Digital Transformation of the Flotation Monitoring Towards an Online Analyzer

• Published in: Smart Applications and Data Analysis pp. 325 - 338
• Main Authors: Bendaouia, Ahmed, Abdelwahed, El Hassan, Qassimi, Sara, Boussetta, Abdelmalek, Benhayoun, Abderrahmane, Benzakour, Intissar, Amar, Oumkeltoum, Zennayi, Yahia, Bourzeix, François, Baïna, Karim, Baïna, Salah, Khalil, Abdessamad, Cherkaoui, Mouhamed, Hasidi, Oussama
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Communications in Computer and Information Science
• Subjects: Deep learning; Image processing; Industry 4.0; Machine vision; Mining industry
• ISBN: 9783031204890; 3031204891
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-3-031-20490-6_26

Accurate and timely investigation to concentrate grade in mining industry is a premise of realizing real time and efficient control in a froth flotation process. This study seeks to use image processing and artificial intelligence technologies to predict the elemental composition of minerals in the flotation froth. The online analyzer is a flotation soft sensor solution that predicts the concentrate grade content of the flotation using froth images and physio-chemical parameters. A froth image dataset from the lead flotation circuit was collected and prepossessed. Frame selection and data augmentation was used for this dataset. Feature extraction includes texture and color distribution using image processing algorithms. Then, several state-of-the-art machine learning algorithms (Linear regression, Random forest, Decision tree, GR Boost) are trained to predict the concentrate grades of minerals. A Convolutional neural network architecture is used on the image dataset to predict the Lead Pb concentrate grade which indicates that the deep learning has a good industrial performance. The promising results of this study demonstrate the significant potential of machine vision and deep learning neural networks in froth image analysis, which is of great importance for development of the mining industry.