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Title: Composite SVR Based Modelling of an Industrial Furnace
Authors: Santos, Daniel
Rato, Luís
Gonçalves, Teresa
Barão, Miguel
Costa, Sérgio
Malico, Isabel
Canhoto, Paulo
Editors: Simian, D.
Stoica, L.F.
Keywords: Energy efficiency
Industrial furnaces
Reduced order model
Support vector regression
Hybrid model
Issue Date: 17-Jan-2020
Publisher: Springer
Citation: Santos, D., Rato, L., Gonçalves, T., Barão, M., Costa, S., Malico, I., Canhoto, P. (2020) Composite SVR Based Modelling of an Industrial Furnace. In: Simian D., Stoica L. (eds) Modelling and Development of Intelligent Systems. MDIS 2019. Communications in Computer and Information Science, vol 1126, pp. 158–170. Springer, Cham. DOI: 10.1007/978-3-030-39237-6_11
Abstract: Industrial furnaces consume a large amount of energy and their operating points have a major influence on the quality of the final product. Designing a tool that analyzes the combustion process, fluid mechanics and heat transfer and assists the work done during energy audits is then of the most importance. This work proposes a hybrid model for such a tool, having as its base two white-box models, namely a detailed Computational Fluid Dynam- ics (CFD) model and a simplified Reduced-Order (RO) model, and a black-box model developed using Machine Learning (ML) techniques. The preliminary results presented in the paper show that this com- posite model is able to improve the accuracy of the RO model without having the high computational load of the CFD model.
Type: article
Appears in Collections:ICT - Artigos em Livros de Actas/Proceedings

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