Lorenzo Tuissi, Daniele Ravasio, S. Spinelli, A. Ballarino
{"title":"Neural Network Modeling of the Refining Motor Load for Medium-Density Fibreboard Production","authors":"Lorenzo Tuissi, Daniele Ravasio, S. Spinelli, A. Ballarino","doi":"10.1109/ICCAE56788.2023.10111131","DOIUrl":null,"url":null,"abstract":"In this study, artificial neural networks are adopted to perform multi-step predictions of the power consumed by the refiner of a thermo-mechanical pulping process specialized in medium-density fiberboard production. In this way, the obtained model can be integrated within a model-based control. The refining process is characterized by a large number of variables, and artificial neural networks are a well-established methodology for multivariate data processing, able to identify the non-linear hidden relationship between monitored variables. Both a Long Short-Term Memory network, with stability guarantees, and a Transformer one are implemented due to their ability to model the evolution of dynamical systems. Simulation results prove both models’ multi-step prediction capabilities.","PeriodicalId":406112,"journal":{"name":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 15th International Conference on Computer and Automation Engineering (ICCAE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCAE56788.2023.10111131","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this study, artificial neural networks are adopted to perform multi-step predictions of the power consumed by the refiner of a thermo-mechanical pulping process specialized in medium-density fiberboard production. In this way, the obtained model can be integrated within a model-based control. The refining process is characterized by a large number of variables, and artificial neural networks are a well-established methodology for multivariate data processing, able to identify the non-linear hidden relationship between monitored variables. Both a Long Short-Term Memory network, with stability guarantees, and a Transformer one are implemented due to their ability to model the evolution of dynamical systems. Simulation results prove both models’ multi-step prediction capabilities.