An LSTM Approach for Quality Prediction in a Mining Process Using Ensemble Data Interpolation
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.
Fields of Study & Subjects
Mineral Processing and Grinding
Minerals Flotation and Separation Techniques
Tailings Management and Properties
Publication details
- Journal / source
- Applied Sciences
- Publisher
- MDPI AG
- Volume
- 16
- Issue
- 7
- Pages
- 3419–3419
- Publication date
- 1 April 2026
- Language
- EN