Abstract: A hybrid intelligent prediction model combining case-based reasoning(CBR) with adaptive particle swarm optimization(PSO) was proposed for the cobalt concentration prediction of purification process in zinc hydrometallurgy.Owing to the different effect of the case in different periods, a combined weighted similarity functions was presented.Consideration of the retrieval accuracy of CBR influenced by the feature weighting vector selection and the optimal number of nearest neighbors, an adaptive PSO algorithm was proposed to optimize these parameters. The experimental verification and comparison analysis were executed using the industrial production data from purification process. The results show that the accuracy of the hybrid intelligent model is higher than the BP neural network model and the prediction results can be used as process data for the operation optimization of the purification process.

Key words: Purification process,Cobalt concentration prediction,Case-based reasoning,Adaptive PSO