Building Diagnostic Models in the Processing Industries with Machine Learning


Machine learning models are often seen as black box systems that can capture complex nonlinear relationships between variables, but that are difficult to interpret otherwise. However, this is not necessarily the case, and in this presentation, the use of random forests and neural networks to build diagnostic models in the process industries will be reviewed. Both these modelling methodologies are highly versatile tools and their implementation in MATLAB will be demonstrated based on several case studies in the mining and mineral processing industries.

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About the Presenter

Professor Chris Aldrich holds a Chair in Process Systems Engineering and is Deputy Head of the WA School of Mines. He is also an Extraordinary Professor at Stellenbosch University in South Africa and an Honorary Professor at The University of Queensland. He was previously the Founding Director of the Anglo American Platinum Centre for Process Monitoring at Stellenbosch University.
He holds a PhD and a higher doctoral degree from Stellenbosch University, is a Fellow of the South African Academy of Engineering, a Fellow of the Australasian Institute of Mining and Metallurgy and serves on the editorial boards of the International Journal of Mining, Minerals and Metals, Minerals Engineering, Water SA and Control Engineering Practice. His research interests include artificial intelligence, machine learning, mineral processing and extractive metallurgy.

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