A comparative study of regression model and the adaptive neuro-fuzzy conjecture systems for predicting energy consumption for jaw crusher

Abstract

Crushing is a vital process for different industrial applications where a significant portion of power is consumed to properly blast rocks into a predefined size of fragmented rock. An accurate prediction of the energy needed to control this process rarely exists in the literature, hence there have been limited efforts to optimize the power consumption at the crushing stage by a jaw crusher; which is the most widely used type of crusher. The existence of accurate power prediction as well as optimizing the steps for primary crushing will offer vital tools in selecting a suitable crusher for a specific application. In this work, the specific power consumption of a jaw crusher is predicted with the help of the adaptive neuro-fuzzy interference system (ANFIS). The investigation included, aside from the power required for rock comminution, an optimization of the crushing process to reduce this estimated power. Results revealed the success of the model to accurately predict comminution power with an accuracy of more than 96% in comparison with the corresponding real data. The obtained results introduce good knowledge that may be used in future academic and industrial research.