Please use this identifier to cite or link to this item: http://www.repositorio.ufop.br/jspui/handle/123456789/14478
Title: Simheuristic-based decision support system for efficiency improvement of an iron ore crusher circuit.
Authors: Santos, Mauro Sérgio
Pinto, Thomás Vargas Barsante
Lopes Júnior, Ênio
Cota, Luciano Perdigão
Souza, Marcone Jamilson Freitas
Euzébio, Thiago Antônio Melo
Keywords: Combinatorial optimization
Simulation
Issue Date: 2020
Citation: SANTOS, M. S. et al. Simheuristic-based decision support system for efficiency improvement of an iron ore crusher circuit. Engineering Applications of Artificial Intelligence, v. 94, artigo 103789, 2020. Disponível em: <https://www.sciencedirect.com/science/article/abs/pii/S0952197620301809>. Acesso em: 25 agosto 2021.
Abstract: The production rate of an ore crushing circuit depends on the amount of equipment in operation. If the amount of active equipment is less than the optimum level, the reduced ore flow paths restrict the production rate. However, if the amount of active equipment is greater than the optimum level, the excess circulating load ore and extra energy consumption reduce the circuit efficiency. In addition, the optimum amount of active equipment can change over time due to changes in the ore characteristics, such as hardness and particle size. In this paper, a decision support system is proposed for optimizing the amount of active equipment for maximum crushing production considering changes in the circuit feed rate. The proposed solution is based on a simheuristic approach in which a simulated plant model is used to evaluate the production rate. Real production scenarios at a Brazilian mining plant are used in computational experiments. The results show that the simheuristic solutions generate a higher production rate and result in less energy consumption. Production is increased by up to 9%, and energy consumption is reduced by up to 59%, demonstrating the efficacy of the proposal.
URI: http://www.repositorio.ufop.br/jspui/handle/123456789/14478
metadata.dc.identifier.uri2: https://www.sciencedirect.com/science/article/abs/pii/S0952197620301809
metadata.dc.identifier.doi: https://doi.org/10.1016/j.engappai.2020.103789
ISSN: 0952-1976
Appears in Collections:DECOM - Artigos publicados em periódicos

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