Estimation of Combustible Recovery and Ash Content of High-Ash Lignite Using MLR and ANN Regression Analyses

If concentrating coal is difficult or impossible using gravity methods (such as jigs, shaking spirals, dense-media drum, and cyclone), which are among the cheapest and simplest options, flotation becomes an alternative. This is due to the differences in surface chemistry properties between the relatively hydrophobic coal and the gangue minerals. On the other hand, flotation methods are far more complex than gravity methods and involve many more parameters that influence concentrate, such as coal particle size, amounts of reagents dosages (e.g., collectors, activators, depressants, and frothers), conditioning times, pulp mixing speeds, flotation times, and pH levels of the pulp medium. In flotation methods with so many variables, determining the combustible recovery (CR) and ash content (AC) of clean coal concentrate that can be obtained may require many experiments. To facilitate these challenging processes, understand the effects of parameters influencing concentration on the flotation method, and estimate the resulting clean coal recovery and ash content, it is necessary to utilize various statistical regression methods. In this study, the effects of six parameters on the flotation of a lignite coal sample with 40% ash content were used to estimate the CR and AC of coal concentrate using multivariate linear regression (MLR) and artificial neural network (ANN) models. As a result, the ANN model demonstrated superior estimate accuracy, with correlation coefficients of 0.988 and 0.963, compared with the MLR models (R2 = 0.575 and 0.540) for estimating the ash content (AC, %) and combustible recovery (CR, %) of coal concentrate, respectively.