Application of MR and ANN in the prediction of the shovel cycle time, thereby improving the performance of the shovel-dumper operation - A case study

Abstract

Loading and hauling of ore and waste are the key operations of an opencast coal mine and entail a high operational cost. The productivity of a mine can be increased by reducing the cycle time of loading equipment as well as utilizing dumpers optimally. In this paper we discuss the impact of rock type, bucket fill factor, rock fragmentation, the height of the cut, and angle of swing of the bucket on shovel performance. A time study is conducted on shovels in an opencast coal mine with experimental blasts of rocks to assess the impact of different factors on the performance of the shovel. Based on the data, the authors applied multiple regression (MR) and artificial neural network (ANN) techniques to develop different models for the prediction of the shovel cycle time. Developed models are validated by comparing the predicted data with actual field data. With the help of the best model, the plausible fleet size is determined in order to utilize the shovel and dumper optimally and to improve the performance of shovel-dumper operation.