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针对湿法炼锌净化过程中钴离子浓度难以准确检测的问题,提出一种基于智能融合策略的钴离子浓度组合预测模型.首先从提高模型预测精度的角度出发,考虑不同核函数对预测性能的影响,分别建立两个在线支持向量回归子模型,并采用改进粒子群优化算法进行子模型参数寻优;然后通过熵值法智能融合策略建立组合预测模型.仿真实验表明,组合模型具有良好的预测性能,预测效果能满足硫酸锌溶液净化过程中对钴离子浓度值的误差要求.
Aiming at the problem that it is difficult to accurately detect the concentration of cobalt ions in the purification process of zinc hydrometallurgy, a combination prediction model of cobalt ion concentration based on intelligent fusion strategy is proposed.First, from the perspective of improving the prediction accuracy of the model, , Two online support vector regression sub-models are established respectively, and the improved particle swarm optimization algorithm is used to optimize the sub-model parameters. Then, a combination forecasting model is established by entropy intelligent fusion strategy. The simulation results show that the combined model has a good prediction The performance and prediction effect can meet the error requirement of cobalt ion concentration in the purification process of zinc sulfate solution.