Production indices prediction model of ore dressing process based on PCA-GA-BP neural network

  • Authors:

  • Yefeng Liu;Gang Yu;Binglin Zheng;Tianyou Chai

  • Affiliations:

  • Key Laboratory of Process Industry Automation, Ministry of Education, Northeastern University, Shenyang, China;Key Laboratory of Process Industry Automation, Ministry of Education, Northeastern University, Shenyang, China;Key Laboratory of Process Industry Automation, Ministry of Education, Northeastern University, Shenyang, China;Key Laboratory of Process Industry Automation, Ministry of Education, Northeastern University, Shenyang, China and Research Center of Automation, Northeastern University, Shenyang, China

  • Venue:
  • CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
  • Year:
  • 2009

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Abstract

In order to determine the global production indices' real-time completion situation after plan's layer upon layer's decomposition and transmition to working procedure and work team. A neural network model based on PCA-GA-BP was proposed to reasonable modify the production plan. The principle component analysis (PCA) was used to select the most relevant process features and to eliminate the correlations of the input variables; back-propagation (BP) neural network was used to characterize the nonlinearity and accuracy; genetic algorithm (GA) was employed to optimize the parameters and structure of the BP neural network by improving GA' fitness function. Carried on prediction to weak magnetic concentrate taste and weak magnetic tailings taste according to actual production data. The Simulation results show that the proposed method provides promising prediction reliability and accuracy.