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.

湿法冶金是一种提取金属及其化合物的方法，具有金属综合回收率高，环境污染小，生产工艺容易的优点。强化洗涤过程是湿法冶金过程中的关键过程，下溢浓度是衡量浓缩洗涤过程质量的关键指标。本文在分析了浓洗过程的特点后，采用结合了机械建模和误差补偿模型的混合模型，该模型基于EDO-TELM（通过整体分布优化算法优化的三层隐层极限学习机）来实现对模型的精确测量。浓洗过程中的底流浓度。混合模型使用改进的EDO-TELM算法作为误差补偿模型来补偿机械模型未建模部分的误差，并对模型的不确定部分进行合理估计，从理论上减少了预测误差。该模型。Matlab仿真结果表明，混合模型的预测误差明显低于机械模型和数据模型的预测误差，可以适应工业现场的测量需求。