Control of a thickening process based on a data-driven model predictive controller

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dc.creatorThomás Vargas Barsante e Pinto
dc.creatorThiago Antônio Melo Euzébio
dc.creatorGuilherme Vianna Raffo
dc.date.accessioned2025-06-03T14:36:05Z
dc.date.accessioned2025-09-09T01:24:20Z
dc.date.available2025-06-03T14:36:05Z
dc.date.issued2023
dc.identifier.doihttps://doi.org/10.20906/SBAI-SBSE-2023/4040
dc.identifier.urihttps://hdl.handle.net/1843/82740
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofXVI Simpósio Brasileiro de Automação Inteligente (SBAI 2023)
dc.rightsAcesso Aberto
dc.subjectMinas e mineração
dc.subjectAprendizado do computador
dc.subject.otherData-driven control, Machine learning, Mineral industry, Model predictive control and Thickening process
dc.titleControl of a thickening process based on a data-driven model predictive controller
dc.typeArtigo de evento
local.citation.epage7
local.citation.spage1
local.description.resumoThe mineral industry is comprised of several large-scale, complex processes that require tight control in order to operate appropriately. Among them, the thickening process is a solid-liquid separation unit operation whose highly nonlinear and slow dynamics pose challenges in obtaining an accurate process model. Consequently, model-based controllers, such as the model predictive controller (MPC), despite all its advantages, do not achieve their best performance in such an industrial environment. In this work, we investigate using a data-driven predictive control (DDPC) approach to control the thickening process, in which we integrate a predictive control formulation and a prediction technique called Lazily Adaptive Constant Kinky Inference (LACKI). The proposed method makes use of process data and a machine learning technique to supply the lack of an accurate model. Simulated results show that this approach performs satisfactorily in controlling the thickening process.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.initialsUFMG
local.url.externahttps://www.sba.org.br/open_journal_systems/index.php/sbai/article/view/4040

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