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- W4308161211 endingPage "107884" @default.
- W4308161211 startingPage "107884" @default.
- W4308161211 abstract "Although various articles have reviewed the application of artificial intelligence (AI) in froth flotation (summarized in this article), other unit operations for mineral concentration in mineral processing have not been reviewed. Thus, this article reviews AI application in various unit operations for mineral concentration. Because unit operations for mineral concentration deal with yields not necessarily linearly correlated with input variables, subsequent yield prediction using AI can add value to their control. The current applications of AI have neglected fundamental variables (e.g., particle agglomeration, particle magnetic susceptibility, particle wettability, particle surface charge, and particle Hamaker constant) as inputs for prediction. Instrumentation and industrial simplicity have hindered the consideration of those variables because validation is required. There are kind learning (repeated patterns and high accuracy measurements) and wicked learning (continuously novel patterns and noise in measurements) environments, which are suitable and challenging for machine learning, respectively. Kind learning environments were largely used for the applications of AI. Furthermore, flow can be captured by AI (e.g., neural networks) to attempt to control drag and mixing using synthetic jet type actuators in equipment (shaking tables, fluidized beds, or vessels). Thus, future applications of AI should consider these points." @default.
- W4308161211 created "2022-11-08" @default.
- W4308161211 creator A5000483708 @default.
- W4308161211 creator A5002204140 @default.
- W4308161211 creator A5030433920 @default.
- W4308161211 creator A5075548073 @default.
- W4308161211 date "2022-11-01" @default.
- W4308161211 modified "2023-10-12" @default.
- W4308161211 title "A critical review of artificial intelligence in mineral concentration" @default.
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