Modelling of coal flotation responses based on operational conditions by random forest

Abstract

Coal consumption is one of the critical factors in the economy of China. Flotation separation of coal from its inorganic part (ash) can reduce environmental problems of coal consumption and improve its combustion. This investigation used random forest (RF) as an advanced machine learning method to rank flotation operations by variable importance measurement and predict flotation responses based on operational parameters. Fifty flotation experiments were designed, and performed based on various flotation conditions and by different variables (collector dosage, frother dosage, air flowrate, pulp density, and impeller speed). Statistical assessments indicated that there is a significant negative correlation between yield and ash content. Experiments indicated that in the optimum conditions, yield and ash content would be 80 and 9%, respectively. Variable importance measurement by RF showed that frother has the highest effectiveness on yield. Outcomes of modelling released that RF can accurately be used for ranking flotation parameters, and generating models within complex systems in mineral processing. [Received: May 20, 2020; Accepted: July 19, 2020]