Machine vision based monitoring and analysis of a coal column flotation circuit

Abstract

Flotation columns are being routinely used for recovery of fine coal particles in the coal preparation plants. Because of high sensitivity of the flotation columns to variations in the process conditions, their continuous control is of vital importance. Machine vision is an economically viable, uncomplicated and reliable technique for monitoring and control of flotation circuits. In this study, a machine vision system is successfully developed and implemented in a coal column flotation circuit. Industrial flotation experiments are conducted at various operating conditions (air flow rate, slurry solids%, froth depth, frother and collector dosage) and the froth visual (bubble size, froth velocity, froth color) and textural (energy, entropy, contrast, homogeneity and correlation) features along with the metallurgical performances (combustible recovery, concentrate ash content) are recorded simultaneously. The relationships between the froth characteristics with the process as well as the performance parameters are analyzed. The promising results indicate that the developed system can be successfully used for diagnosing the process conditions as well as predicting the process performance at different operating conditions.