{"title":"Enhancing interactive optimization with operating condition supervision for distillation units","authors":"Sihong Li, Yi Zheng, Yuanyuan Zou, Shaoyuan Li","doi":"10.1016/j.conengprac.2024.105942","DOIUrl":null,"url":null,"abstract":"<div><p>Changing operating conditions is a common phenomenon in the distillation unit (DU), which poses difficulties to production operations. To fully cope with varying operating conditions and further improve production performance, an interactive operation optimization strategy (OOS) with operating condition supervision is proposed in this work. In this strategy, the operating condition supervision module perfectly cooperates with the interactive optimization strategy to reduce the two major performance losses incurred after changes in operating conditions. The bidirectional interaction between the optimization layer and the control layer during operation can eliminate losses and hazards caused by delayed response and inter-layer mismatches, ultimately achieving optimal closed-loop performance. Through detailed analysis of the specific industrial behavior, it provides professional support for the production operations under varying operating conditions. It is worth mentioning that a convolutional neural network (CNN) process model based on parameter transfer is established. It can be fine-tuned online. Experimental results show the proposed strategy can flexibly and effectively handle changes in operating conditions. The proposed OOS improves the product qualification rate and has broad application prospects in industrial processes.</p></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":null,"pages":null},"PeriodicalIF":5.4000,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Control Engineering Practice","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0967066124001023","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Changing operating conditions is a common phenomenon in the distillation unit (DU), which poses difficulties to production operations. To fully cope with varying operating conditions and further improve production performance, an interactive operation optimization strategy (OOS) with operating condition supervision is proposed in this work. In this strategy, the operating condition supervision module perfectly cooperates with the interactive optimization strategy to reduce the two major performance losses incurred after changes in operating conditions. The bidirectional interaction between the optimization layer and the control layer during operation can eliminate losses and hazards caused by delayed response and inter-layer mismatches, ultimately achieving optimal closed-loop performance. Through detailed analysis of the specific industrial behavior, it provides professional support for the production operations under varying operating conditions. It is worth mentioning that a convolutional neural network (CNN) process model based on parameter transfer is established. It can be fine-tuned online. Experimental results show the proposed strategy can flexibly and effectively handle changes in operating conditions. The proposed OOS improves the product qualification rate and has broad application prospects in industrial processes.
期刊介绍:
Control Engineering Practice strives to meet the needs of industrial practitioners and industrially related academics and researchers. It publishes papers which illustrate the direct application of control theory and its supporting tools in all possible areas of automation. As a result, the journal only contains papers which can be considered to have made significant contributions to the application of advanced control techniques. It is normally expected that practical results should be included, but where simulation only studies are available, it is necessary to demonstrate that the simulation model is representative of a genuine application. Strictly theoretical papers will find a more appropriate home in Control Engineering Practice''s sister publication, Automatica. It is also expected that papers are innovative with respect to the state of the art and are sufficiently detailed for a reader to be able to duplicate the main results of the paper (supplementary material, including datasets, tables, code and any relevant interactive material can be made available and downloaded from the website). The benefits of the presented methods must be made very clear and the new techniques must be compared and contrasted with results obtained using existing methods. Moreover, a thorough analysis of failures that may happen in the design process and implementation can also be part of the paper.
The scope of Control Engineering Practice matches the activities of IFAC.
Papers demonstrating the contribution of automation and control in improving the performance, quality, productivity, sustainability, resource and energy efficiency, and the manageability of systems and processes for the benefit of mankind and are relevant to industrial practitioners are most welcome.