Prediction of component content in rare earth extraction process based on ESNs-Adaboost

Abstract

Aiming at the problem that the component content of elements in REEP (rare earth extraction process) is difficult to detect, a modeling method for component content in REEP based on ESNs-Adaboost is proposed. It uses ESN (echo state network) with fast training speed and high stability to establish multiple identification model of REEP. And then combined with the improved Adaboost algorithm, multiple models are integrated into a final ESNs-Adaboost model of component content of REEP according to the combination rule. The improved Adaboost algorithm has an advantage that its threshold can be adjusted adaptively with the training error. By collecting data from CePr/Nd extraction process, the simulation results show that the proposed method has high prediction accuracy, strong generalization ability, good robustness and better performance than single ESN model, which can meet the requirements of rapid detection of component content in extraction field.