MACHINE LEARNING PROJECT

'Machine learning algorithms for mineral processing' - a collaborative project between SMI-JKMRC and MIDAS Tech International. Traditional mineral processing modelling have been based on well-defined parameterised models. These models are generally physics-based models or regression models. The Machine learning approach is different and requires the models to be largely constructed from the data itself. The basic idea is that data is surveyed from a plant, and the data themselves are used to identify process improvement opportunities. The focus of the project is to compare, and validate the particular machine-learning algorithms with particular emphasis on probability-based models... The main advocate for machine-learning methods for mineral processing is Dr Stephen Gay. Stephen originally developed ML algorithms as a Physical Oceanographer at the Australian Institute of Marine Science, and later in mineral processing.