Prediction of Energy Efficiency of Main Transportation System Used in Underground Coal Mines – A Statistical Approach
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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Nature
Abstract
Transport in underground mines i.e. belt conveyor is used for carrying extracted materials from one station to other. Transportation involves energy as its main consumer. An efficient energy system adapted for transporting extracted materials can minimize energy losses, hence resulting in reduced cost of energy. Energy to transportation is provided by means of an electric motor, the efficiency of the electric motor depend on load carried by the system, the length and height to which the material has to be delivered. The present study was carried on the energy efficiency of three different transportation systems in GDK-1&3 incline underground mine, The Singareni Collieries Company Limited, Ramagundam. The present study was carried out considering two cases with first, load varying from 20% to 100% keeping conveyor speed constant. Secondly, with 20% fixed loading and varying the conveyor speed from 1 m/s to 2 m/s. Estimation of the energy efficiency for a unique electric motor was estimated considering both the cases which involved three different lengths and heights. It was observed that with a constant conveyor speed of 2 m/s and filling rate varying from 27.775 kg/m to 5.555 kg/m, the amount of increase in efficiency was found to be 23.92%, 18.75% and 5.25% for Gantry, 5L and Surface conveyors respectively. Also with a constant filling rate of 5.555 kg/m and conveyor speed varying from 1 m/s to 2 m/s, the amount of decrease in efficiency was found to be 13.63%, 11.52% and 1.64% for Gantry, 5L and Surface conveyors respectively. Further a prediction study was carried on the energy efficiency based on the input parameters load, length and height. The model gives an R2 value 87% which is significant. © 2020, Springer Nature Switzerland AG.
Description
Keywords
Belt conveyor, Energy efficiency, Transportation, Underground coal mines
Citation
Learning and Analytics in Intelligent Systems, 2020, Vol.2, , p. 337-344
