Please use this identifier to cite or link to this item: http://hdl.handle.net/10174/26758

Title: Simulation of a Billet Heating Furnace
Authors: Cavaleiro Costa, Sérgio
Malico, Isabel
Santos, Daniel
Barão, Miguel
Gonçalves, Teresa
Rato, Luís
Canhoto, Paulo
Lima, Rui Pedro
Oliveira, Sofia
Fontes, Paulo
Cravo, Susana
Keywords: Energy Efficiency
Industrial Furnaces
Reduced-order Models
CFD
Machine Learning
Issue Date: Sep-2019
Citation: Cavaleiro Costa, S., Malico, I., Santos, D., Barão, M., Gonçalves, T., Rato, L., Canhoto, P., Lima, P., Oliveira, S., Fontes, P., Cravo, S. (2019). Simulation of a billet heating furnace. The 5th Ibero-American Congress on Entrepreneurship, Energy, Environment and Technology – CIEEMAT '19, Portalegre, Portugal, 11-13 de Setembro, pp. 160-164. ISBN: 978-84-17934-30-9.
Abstract: This work presents the method developed in the scope of the “Audit Furnace” project to support the manufacturing industry in understanding the energy efficiencies of its furnaces and to identify strategies for the continuous improvement of its processes. A digital representation to support the development, calibration, and training of a physical-based reduced-order model for industrial furnaces is sought by integrating experimental data obtained in energy audits performed at several industrial units with detailed numerical results from computational fluid dynamics simulations of the furnaces. Composite models with two blocks, a physics-based reduced-order block, and a machine learning model block, are proposed in order to simultaneously achieve performance and flexibility in its adaptation to different furnaces, while keeping the computational load in acceptable levels. In this paper, preliminary results of the application of the method to a billet heating furnace are presented, namely the results of the computational fluid dynamics simulations of the furnace and their comparison with the measurements performed in an energy audit. This is the first, essential step of the proposed method. The numerical results generated will allow calibrating and training the reduced-order model and will feed the machine learning model training process.
URI: http://hdl.handle.net/10174/26758
Type: lecture
Appears in Collections:CEM - Comunicações - Em Congressos Científicos Internacionais

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