Predicting energy consumption of grinding mills in mining industry : A review

Mines are a complex and challenging industry that needs to consume more energy for the production of minerals. In order to enhance the energy efficiency of the mining industry, artificial intelligence technology is being integrated to manage, predict, and optimize the energy consumption of mining equipment. The energy-intensive equipment in the mining industry is the grinding mills. Due to the complexity and difficulty of modeling the grinding mills, data-driven modeling solves these challenges by predicting and optimizing energy consumption through the development of machine learning models. In this article, a literature review on the application of artificial intelligence models to predict the energy consumption of grinding mills has been conducted. This research study presents a description of the energy prediction system, from the data acquisition to the prediction results. It provides a comparison description of the parameters of available datasets and a classification of their machine learning and deep learning models applied to predict energy consumption or power of mining grinding mills. Furthermore, it identifies the performance parameters of each research study. Then, the research study will be concluded with a recommendation and the main area for future research.