furthermore, to effectively address CD in real-world industrial settings, the Conformal Martingale (CM) is employed for real-time detection. This framework detects changes by identifying increases in martingale values when CD occurs. Upon detection, the model is promptly retrained using the most recent data following the drift. Finally, the proposed method is validated through experiments conducted on three datasets: the UCI dataset, the alumina evaporation process dataset, and the blast furnace ironmaking dataset. Experimental results demonstrate that the proposed semi-supervised method significantly enhances the performance of the original training model. The detection method accurately identifies CD and notably reduces test errors through model retraining, thereby improving the effectiveness of the model in real-world industrial applications.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"147 ","pages":"Article 103374"},"PeriodicalIF":3.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182256","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":"BP neural network-based explicit MPC of nonlinear boiler-turbine systems","authors":"Jing Li ,&nbsp;Defeng He ,&nbsp;Xiuli Wang ,&nbsp;Yu Kang","doi":"10.1016/j.jprocont.2024.103353","DOIUrl":"10.1016/j.jprocont.2024.103353","url":null,"abstract":"<div><div>This paper proposes a new explicit model predictive control (EMPC) scheme of constrained nonlinear systems with unknown but bounded input disturbances. Firstly, support vector machine is used to learn internal and external approximations of the feasible state space of the EMPC. Then, the control surface on the feasibility of EMPC is constructed by a backpropagation neural network (BPNN). The finite horizon optimal control solution to the EMPC can be computed from real-time data by training the control surface. The proposed EMPC is also suitable for nonlinear systems with higher dimensions in terms of reducing online computational burdens and enhancing control accuracy. Next, the Hoeffding's Inequality is used to ensure that the EMPC law computed by the BPNN approximation complies with the specified range with a high level of confidence. Moreover, some conditions are obtained to guarantee the stability and recursive feasibility of the EMPC with probabilistic assurances. Finally, a 160 MW boiler-turbine system is employed to verify the effectiveness and applications of the proposed method.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"145 ","pages":"Article 103353"},"PeriodicalIF":3.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096452","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":"Integrated fault estimation and fault-tolerant control for constrained LPV systems subject to bounded disturbances","authors":"Damiano Rotondo ,&nbsp;Marcin Pazera ,&nbsp;Marcin Witczak","doi":"10.1016/j.jprocont.2024.103343","DOIUrl":"10.1016/j.jprocont.2024.103343","url":null,"abstract":"<div><div>The paper deals with the issue of integrating fault estimation and fault-tolerant control for constrained continuous-time linear parameter varying systems with ellipsoidally bounded external disturbances. The unappealing effect related to the above coupling is that control and estimation influence each other. Thus, the main goal is to prevent such an unacceptable performance of the controlled system by providing a new integration strategy. The proposed framework is based on an output feedback approach, which is based on two staged: <em>off-line</em> – a low-complexity optimization task related to the fault estimation and control based on linear matrix inequalities, as well as <em>on-line</em> – a deterministic model predictive control for linear parameter-varying system. The effectiveness of this two-stage approach is illustrated with simulations based on a quadruple tank system.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"145 ","pages":"Article 103343"},"PeriodicalIF":3.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Input-Output Cross Autocorrelation Diagram (IO-CAD) for control loop performance assessment in offshore oil production","authors":"Leonardo M. De Marco ,&nbsp;Jorge Otávio Trierweiler ,&nbsp;Fábio César Diehl ,&nbsp;Marcelo Farenzena","doi":"10.1016/j.jprocont.2024.103345","DOIUrl":"10.1016/j.jprocont.2024.103345","url":null,"abstract":"<div><div>Severe slugging is a prevalent issue in offshore oil wells that significantly hampers oil production. While active pressure control has proven effective in mitigating this problem, determining optimal setpoints remains a manual and labor-intensive procedure. This study introduces the Input-Output Cross Autocorrelation Diagram (IO-CAD), which examines both input and output autocorrelations to provide a more comprehensive assessment of control loops compared to previous methods that only use output autocorrelation data. Four indicators based on IO-CAD were developed and tested in two case studies involving offshore oil production. They were compared to an oscillation detection method published in the literature, as oscillations in the control loop may indicate the slugging flow. Early detection of slugging patterns is crucial in offshore oil production to prevent severe slugging and stabilize control loops. The results demonstrated that the IO-CAD indicators are robust against setpoint changes, disturbances, and noise in the control loop performance assessment, while the oscillation detection method from the literature is sensitive to measurement and process noise, as well as control loop oscillations.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"145 ","pages":"Article 103345"},"PeriodicalIF":3.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096454","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}