Work

An LSTM Approach for Quality Prediction in a Mining Process Using Ensemble Data Interpolation

Year: 2026

Type: article

Abstract: The presence of silica in iron ore concentrate can have significant negative impacts on the efficiency and quality of steel production. As such, providing engineers with early and reliable information about the purity of iron ore concentrate is crucial for smooth mining operations. This paper reports on the development of a long short-term memory (LSTM) network and an ensemble data interpolation technique to enhance quality prediction in the froth flotation process of an iron ore mine. Our results demonstrate the ability of our model to accurately predict the silica content of iron ore concentrate on a minute-by-minute basis, as well as the ability to forecast hours in advance.

Source: Applied Sciences

DOI: 10.3390/app16073419

Authors: Jorge Ortega-Moody, Tyler Ward, Kouroush Jenab, Cesar Isaza, Alireza Ahmadi, Ghazal Barari