浮选加药量预测模型的研究

Study of flotation reagent dosage prediction model

为解决浮选加药过程中存在的人为主观因素大,浮选产品灰分易波动,浮选药剂耗损量大等问题,分别建立了基于BP神经网络和GRNN神经网络的两种加药量预测模型,并设计硬件系统,对两种模型预测的捕收剂添加量和起泡剂添加量进行了试验验证。结果表明,基于BP神经网络建立的浮选加药量预测模型预测效果较好,更适用于浮选生产过程中的药剂添加量预测。

Heavy involvement of artificial subjective factors in dosing agents in flotation process generally leads to fluctuation of ash of flotation products and high consumption of agents.To address this issue,two agent dosage prediction models based respectively on BP neural network and GRNN neural network are developed and the hardware system is designed.As revealed through verification test on the dosing amounts of collector and frother predicted by the two models,the BF neural network-based model can offer a relatively better prediction result and is more suitable for use in industrial flotation process.