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金属矿山 ›› 2019, Vol. 48 ›› Issue (02): 200-204.

• 机电与自动化 • 上一篇    

基于LM-BP神经网络的浮选药剂流量预测模型研究

唐学飞1,杨光1,高鹏2,3,张臣一2,3   

  1. 1. 鞍山钢铁集团公司 东鞍山烧结厂,辽宁 鞍山 114041;2. 东北大学资源与土木工程学院,辽宁 沈阳 110819;3. 难采选铁矿资源高效开发利用技术国家地方联合工程研究中心,辽宁 沈阳 110819
  • 出版日期:2019-02-25 发布日期:2019-04-08
  • 基金资助:

    基金项目:“十二五”国家科技支撑计划项目(编号:2015BAB15B02)。

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

Tang Xuefei1, Yang Guang1, Gao Peng2,3, Zhang Chenyi2,3   

  1. 1. Donganshan sintering Plant, Anshan Steel Group Corporation, Anshan 114041,China;2. School of Resources and Civil Engineering, Northeastern University, Shenyang 110819,China;3. National-Local Joint Engineering Research Center of Refractory Iron Ore Resources Efficient Utilization Technology,Shenyang 110819, China)
  • Online:2019-02-25 Published:2019-04-08

摘要: 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。

关键词: LM-BP神经网络, 浮选药剂流量预测模型, 药剂制度

Abstract: 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 flotation reagent scheme reasonably make the flotation pulp reach the optimum mineralization state, and then optimize the flotation 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.

Key words: LM-BP neural network, Flotation reagent flow prediction model, Flotation reagent scheme