A nonlinear goal-programming-based DE and ANN approach to grade optimization in iron mining

This study proposes a combined ‘nonlinear goal-programming’-based ‘differential evolution’ (DE) and ‘artificial neural networks’ (ANN) methodology for grade optimization in iron mining production processes. The nonlinear goal-programming model has decision variables of ‘cutoff grade,’ ‘dressing grade’ and ‘concentrate grade,’ with the goals being ‘concentrate output,’ ‘resource utilization rate’ and ‘economic benefit (profit).’ The model, which contains three unknown functions, the ‘loss rate,’ the ‘ore-dressing metal recovery rate’ and the ‘total cost,’ is subsequently converted into an unconstrained optimization problem, to be solved by our integrated DE–ANN approach. DE is used to search for the optimum combination of the cutoff, dressing and concentrate grades, with the crossover rate in the DE analysis being dynamically adjusted within the evolutionary process. The loss rate is calculated by a regression model, whilst the ore-dressing metal recovery rate and the total cost functions are, respectively, calculated using ‘back-propagation’ and ‘radial basis function’ neural networks. We subsequently go on to analyze a case study of the Daye iron mine in China to demonstrate the reliability and efficiency of our proposed approach. Our study provides a novel approach for decision makers to guide production and management in iron mining.