Artificial neural network approach for rheological characteristics of coal-water slurry using microwave pre-treatment

Abstract

Detailed experimental investigations were carried out for microwave pre-treatment of high ash Indian coal at high power level (900 W) in microwave oven. The microwave exposure times were fixed at 60 s and 120 s. A rheology characteristic for microwave pre-treatment of coal-water slurry (CWS) was performed in an online Bohlin viscometer. The non-Newtonian character of the slurry follows the rheological model of Ostwald de Waele. The values of n and K vary from 0.31 to 0.64 and 0.19 to 0.81 Pa⋅sn, respectively. This paper presents an artificial neural network (ANN) model to predict the effects of operational parameters on apparent viscosity of CWS. A 4-2-1 topology with Levenberg-Marquardt training algorithm (trainlm) was selected as the controlled ANN. Mean squared error (MSE) of 0.002 and coefficient of multiple determinations (R²) of 0.99 were obtained for the outperforming model. The promising values of correlation coefficient further confirm the robustness and satisfactory performance of the proposed ANN model.