An Automated Machine learning (AutoML) approach to regression models in minerals processing with case studies of developing industrial comminution and flotation models. (November 2022)

• Record Type: Journal Article
• Authors: Koh, Edwin J.Y.; Amini, Eiman; Gaur, Shruti; Becerra Maquieira, Miguel; Jara Heck, Christian; McLachlan, Geoffrey J.; Beaton, Nick
• Is Part Of: Minerals engineering. Volume 189(2022)
• Publication Date: 2022-11
• Subjects: Automated Machine Learning -- Simulation and Modelling -- Comminution -- Flotation -- Neural Networks; Mines and mineral resources -- Periodicals; Ressources minérales -- Périodiques; Mines and mineral resources; Periodicals; Electronic journals; 622
• DOI: 10.1016/j.mineng.2022.107886
• Languages: English
• ISSNs: 0892-6875

Abstract

Highlights: Automated Machine Learning pipeline develops predictive models from plant data within hours, Steps include filtering, temporal resolution, feature selection, and neural network architecture optimisation, Pipeline does not require expert Machine Learning knowledge for model development, Three case studies with minimal errors of < 3 % for valuables and < 7 % for by-products, Developed AutoML models always better than Partial Least Squares models. Abstract: Deep learning (DL), a subset of machine learning (ML) has been a popular research interest after obtaining remarkable achievements on various tasks like image classification, object detection, language processing, and artificial intelligence. However, the successes of these algorithms were highly dependent on human expertise for hyperparameter optimisation and data preparation. As a result, widespread application of DL systems in minerals processing is still absent despite the increasing ability to collect data from process information (PI) and assay data. Automated Machine Learning (AutoML) is an emerging area of research which aims to automate the development of ready-to-use end-to-end ML models with little to no user ML knowledge. However, existing commercially available AutoML algorithms are not well designed for minerals processing data. In this study, we develop an AutoML algorithm to develop steady-state minerals processing models suitable for mine scheduling and process optimisation. The algorithm consists of data filtering, temporal resolution alignment, feature selection, neural network architecture search, and development. The AutoML algorithm was tested on three case studies of different processes and ore types. These case studies cover the range of difficulties of possible datasets encountered in the mining and processing industry from clean simulated data to noisy data with poor correlation. The algorithm successfully developed neural network models within hours from hourly raw PI and/or daily assay data with no human intervention. These models derived from process data have minimal errors as low as < 3 % for major valuables like Ni and Cu, 6–7 % for by-products like Au, 8–10 % for deleterious minerals like MgO, and 5–8 % for gangue. The algorithm was also designed so that expert minerals processing knowledge can influence the pipeline to improve the quality of models. As a result, the AutoML algorithm becomes a powerful tool for mining and mineral processing experts to apply their domain knowledge of the process to develop models of equipment or processing circuits.