Prediction of roof falls and induced caving in continuous miner panel using Machine Learning
Abstract
Continuous Miners are deployed in underground mining for extraction of coal by caving method. The safety conditions require regular caving in the goaf to release the stress in the roof. These falls depend upon various geo-mining and specific characteristics in the area under extraction. Prediction of roof cavability is conducted by emprerical calculations and also numerical modeling. The cavability is assesed by monitoring of convergence in the front abutment zone. A threshold limit of 5mm convergence is considered for cavabilty in the goaf. In some cases, the roof fall does not occur beyond the threshold limit and requires induced caving of the roof in the goaf. In this paper 336 data sets of roof falls in five continuous miner panels were analyzed by logistic regression and machine learning algorithms, to predict the need for induced caving or not. The comparison of the field data in 336 sets, with the logistic regression was found to be about 74%. The variation is because of the varying depths and dimensions of the five panels in the mine under study. It is concluded that the logistic regression and machine learning algorithms of prediction is a useful tool for the decision of induced caving in a continuous miner panel based on the sufficient field data of 336 sets.
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