{"title":"Geometric constructive network with block increments for lightweight data-driven industrial process modeling","authors":"Jing Nan , Wei Dai , Haijun Zhang","doi":"10.1016/j.jprocont.2023.103159","DOIUrl":null,"url":null,"abstract":"<div><p>Industrial data-driven models may require frequent reconstruction to maintain model performance due to the dynamics, uncertainty, and complexity of industrial processes. The infrastructure of the industrial processes is usually distributed control systems<span> (DCS) with energy-sensitive and resource-constrained. In this context, this article proposes a geometric constructive network with block increments (BI-GCN) to reduce the modeling consumption<span> while achieving comparable accuracy. First, this article proposes a geometric control strategy with block increments, which is capable of adding multiple nodes to the BI-GCN simultaneously. Second, this article demonstrates the universal approximation property of BI-GCN, which in turn guarantees the potential high performance of BI-GCN for modeling tasks. Finally, experiments on benchmark datasets and the grinding process show that BI-GCN can effectively reduce the number of iterations in the modeling process while maintaining comparable accuracy.</span></span></p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-01-10","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/S0959152423002470","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
Industrial data-driven models may require frequent reconstruction to maintain model performance due to the dynamics, uncertainty, and complexity of industrial processes. The infrastructure of the industrial processes is usually distributed control systems (DCS) with energy-sensitive and resource-constrained. In this context, this article proposes a geometric constructive network with block increments (BI-GCN) to reduce the modeling consumption while achieving comparable accuracy. First, this article proposes a geometric control strategy with block increments, which is capable of adding multiple nodes to the BI-GCN simultaneously. Second, this article demonstrates the universal approximation property of BI-GCN, which in turn guarantees the potential high performance of BI-GCN for modeling tasks. Finally, experiments on benchmark datasets and the grinding process show that BI-GCN can effectively reduce the number of iterations in the modeling process while maintaining comparable accuracy.
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.