{"title":"A novel parallel feature extraction-based multibatch process quality prediction method with application to a hot rolling mill process","authors":"Kai Zhang , Xiaowen Zhang , Kaixiang Peng","doi":"10.1016/j.jprocont.2024.103166","DOIUrl":null,"url":null,"abstract":"<div><p>In a hot strip rolling mill (HSRM) process, the prediction of the steel crown is a key factor in improving the quality of the strip steel. In this paper, a new multibatch feature extraction-based method is proposed for predicting the steel crown. Different from the cascaded feature extraction-based method which cannot extract both temporal and local features well, this method parallelly captures the feature between different batches of data using a method based on the multi-channel convolution neural network<span> (MCNN) and long short-term memory (LSTM). The feature extraction is performed in parallel by an LSTM layer fusing variable attention and temporal attention, and a Multi-channel convolutional neural network fusing channel attention and spatial attention, which are used to extract temporal and local features of the input variables, respectively. Then, an LSTM-based fusion layer is used to incorporate both features for the development of the prediction model. The proposed method is applied to a cloud–edge-end collaborative prototype system, where the actual HSRM data is integrated. Based on the fact that an HSRM process commonly runs with the steel header crown data for the model update, an adaptive prediction method is also developed and deployed in the prototype system. It can be seen from the model complexity analysis and application results that the prediction performance improves by 42.70% compared with the cascaded feature extraction-based method, and the adaptive method can ensure a realtime prediction realization.</span></p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Process Control","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0959152424000064","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
In a hot strip rolling mill (HSRM) process, the prediction of the steel crown is a key factor in improving the quality of the strip steel. In this paper, a new multibatch feature extraction-based method is proposed for predicting the steel crown. Different from the cascaded feature extraction-based method which cannot extract both temporal and local features well, this method parallelly captures the feature between different batches of data using a method based on the multi-channel convolution neural network (MCNN) and long short-term memory (LSTM). The feature extraction is performed in parallel by an LSTM layer fusing variable attention and temporal attention, and a Multi-channel convolutional neural network fusing channel attention and spatial attention, which are used to extract temporal and local features of the input variables, respectively. Then, an LSTM-based fusion layer is used to incorporate both features for the development of the prediction model. The proposed method is applied to a cloud–edge-end collaborative prototype system, where the actual HSRM data is integrated. Based on the fact that an HSRM process commonly runs with the steel header crown data for the model update, an adaptive prediction method is also developed and deployed in the prototype system. It can be seen from the model complexity analysis and application results that the prediction performance improves by 42.70% compared with the cascaded feature extraction-based method, and the adaptive method can ensure a realtime prediction realization.
期刊介绍:
This international journal covers the application of control theory, operations research, computer science and engineering principles to the solution of process control problems. In addition to the traditional chemical processing and manufacturing applications, the scope of process control problems involves a wide range of applications that includes energy processes, nano-technology, systems biology, bio-medical engineering, pharmaceutical processing technology, energy storage and conversion, smart grid, and data analytics among others.
Papers on the theory in these areas will also be accepted provided the theoretical contribution is aimed at the application and the development of process control techniques.
Topics covered include:
• Control applications• Process monitoring• Plant-wide control• Process control systems• Control techniques and algorithms• Process modelling and simulation• Design methods
Advanced design methods exclude well established and widely studied traditional design techniques such as PID tuning and its many variants. Applications in fields such as control of automotive engines, machinery and robotics are not deemed suitable unless a clear motivation for the relevance to process control is provided.