Research on a method for predicting the underflow concentration of a thickener based on the hybrid model

Abstract

As a method for extracting metals and their compounds, hydrometallurgy has the advantages of high comprehensive metal recovery rate, low environmental pollution, and easier production process. The intensive washing process is a key process in the hydrometallurgical process, and the underflow concentration is a key indicator for measuring the quality of the concentrated washing process. In this paper, after analyzing the characteristics of the thick washing process, the hybrid model combining mechanism modeling and error compensation model based on EDO-TELM (three hidden layers Extreme Learning Machine optimized with Entire Distribution Optimization algorithm) is used to achieve accurate measurement of the underflow concentration in the dense washing process. The hybrid model uses the improved EDO-TELM algorithm as an error compensation model to compensate the error of the un-modeled part of the mechanism model, and gives a reasonable estimate of the uncertain part of the model, which theoretically reduce the prediction error of the model. The Matlab simulation results show that the prediction error of the hybrid model is significantly lower than that of the mechanism model and the data model, and can be adapted to the measurement needs of the industrial site.