基于LM-BP神经网络的浮选药剂流量预测模型研究

Research on Flotation Reagent Flow Prediction Model Based on LM-BP Neural Network

结合东鞍山选矿厂浮选流程的实际工况,采集现场浮选流程的关键过程变量、工艺指标,提出了基于LM-BP神经网络的浮选药剂流量预测模型。数据交叉验证的结果表明,该方法能够在保证精矿品位、回收率等指标满足生产要求的前提下,合理预测浮选药剂制度,使浮选矿浆达到最佳矿化状态,进而优化浮选各项指标,对于降低选厂浮选流程的生产成本有一定的参考价值。

Combining with the actual working conditions of flotation process in concentrator,the key process variables and process indexes of flotation process in site were collected for a long time,and a prediction model of flotation reagent flow based on LM-BP neural network was put forward. The results of data cross-validation show that this method can predict the flo?tation reagent scheme reasonably make the flotation pulp reach the optimum mineralization state,and then optimize the flota?tion indicators on the premise that the concentrate grade,recovery and other indicators meet the production requirements. It has a certain reference value for reducing the production cost of flotation process in the plants.