Advancements in Machine Learning for Optimal Performance in Flotation Processes: A Review

Abstract

Flotation stands out as a successful and extensively employed method for separating valuable mineral particles from waste rock. The efficiency of this process is subjected to the distinct physicochemical attributes exhibited by various minerals. However, the complex combination of multiple sub-processes within flotation presents challenges in controlling this mechanism and achieving optimal efficiency. Consequently, there is a growing dependence on machine learning methods in mineral processing research. This paper provides a comprehensive overview of machine learning and artificial intelligence techniques, presenting their potential applications in flotation processes. The review demonstrates advancements discussed in scholarly research over the past decade and highlights a growing interest in utilizing machine learning methods for monitoring and optimizing flotation processes, as demonstrated by the increasing number of studies in this field. Recent trends also suggest that the course of flotation process monitoring, and control will increasingly focus on the refinement and deployment of deep learning networks developed specifically for froth image extraction and analysis.

Editorial for Special Issue “Design, Modeling, Optimization and Control of Flotation Process”

“…Additionally, ongoing maintenance needs to be performed to uphold the system's high standards, which will incur additional expenses. To address these challenges, predictive models emerge as effective and economically viable solutions to handling the intricacies of the flotation process [2].…”