Endpoint prediction model of basic oxygen furnace steelmaking based on PSO-ICA and RBF neural network

A radial basis function neural network model combined with particle swarm optimization algorithm and independent component analysis is proposed, which is used to predict the endpoint of BOF steelmaking. In order to solve the issues that the objective function falls into the local optimum and the sequence of independent components is uncertain, this paper utilizes the global ergodicity of particle swarm optimization algorithm and the local optimizing capacity of fast fixed-point algorithm to improve the traditional independent component analysis algorithm, as well as the redundant information is compressed and the input dimension is reduced. The extracted independent features are introduced into the radial basic function neural network to predict the endpoint temperature and carbon content. Simulations are made with the practical data of basic Oxygen furnace production, the result proves the proposed model can improve the accuracy and reassure the reliability of prediction.