Control of conventional continuous thickeners via proximal policy optimization.

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorSilva, Jonathan R.-
Autor(es): dc.creatorEuzebio, Thiago Antonio Melo-
Autor(es): dc.creatorBraga, Marcio Feliciano-
Data de aceite: dc.date.accessioned2025-08-21T15:48:35Z-
Data de disponibilização: dc.date.available2025-08-21T15:48:35Z-
Data de envio: dc.date.issued2024-11-25-
Data de envio: dc.date.issued2024-11-25-
Data de envio: dc.date.issued2023-
Fonte completa do material: dc.identifierhttps://www.repositorio.ufop.br/handle/123456789/19193-
Fonte completa do material: dc.identifierhttps://www.sciencedirect.com/science/article/pii/S0892687524001900-
Fonte completa do material: dc.identifierhttps://doi.org/10.1016/j.mineng.2024.108761-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1024634-
Descrição: dc.descriptionWith the rise of historical data availability and increased computational power, reinforcement learning (RL) has become more prominent. It has shown the ability to outperform human knowledge in certain areas. RL is particularly valuable in control applications due to its generalization, self-adjustment, and independence from a mathematical model. Despite its potential, there are limited studies and applications focusing on using RL for mining processes. This study aims to showcase the applicability of RL in the mining industry. An algorithm was developed using proximal policy optimization (PPO) to control a simulated conventional cylindrical-conical thickener. The controller’s goal is to adjust the slurry density and solid interface height to produce a material with a high solid concentration. By regulating the thickener’s output flow and the flocculant dosage in the incoming slurry, PPO achieves this objective. Through simulation, an RL agent was trained to control the thickener efficiently, managing flocculant usage and responding appropriately to system disturbances. The study concludes that applying reinforcement learning holds promise for enhancing mining process control. However, further research is needed to fine-tune algorithm parameters, enhance design structure, optimize control, and maximize the technique’s benefits.-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Direitos: dc.rightsrestrito-
Palavras-chave: dc.subjectReinforcement learning-
Palavras-chave: dc.subjectProcess control-
Palavras-chave: dc.subjectThickener-
Palavras-chave: dc.subjectMineral processing-
Título: dc.titleControl of conventional continuous thickeners via proximal policy optimization.-
Aparece nas coleções:Repositório Institucional - UFOP

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