Artificial neural network modeling for predicting the screening efficiency of coal with varying moisture content in the vibrating screen

Abstract

ABSTRACT In India, coal is one of the prime sources of energy used in the power generation and metallurgy sector. The processing of coal below 3 mm is not successfully carried out in India. The quality of coal below 3 mm can be improved by decreasing the coal’s particle size, which reduces the ash percentage of coal. Screening is one of the significant beneficiation techniques used to reduce the size fraction of coal. The difficult to process coal of size −3 + 1 mm was selected in the present work. In this work, an attempt has been made to screen the coal of size −2 + 1 mm from −3 + 1 mm using a 2 mm screen mesh in the vibrating screen generated at different moisture content, angle, and frequency of the deck. The performance of the vibrating screen was evaluated using screening efficiency. Furthermore, prediction using a feed backward artificial neural network (ANN) model was developed on the experimental results for ten different neuron conditions. From the results, it was clear that the prediction results obtained from the ANN model were in good correlation with the experimental results.