提出了一种钴湿法冶炼萃取组分含量混合建模方法.该模型由基于物料衡算关系的动态机理模型与基于萃取平衡实验数据的RBF神经网络模型组成.机理模型作为描述过程动态行为的整体框架,RBF神经网络用来辨识机理模型中的未知函数关系.在上述混合模型的基础上,还提出了一种模型校正策略,进一步提高了模型的精确性.将所建立的混合模型应用于实际湿法冶炼生产过程中,结果表明该方法具有良好的估计性能. A hybrid modeling method for the cobalt content in the hydrometallurgy smelting process is proposed.The model consists of a dynamic mechanism model based on the material balance relationship and a RBF neural network model based on the experimental data of the extraction equilibrium.The mechanism model is used to describe the dynamic behavior of the process The whole framework and RBF neural network are used to identify the unknown function in the mechanism model.On the basis of the above hybrid model, a model correction strategy is proposed to further improve the accuracy of the model.The hybrid model is applied to The actual wet-process smelting production process, the results show that the method has good estimation performance.