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MODELING OF WET GRINDING OPERATION USING ARTIFICIAL INTELLIGENCE BASED TECHNIQUES

Abstract The Artificial Intelligence (AI) based modeling techniques applied to the industrial grinding operation of a lead-zinc ore-beneficiation plant to predict the key performance indicators (KPIs) for the circuit. As system identification of the non-linear process is a must in advanced control, AI based techniques are applied to predict the KPIs within some acceptable limits. The nonparametric model for these KPIs is constructed using Feed-Forward Neural Networks (FNN), and wavelet-frames. A well-validated hybrid-model, using physico-empirical methodologies, is used to approximate the actual behaviour of the plant. Merits and demerits of each of these techniques are presented.