Applying multiobjective RBFNNs optimization and feature selection to a mineral reduction problem

The Nickel reduction process is a complex task where many dynamic optimization problems arises that, nowadays, requires a human operator to take decisions based on his experience and intuition. In order to help the operator to optimize the reduction process in terms of maximum amount of mineral extracted and minimum energy consumption, a control system integrated by several modules is being designed. One of the modules has the task of predicting how much petroleum will be burned in the ovens where the raw material is processed. This paper proposes an algorithm to design Radial Basis Function Neural Networks that will be able to predict accurately the amount of petroleum given a set of input parameters. The algorithm is also able of identifying the most relevant input parameters for the network so the dimensionality reduction problem is ameliorated. Hence, this paper, as it will be shown in the experiments section is able to apply the synergy of different Soft Computing techniques to the industrial process obtaining satisfactory results.