基于PLS-LSSVM方法的湿法炼锌过程预测建模

Forecasting modeling of zinc hydrometallurgy process based on PLS-LSSVM approach

在湿法炼锌过程中,沉铁工序具有流程长、化学反应耦合强和物理沉降过程复杂的特点。针对这一特点,提出基于PLS-LSSVM的预测建模方法。PLS能有效处理复杂冶金生产过程中的非线性、多输入和数据相关性等特性。同时,针对建模过程中LS-SVM两个重要参数的优化选择,提出免疫文化差分进化算法。仿真结果表明,本文提出的基于PLS-LSSVM的预测模型能取得较好的预测结果,为解决沉铁过程铁渣品位实时检测提供了一种有效可行的方法。

In Zinc hydrometallurgy process,the procedure of iron precipitation is a long flow,strong chemical reaction coupling and complex physical deposition process.Aiming at this characteristic,a forecasting modeling method based on PLS-LSSVM approach is put forward.The PLS can effectively handle the nonlinearity,multiple inputs and data correlation in complex metallurgical production process.Meanwhile,in order to optimize two important parameters of LS-SVM in the modeling process,immune cultural differential evolution algorithm（ICDEA） is proposed.Simulation results show that the proposed PLS-LSSVM method has achieved good model prediction result,and also can provide an effective and feasible method for iron residue grade real-time detection in the process of iron precipitation.