Journal of Process Control最新文献

{"title":"A continuous-time LPV models for a biofiltration process in wastewater nitrification — A global approach methodology for parametric estimation","authors":"Fatima Zahra Boutourda ,&nbsp;Régis Ouvrard ,&nbsp;Thierry Poinot ,&nbsp;Driss Mehdi ,&nbsp;Fouad Mesquine ,&nbsp;Éloïse De Tredern ,&nbsp;Vincent Jauzein","doi":"10.1016/j.jprocont.2024.103356","DOIUrl":"10.1016/j.jprocont.2024.103356","url":null,"abstract":"<div><div>Biological wastewater treatment processes are essential in the sustainable management of water resources, offering an efficient method for removing contaminants and pollutants, such as ammonium, from wastewater to protect both public health and the environment. Among various treatment methods, submerged aerated biofilters stand out for their efficiency in converting high ammonium concentrations into nitrate. This process stimulates the growth of specific microorganisms on filtering materials, aiding in efficient pollutant conversion.</div><div>However, the complexity of biological wastewater treatment processes presents significant modeling challenges, especially under varying operational conditions. Linear Parameter-Varying (LPV) models have emerged as a promising solution to accurately represent these nonlinear systems. Despite their potential, constructing LPV models remains complex, especially for intricate biological treatment processes like wastewater treatment.</div><div>This paper presents a novel methodology within the global approach framework for estimating continuous-time LPV models. The proposed approach addresses the challenge of initializing iterative procedures due to the lack of prior knowledge about LPV model parameters. By extending the reinitialized partial moment approach to LPV models, the methodology provides an effective pre-estimate for initializing parameter estimation algorithms. Validation of the proposed methodology through simulation examples establishes a robust foundation for extending the approach to real-world applications, such as estimating LPV models for the nitrification process in wastewater treatment plants.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"146 ","pages":"Article 103356"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174879","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":"Distributed process monitoring of the large-scale system using spatio-temporal-causality and Wasserstein-distance-based canonical variate analysis","authors":"Chong Xu ,&nbsp;Daoping Huang ,&nbsp;Guangping Yu ,&nbsp;Yiqi Liu","doi":"10.1016/j.jprocont.2024.103367","DOIUrl":"10.1016/j.jprocont.2024.103367","url":null,"abstract":"<div><div>Distributed process monitoring gains popularity recently to perform system health management for large-scale industrial processes and support the decision-making for system maintenance. However, process monitoring for complex large -scale systems using distributed approaches is often challenging due to significant nexus among variables. Therefore, this article proposed a novel distributed process monitoring method to achieve efficient monitoring with a reasonable and interpretable division scheme which is only given by the spatial distribution of each variable and the results of Granger causality analysis. At each subblock, a local canonical variate analysis model with Wasserstein-distance-based indices can be built to monitor each local system. With the help of a Bayesian inference strategy, all the local monitoring results are fused into a global one. Then, from both block-level and variable-level, the proposed hierarchical fault isolation method can sort out candidates for the rooting causality analysis of the detected fault, respectively. Depending on the causal analysis, the rooting cause can be identified from the intersection of two candidate sets, thereby virtualizing the propagation path of a fault. Lastly, the presented methodology of distributed process monitoring is verified by a numeral case study and the Tennessee Eastman (TE) benchmarking platform, respectively. The conclusions show that the presented methodology can perform more accurately and efficiently than traditional approaches. In particular, the proposed method can detect simulated faults in a mathematical case and the fault 15 in the TE process with nearly 100 % and 94.72 %, respectively, in terms of fault detection rates, which is barely achieved by reported methods</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"146 ","pages":"Article 103367"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174925","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 new class of fault detection and diagnosis methods by fusion of spatially distributed and time-dependent features","authors":"Yan Chen,&nbsp;Xiaoyu Zhang,&nbsp;Dazi Li,&nbsp;Jinglin Zhou","doi":"10.1016/j.jprocont.2024.103372","DOIUrl":"10.1016/j.jprocont.2024.103372","url":null,"abstract":"<div><div>Nonlinear, non-Gaussian, and dynamic features pose a great challenge for complex fault detection and fault diagnosis (FDD). Focusing on fault detection, independent component analysis (ICA) and adversarial autoencoder (AAE) are fused to form a new method for nonlinear non-Gaussian latent variable extraction: ICA–AAE. In addition, a strategy for establishing more accurate fault detection thresholds using tail distribution features is presented. Furthermore, a new class of fault diagnosis frameworks to fully exploit the information obtained from normal samples is developed. Fault data are first re-represented using the established ICA–AAE model. Then, the low-dimensional spatial distribution features with their inherited high-dimensional temporal dependencies are synthesized into image information using an image-based approach, and a spatio-temporal fusion fault diagnosis method is implemented using a convolutional neural network (CNN). Tennessee Eastman (TE) process results show that the proposed methods can achieve more accurate fault detection and diagnosis.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"146 ","pages":"Article 103372"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174923","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 data-driven design of fault-tolerant control systems with unknown dynamics","authors":"Wenli Chen ,&nbsp;Xiaojian Li","doi":"10.1016/j.jprocont.2024.103370","DOIUrl":"10.1016/j.jprocont.2024.103370","url":null,"abstract":"<div><div>This paper investigates the adaptive data-driven design issue for fault-tolerant control systems with unknown dynamics. Initially, the fault-tolerant control problem is transformed into a stabilization problem for switched systems, where both the switching signal and system dynamics are unknown due to the uncertainties in fault occurrence instants and faulty modes. While extensive research has been conducted on switched systems, the strategies for addressing unknown switching signals remain comparatively scarce, especially when system dynamics are also unknown. To tackle this issue, a Lyapunov function-based monitoring scheme is provided to determine the time instants of switching in system dynamics during operation. Subsequently, a data-driven adaptive learning control mechanism is introduced to update feedback gains. Considering the asynchronous issue between the switching of the controller and system dynamics due to the learning process, sufficient conditions concerning the switching frequency of the system dynamics are provided. Thereby, a data-driven adaptive learning fault-tolerant control algorithm is proposed. Under the frequency constraint on the switching of system dynamics, it is shown that the offered control scheme maintains the closed-loop system’s stability. Finally, two simulation examples are provided to show the effectiveness of the proposed approach.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"146 ","pages":"Article 103370"},"PeriodicalIF":3.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174924","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":"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}