TY - GEN
T1 - Predicting mine dam levels and energy consumption using artificial intelligence methods
AU - Hasan, Ali N.
AU - Twala, Bhekisipho
AU - Marwala, Tshilidzi
PY - 2013
Y1 - 2013
N2 - Four machine learning algorithms (artificial neural networks, a naive Bayes' classifier, a support vector machines and decision trees) were applied for a single pump station mine to monitor and predict the dam levels and energy consumption. This work was undertaken to investigate the feasibility of using artificial intelligence in certain aspects of the mining industry. If successful, artificial intelligence systems could lead to improved safety and reduced electrical energy consumption. The results show neural networks to be more efficient when compared with support vector machines, a naive Bayes' classifier and in particular, decision trees in terms of predicting underground dam levels. Artificial neural networks showed 60% accuracy, out-performing support vector machine, naive Bayes' classifier and decision trees. For the prediction of water pump energy consumption, an artificial neural network and a naive Bayes' classifier had the same accuracy of 99.0%, whereas a support vector machine and decision trees achieved a lower accuracy.
AB - Four machine learning algorithms (artificial neural networks, a naive Bayes' classifier, a support vector machines and decision trees) were applied for a single pump station mine to monitor and predict the dam levels and energy consumption. This work was undertaken to investigate the feasibility of using artificial intelligence in certain aspects of the mining industry. If successful, artificial intelligence systems could lead to improved safety and reduced electrical energy consumption. The results show neural networks to be more efficient when compared with support vector machines, a naive Bayes' classifier and in particular, decision trees in terms of predicting underground dam levels. Artificial neural networks showed 60% accuracy, out-performing support vector machine, naive Bayes' classifier and decision trees. For the prediction of water pump energy consumption, an artificial neural network and a naive Bayes' classifier had the same accuracy of 99.0%, whereas a support vector machine and decision trees achieved a lower accuracy.
KW - de-watering system
KW - deep gold mines
KW - energy consumption
KW - machine learning algorithms
KW - underground pump stations
UR - http://www.scopus.com/inward/record.url?scp=84886484707&partnerID=8YFLogxK
U2 - 10.1109/CIES.2013.6611745
DO - 10.1109/CIES.2013.6611745
M3 - Conference contribution
AN - SCOPUS:84886484707
SN - 9781467358514
T3 - Proceedings of the 2013 IEEE Symposium on Computational Intelligence for Engineering Solutions, CIES 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
SP - 171
EP - 175
BT - Proceedings of the 2013 IEEE Symposium on Computational Intelligence for Engineering Solutions, CIES 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
T2 - 2013 IEEE Symposium on Computational Intelligence for Engineering Solutions, CIES 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
Y2 - 16 April 2013 through 19 April 2013
ER -