Please use this identifier to cite or link to this item:
Title: Artificial neural network modeling for predicting the screening efficiency of coal with varying moisture content in the vibrating screen
Authors: Shanmugam B.K.
Vardhan H.
Raj M.G.
Kaza M.
Sah R.
Hanumanthappa H.
Issue Date: 2021
Citation: International Journal of Coal Preparation and Utilization , Vol. , , p. -
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. © 2021 Taylor & Francis Group, LLC.
Appears in Collections:1. Journal Articles

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.