Hybrid features extraction for the online mineral grades determination in the flotation froth using Deep Learning

• Published in: Engineering applications of artificial intelligence Vol. 129; p. 107680
• Main Authors: Bendaouia, Ahmed, Abdelwahed, El Hassan, Qassimi, Sara, Boussetta, Abdelmalek, Benzakour, Intissar, Benhayoun, Abderrahmane, Amar, Oumkeltoum, Bourzeix, François, Baïna, Karim, Cherkaoui, Mouhamed, Hasidi, Oussama
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2024
• Subjects: Computer vision; Deep learning; Features extraction; Flotation; Industry 4.0; Machine learning; Mining industry; Deep learning; Computer vision; Flotation; 0000; 1111; Machine learning; Industry 4.0; Features extraction; Mining industry
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2023.107680

The control of the froth flotation process in the mineral industry is a challenging task due to its multiple impacting parameters. Accurate and convenient examination of the concentrate grade is a crucial step in realizing effective and real-time control of the flotation process. The goal of this study is to employ image processing techniques and CNN-based features extraction combined with machine learning and deep learning to predict the elemental composition of minerals in the flotation froth. A real world dataset has been collected and preprocessed from a differential flotation circuit at the industrial flotation site based in Guemassa, Morocco. Using image-processing algorithms, the extracted features from the flotation froth include: the texture, the bubble size, the velocity and the color distribution. To predict the mineral concentrate grades, our study includes several supervised machine learning algorithms (ML), artificial neural networks (ANN) and convolutional neural networks (CNN). The industrial experimental evaluations revealed relevant performances with an accuracy up to 0.94. Furthermore, our proposed Hybrid method was evaluated in a real flotation process for the Zn, Pb, Fe and Cu concentrate grades, with an error of precision lesser than 4.53. These results demonstrate the significant potential of our proposed online analyzer as an artificial intelligence application in the field of complex polymetallic flotation circuits (Pb, Fe, Cu, Zn). •The applications of artificial intelligence and computer vision in froth flotation are reviewed.•Various features extraction approaches applied on images of the flotation froth.•Data were collected from a differential flotation circuit of polymetallic ore.•Prediction models for Zn, Pb, Fe, and Cu grades in the flotation were established.•The performance was evaluated in a real world flotation process.•Experimental evaluations yielded an error of less than 4.53% in the industrial application test.