Novel Advanced Machine Learning Methods in Mineral Processing

Dear Colleagues,

In mineral processing, there are several linear and nonlinear relationships between material properties and process and design parameters. These correlations can be assessed based on various experimental and numerical methods. Over the last three decades, different intelligent computing and statistical methods, such as genetic algorithms, artificial neural networks, various types of multivariable regression, and tree-based systems, have been introduced in order to describe the complex and sometimes nonlinear relationships. This has resulted in the generation of various intelligent models for the prediction of process responses, i.e., recovery, grade, and comminution or separation efficiency. This special issue will explore the application of “novel advanced machine learning methods in mineral processing”.

Prof. Dr. Saeed Chehreh Chelgani
Prof. Dr. Jan Rosenkranz
Guest Editors

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