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Gold Science and Technology >

2020 , Vol. 28 >Issue 1: 82 - 89

DOI: https://doi.org/10.11872/j.issn.1005-2518.2020.01.076

Mining Technology and Mine Management

Based on Particle Swarm Algorithm to Optimize the BP Neural Network of Leaching Rate Prediction in Leaching Mining

  • Jinge BU ,
  • Jianhong CHEN
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  • School of Resources and Safety Engineering，Central South University，Changsha 410083，Hunan，China

Received date: 2019-06-11

  Revised date: 2019-09-30

  Online published: 2020-02-26

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Highlights

With the development of mining technology，the development of mineral resources in China is progressing steadily.Nowadays，the mining trend is green mining mode with environmental protection and high safety.However，many mining methods are faced with serious pollution and low recovery rate.Leaching mining is a kind of mining method which combines mining，sorting and hydrometallurgy.In order to explore how to improve the leaching rate during the leaching process,in this paper,the leaching process of antimonial sulfide ore was taken as an example to analysis the main factors that influence the leaching rate.The BP neural network prediction model was established and optimized by the particle swarm algorithm,so it can conduct the variable training with existing sample data.Finally, the BP neural network model, the PSO - BP neural network model were used to predict leaching rate, respectively，and compared to two kinds of model error precision of the predicted values and actual values.The research results show that the impact containing antimony sulfide ore leaching rate of interaction between these factors and nonlinear relationship and leaching rate is more by 40 groups will affect the relationship between parameters and the leaching rate of leaching rate of data through the neural network training model to predict the 8 groups of leaching rate data，compare the leaching rate of output value and the actual values can be found using the particle swarm algorithm to optimize BP neural network model training accuracy is higher，the more accurate predictions for leaching rate，the prediction data set of normalized linear curve slope is more close to 1.Through further error analysis，it can be seen that compared with BP neural network，the relative errors and variances of the predicted results of the model optimized by particle swarm optimization algorithm and the actual values are significantly reduced.Therefore，this prediction model has certain reference value for the optimization of leaching rate in the current leaching mining area.

Cite this article

Jinge BU , Jianhong CHEN . Based on Particle Swarm Algorithm to Optimize the BP Neural Network of Leaching Rate Prediction in Leaching Mining[J]. Gold Science and Technology, 2020 , 28(1) : 82 -89 . DOI: 10.11872/j.issn.1005-2518.2020.01.076

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