Toward Automatic Monitoring for Anomaly Detection in Open-Pit Phosphate Mines Using Artificial Vision: A Case Study of the Screening Unit

Abstract

Phosphorus is a limited resource that is non-replaceable worldwide. Its significant role as a fertilizer underlines the necessity for prudent and strategic management. The adequate monitoring of the phosphate extraction process mitigates anything that can influence the quantity or quality of the product. The phosphate extraction process’s most important phase is the screening unit, which can be used to separate phosphate minerals from unwanted materials. Nevertheless, it encounters several anomalies and malfunctions that influence the performance of the whole chain. This unit requires continuous automated control to avoid any blockages or risks caused by malfunctions. Using artificial intelligence and image processing techniques, the main goal of the investigations described in this paper was to evaluate the performances of machine-learning and deep-learning models to detect the screening unit malfunction in the open pit of the phosphate mine in Benguerir-Morocco. These findings highlight that the CNN and HOG-based models are the most suitable and accurate for the given case study.