A prediction algorithm for pulp concentration using norm-optimized extreme learning machine

Pulp concentration as one of the most important production parameters plays an important role in the ore production.Generally,the production efficiency can be improved by a prediction for pulp concentration.Since there are some nonlinear relationships between the pulp concentration and other production parameters,it imposes very challenging obstacles to address this issue of prediction.A novel prediction method is proposed in this paper through the use of extreme learning machine (ELM) that is an effective learning algorithm developed for neural network.Firstly,the pulp concentration data is preprocessed by the phase space reconstruction method,and the time series prediction model is adjusted from one dimension to multiple dimensions.Secondly,an improved ELM algorithm using L2 norm (ELM-L2) is developed to implement the prediction.The experiments are conducted with a real-world production data set from a mine.Compared with the traditional prediction method using neural network,the proposed approach can reduce the training time by 30% and improve the prediction accuracy by 48% for a large-scale data set.The experimental results show the effectiveness of the proposed algorithm.