ASSESSMENT OF INSTALLED POWER FOR INCLINED BELT CONVEYORS USING GENETIC ALGORITHM AND ARTIFICIAL NEURAL NETWORKS

Abstract

In this study, the installed power (Pinst, kW) of several inclined belt conveyors operating in the mining industry of Turkey was investigated through two soft computing algorithms (i.e., genetic expression programming (GEP) and artificial neural networks (ANN)). For this purpose, the most crucial belt (i.e., belt length (L), belt width (W), belt inclination (α)), operational (i.e., belt speed (Vb) and throughput (Q)) and infrastructural (belt weight (Wb) and idler weight (Wid)) features of 42 belt conveyors were collected for each investigated belt conveyor. The collected data was transformed into a comprehensive dataset for soft computing analyses. Based on the GEP and ANN analyses, two robust predictive models were proposed to estimate the Pinst. The performance of the proposed models was evaluated using several statistical indicators, and the statistical evaluations demonstrated that the models yielded a correlation of determination (R2) greater than 0.95. Nevertheless, the ANN-based model has slightly overperformed in predicting the Pinst values. In conclusion, the proposed models can be reliably used to estimate the Pinst for the investigated conveyor belts. In addition, the mathematical expressions of the proposed models were given in the present study to let users implement them more efficiently.