Journal of Process Control最新文献_第6页

Multi-view graph convolutional network with comprehensive structural learning: Enhancing dynamics representation for industrial processes 具有综合结构学习能力的多视图卷积网络：增强工业流程的动态表示

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-09-07 DOI: 10.1016/j.jprocont.2024.103301

Tianhao Mou , Jinfeng Liu , Yuanyuan Zou , Shaoyuan Li

{"title":"Multi-view graph convolutional network with comprehensive structural learning: Enhancing dynamics representation for industrial processes","authors":"Tianhao Mou , Jinfeng Liu , Yuanyuan Zou , Shaoyuan Li","doi":"10.1016/j.jprocont.2024.103301","DOIUrl":"10.1016/j.jprocont.2024.103301","url":null,"abstract":"<div><p>Quality variable prediction is crucial for improving product quality and ensuring safety for industrial processes. Recently, researchers have explored the application of graph neural networks (GNNs) for this task, leveraging process knowledge encoded in graphs. GNN-based methods have demonstrated high prediction accuracy and partial interpretability. However, these methods typically consider only one type of prior graph and fail to utilize the multi-view prior graphs that coexist in the same process. This knowledge bias prevents effective representation learning about process dynamics, leading to inconsistencies with true process dynamics and overfitting. Thus. their practical applications are limited, especially under scenarios of limited data availability. To address this, a multi-view graph convolutional network with information short (MVGCN-IS) framework is proposed. MVGCN-IS comprises three key components: multi-view graph utilization, multi-view graph fusion, and information shortcut. First, multi-view prior graphs are integrated through multiple pre-trained preliminary GCNs to extract view-specific node representations. Then, a multi-view fusion module aggregates node representations from different views into unified unit representations, capturing comprehensive process structural information. Finally, an information shortcut extracts measurement representations and integrates detailed process measurement data to further enhance model performance. The proposed MVGCN-IS framework is validated on a benzene alkylation process and a debutanizer column process, with a special focus on model reliability under small data scenarios. Experimental results demonstrate the superior prediction accuracy and improved reliability of MVGCN-IS, validating its effectiveness in representation learning and capturing process dynamics.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"143 ","pages":"Article 103301"},"PeriodicalIF":3.3,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Low-carbon economic operation strategy for a multi-microgrid system considering internal carbon pricing and emission monitoring 考虑内部碳定价和排放监测的多微网系统低碳经济运行策略

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-09-06 DOI: 10.1016/j.jprocont.2024.103313

Ying Wang , Junxiang Li , Deqiang Qu , Xi Wang

{"title":"Low-carbon economic operation strategy for a multi-microgrid system considering internal carbon pricing and emission monitoring","authors":"Ying Wang , Junxiang Li , Deqiang Qu , Xi Wang","doi":"10.1016/j.jprocont.2024.103313","DOIUrl":"10.1016/j.jprocont.2024.103313","url":null,"abstract":"<div><p>With gradual deepening of a low-carbon transition of energy, the application of the multi-microgrid system (MMS) is becoming more and more popular. The internal carbon pricing mechanism is a key issue in realizing low carbon of the MMS. In order to fully utilize the advantages of energy mutual benefit and collaborative optimization, a real-time carbon trading model with cost minimization is established in the day-ahead market, in which the shadow price is taken as the optimal internal carbon price and the proposed distributed algorithm protects microgrids’ privacy. Furthermore, for the purpose of amending the deviation of carbon emission between the actual and target values, we design an automated process control (APC) strategy to adjust the real-time carbon price. And then a dual-objective problem is portrayed that balances cost and carbon emission deviation minimization in the intra-day market, and it is transformed into a single-objective constrained problem to be solved. Total cost and carbon emission decrease by 4.03% and 6.17% respectively in the solution. The results show that the proposed models have great performance of cost savings and carbon reduction for the MMS.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"143 ","pages":"Article 103313"},"PeriodicalIF":3.3,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simultaneous interval estimation of actuator fault and state for a class of nonlinear systems by zonotope analysis 通过区角分析法同时对一类非线性系统的致动器故障和状态进行区间估计

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-08-31 DOI: 10.1016/j.jprocont.2024.103303

Chi Xu , Zhenhua Wang , Vicenç Puig , Yi Shen

引用次数: 0

Quality-driven deep feature representation learning and its industrial application to soft sensors 质量驱动的深度特征表示学习及其在软传感器中的工业应用

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-08-29 DOI: 10.1016/j.jprocont.2024.103300

Xiao-Lu Song , Ning Zhang , Yilin Shi , Yan-Lin He , Yuan Xu , Qun-Xiong Zhu

{"title":"Quality-driven deep feature representation learning and its industrial application to soft sensors","authors":"Xiao-Lu Song , Ning Zhang , Yilin Shi , Yan-Lin He , Yuan Xu , Qun-Xiong Zhu","doi":"10.1016/j.jprocont.2024.103300","DOIUrl":"10.1016/j.jprocont.2024.103300","url":null,"abstract":"<div><p>Establishing effective soft sensors relies on feature representation that is capable of capturing critical information. Stacked AutoEncoder (SAE) is able to capture the intricate structures of data characterized by high dimensionality and strong non-linearity by extracting abstract features layer by layer, making it widely used. However, the pretraining process of SAE is unsupervised, which means the features extracted cannot leverage label information to provide more actionable insights for downstream tasks. To extract more valuable feature representation, a new quality-driven dynamic weighted SAE (QD-SAE) is proposed in this paper. By incorporating supervised information dominated by the quality variable into the learned features during the pretraining of the SAE and weighting the abstract features layer by layer, the features that are beneficial to the prediction task are thus focused. In QD-SAE, the supervised information is computed by an improved attention score. In the initial state of the supervised fine-tuning process, the weighted features compose the hidden layers of the entire network. Finally, a benchmark function case and a real complex industrial process case are used to verify the effectiveness and advantages of QD-SAE. The experimental analyses demonstrate that the soft sensor constructed by the QD-SAE can predict the output variable with high accuracy and outperforms the conventional neural networks.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"142 ","pages":"Article 103300"},"PeriodicalIF":3.3,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142095121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Simple modification to the Smith Predictor Structure for dealing with high-order delayed processes considering one unstable pole 对史密斯预测器结构的简单修改，用于处理考虑一个不稳定极点的高阶延迟过程

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-08-28 DOI: 10.1016/j.jprocont.2024.103299

J.F. Marquez-Rubio , B. del Muro-Cuéllar , R.J. Vazquez-Guerra , A. Urquiza-Castro , L.A. Barragan-Bonilla , C. Martínez

{"title":"A Simple modification to the Smith Predictor Structure for dealing with high-order delayed processes considering one unstable pole","authors":"J.F. Marquez-Rubio , B. del Muro-Cuéllar , R.J. Vazquez-Guerra , A. Urquiza-Castro , L.A. Barragan-Bonilla , C. Martínez","doi":"10.1016/j.jprocont.2024.103299","DOIUrl":"10.1016/j.jprocont.2024.103299","url":null,"abstract":"<div><p>The traditional Smith Predictor (SP) is restricted to dealing with stable plants. In this paper, a slight modification of the SP is proposed in order to control unstable plants: systems of any order but with one unstable pole are tackled. In fact, many modifications to the SP can be found in the literature dealing with this kind of system, but none, to our best knowledge, have the simplicity of the structure here proposed. Only one or two gains are added to the traditional SP structure to achieve the stabilization of this kind of unstable system. A simple relation states the necessary and sufficient condition guaranteeing the existence of stabilizing gains, in terms of the location of the unstable pole and the size of the delay term. The range of values for the gains solving the problem is characterized. In addition, the tracking of setpoints and disturbance rejections are analysed. Some numerical examples are presented to illustrate the effectiveness of the proposed strategy, as well as one real-time example.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"142 ","pages":"Article 103299"},"PeriodicalIF":3.3,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142087776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal switching of MPC cost function for changing active constraints 改变主动约束条件时 MPC 成本函数的优化切换

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-08-27 DOI: 10.1016/j.jprocont.2024.103298

Lucas Ferreira Bernardino, Sigurd Skogestad

{"title":"Optimal switching of MPC cost function for changing active constraints","authors":"Lucas Ferreira Bernardino, Sigurd Skogestad","doi":"10.1016/j.jprocont.2024.103298","DOIUrl":"10.1016/j.jprocont.2024.103298","url":null,"abstract":"<div><p>Model predictive control (MPC) allows for dealing with multivariable interactions, known future changes and dynamic satisfaction of constraints. Standard MPC has a cost function that aims at keeping selected controlled variables at constant setpoints. This work considers systems where the <em>steady-state optimal</em> active constraints change during operation. This situation is not handled optimally by standard MPC which uses fixed controlled variables for the unconstrained degrees of freedom. We propose a simple framework that detects the constraint changes and updates the controlled variables accordingly. The unconstrained controlled variables are chosen to be the reduced cost gradients, which when controlled to zero minimizes the steady-state economic cost. In this paper, the nullspace method for self-optimizing control is used to estimate the cost gradient using a static combination of the measurements. This estimated gradient is also used for detecting the current set of active constraints, which in particular allows for giving up constraints that were previously active. The proposed framework, here referred to as “region-based MPC”, is shown to be optimal for linear constrained systems with a quadratic economic cost function, and it allows for good economic performance in nonlinear systems in a neighborhood of the considered design points.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"142 ","pages":"Article 103298"},"PeriodicalIF":3.3,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0959152424001380/pdfft?md5=c36ee6904958a61e44d0258bd0afd216&pid=1-s2.0-S0959152424001380-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142087775","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}

引用次数: 0

EHOPN: A novel enhanced high-order pooling-based network for industrial fault detection EHOPN：用于工业故障检测的新型增强型高阶池网络

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-08-24 DOI: 10.1016/j.jprocont.2024.103296

Tongkang Zhang , Datong Li , Chun Li , Jinliang Ding

{"title":"EHOPN: A novel enhanced high-order pooling-based network for industrial fault detection","authors":"Tongkang Zhang , Datong Li , Chun Li , Jinliang Ding","doi":"10.1016/j.jprocont.2024.103296","DOIUrl":"10.1016/j.jprocont.2024.103296","url":null,"abstract":"<div><p>Recently, deep learning algorithms have been successfully applied to industrial fault detection because they are better at automatically extracting complex features and processing high-dimensional data than traditional methods. However, most existing deep learning-based fault detection methods only concentrate on extracting features from industrial process data without considering the crucial long-term temporal features and higher-order statistical information. To address this challenge, we proposed a novel enhanced higher-order pooling-based network (EHOPN) for industrial fault detection. First, the data pre-processing of the network is presented to capture the dynamic features of time-series process data and unify the high-dimensional data scale. Second, the EHOPN utilizes channel and temporal second-order pooling techniques to gather temporal and channel statistics information, facilitating the backbone network’s ability to capture complex inter-dependencies and long-term dynamics. Additionally, the high-order feature aggregation module is presented to aggregate global and local features, enhancing the network’s generalization ability. The proposed industrial fault detection approach is evaluated on the Tennessee Eastman benchmark and a real-world heavy-plate production process. Experimental results show that the proposed method is significantly better than comparison models in four evaluation metrics: accuracy, precision, recall, and F1-score, further proving the effectiveness of EHOPN.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"142 ","pages":"Article 103296"},"PeriodicalIF":3.3,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142049557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-08-21 DOI: 10.1016/j.jprocont.2024.103295

Xiangxiang Zhang , Wenkai Hu , Ahmad W. Al-Dabbagh , Jiandong Wang

{"title":"Improved similarity analysis of industrial alarm flood sequences by considering alarm correlations","authors":"Xiangxiang Zhang , Wenkai Hu , Ahmad W. Al-Dabbagh , Jiandong Wang","doi":"10.1016/j.jprocont.2024.103295","DOIUrl":"10.1016/j.jprocont.2024.103295","url":null,"abstract":"<div><p>Alarm floods are leading issues that compromise the efficiency of industrial alarm systems and are identified as major causes of many industrial accidents. As an advanced technique to handle alarm floods, sequence alignment based similarity analysis has been developed to match alarm flood sequences, and thus can help with further root cause identification and early warning of alarm floods. However, existing methods based on biological sequence alignment algorithms ignore the relations between alarm occurrences, and thus may cause incorrect matches or mismatches of alarms when comparing two flood sequences. Accordingly, this paper proposes a new alarm flood similarity analysis method based on global vectors and Move–Split–Merge (MSM) distance. The contributions are mainly twofold: (1) An alarm encoding model based on modified global vectors is devised to convert alarm sequences into numerical vectors that reflect the correlations of alarms; (2) a similarity analysis method based on the modified MSM distance is proposed for comparison of encoded alarm flood sequences of unequal lengths. The effectiveness of the proposed method is demonstrated through a case study with a publicly accessible industrial model for Vinyl Acetate Monomer.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"142 ","pages":"Article 103295"},"PeriodicalIF":3.3,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142020758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive state estimation for Markov jump linear system with unknown measurement loss and transition probability matrix 具有未知测量损失和转换概率矩阵的马尔可夫跳跃线性系统的自适应状态估计

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-08-07 DOI: 10.1016/j.jprocont.2024.103285

Yichao Yang, Chen Xu, Li Xie, Hongfeng Tao, Huizhong Yang

引用次数: 0

Full condition monitoring of geological drilling process based on just-in-time learning-aided slow feature analysis 基于即时学习辅助慢特征分析的地质钻探过程全状态监测

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-08-05 DOI: 10.1016/j.jprocont.2024.103284

Aoxue Yang , Min Wu , Chengda Lu , Jie Hu , Yosuke Nakanishi

{"title":"Full condition monitoring of geological drilling process based on just-in-time learning-aided slow feature analysis","authors":"Aoxue Yang , Min Wu , Chengda Lu , Jie Hu , Yosuke Nakanishi","doi":"10.1016/j.jprocont.2024.103284","DOIUrl":"10.1016/j.jprocont.2024.103284","url":null,"abstract":"<div><p>Presently, the demand for precise process monitoring during geological drilling has increased dramatically. However, there exists complex dynamic characteristics due to the various forms of changes in operation conditions. A large number of false alarms are usually triggered when using the conventional static-based monitoring methods. In this paper, two types of dynamic behaviors are comprehensively considered, including the dynamic behaviors caused by the operating parameters adjustment and the operating mode switching, and then, a full condition monitoring method is proposed for the drilling process based on just-in-time learning (JITL)-aided slow feature analysis (SFA). On one hand, the JITL local modeling strategy is improved and adopted to deal with the dynamic behavior due to the operating mode switching. Specifically, a sequence spatiotemporal similarity analysis method is developed to improve the local modeling performance. On the other hand, the SFA-based concurrent monitoring of static deviations and dynamic anomalies is realized to cope with the dynamic behavior due to the operating parameters adjustment. Several industrial cases based on actual drilling data are conducted, which illustrate the effectiveness and superiority of the proposed method.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"142 ","pages":"Article 103284"},"PeriodicalIF":3.3,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0