Flow characteristics of microwave treated Indian coal: A deep learning modelling

Abstract

To meet the escalating energy needs of the growing population, the power stations are gradually shifting towards abundant low-quality coals and lignites. This study proposes a sustainable methodology to utilize such low-quality coals by cutting down the coal pretreatment costs. Instead of utilizing the treated coal as a whole, this study proposes to blend the treated coal with the untreated coal for further utilization. These blended samples are then used to prepare coal water slurry, with concentrations varying in the range of 10 to 60% by mass. The rheology of the blended samples shows a reduction in viscosity by ∼ 23% when the blended sample contains 50% treated coal. The sustainability of the process is verified by a trained artificial neural network, that includes an extensive validation of predicted results with experimental data and Irvine (1988) model. The artificial neural network indicates a maximum of 11.5% reduction in the headloss when the blended sample is transported at 50% solid concentration. The specific enthalpy consumption is found to be the minimum for the blended samples transported at 50% solid concentration.