Artificial neural network-based sensitivity analysis and experimental investigation of liquid–solid fluidization technique for low-grade coal upgradation

Abstract

Liquid-solid fluidization technique is being applied where low-grade coal or minerals enrichment is mostly density-based. Static and dynamic behavior of particles in a fluid medium has been extensively investigated over the years because of its dynamic applications across various industries. In this work, bed characterization studies and experiments have been conducted to study coal washing ability of the liquid-solid fluidized bed separator. Results have been recorded in terms of ash rejection%, combustible recovery% and separation efficiency%. Minimum fluidization velocity and pressure drop values have been predicted using existing theoretical correlations and compared with the experimental values. A three-layered (4:5:3) feedforward back-propagation (FFBP) neural network model was developed using Levenberg-Marquardt algorithm, LOGSIG and MSE as training, transfer and performance functions respectively. Garson’s algorithm and connection weight approach have been employed for sensitivity analysis to interpret the neural network results physically. Coefficients of correlation, all R (including training, validation & testing datasets) obtained for outputs ash rejection (R¼0.9960), combustible recovery (R¼0.9952) and separation efficiency (R¼0.9944) suggest that predicted values are in agreement with the experimental values and the developed model is a good fit.