Journal of Central South University

, Volume 18, Issue 5, pp 1441–1447 | Cite as

Optimization of processing parameters for microwave drying of selenium-rich slag using incremental improved back-propagation neural network and response surface methodology

  • Ying-wei Li (李英伟)
  • Jin-hui Peng (彭金辉)Email author
  • Gui-an Liang (梁贵安)
  • Wei Li (李玮)
  • Shi-min Zhang (张世敏)


In the non-linear microwave drying process, the incremental improved back-propagation (BP) neural network and response surface methodology (RSM) were used to build a predictive model of the combined effects of independent variables (the microwave power, the acting time and the rotational frequency) for microwave drying of selenium-rich slag. The optimum operating conditions obtained from the quadratic form of the RSM are: the microwave power of 14.97 kW, the acting time of 89.58 min, the rotational frequency of 10.94 Hz, and the temperature of 136.407 °C. The relative dehydration rate of 97.1895% is obtained. Under the optimum operating conditions, the incremental improved BP neural network prediction model can predict the drying process results and different effects on the results of the independent variables. The verification experiments demonstrate the prediction accuracy of the network, and the mean squared error is 0.16. The optimized results indicate that RSM can optimize the experimental conditions within much more broad range by considering the combination of factors and the neural network model can predict the results effectively and provide the theoretical guidance for the follow-up production process.

Key words

microwave drying response surface methodology optimization incremental improved back-propagation neural network prediction 


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Copyright information

© Central South University Press and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ying-wei Li (李英伟)
    • 1
    • 2
  • Jin-hui Peng (彭金辉)
    • 1
    • 2
    Email author
  • Gui-an Liang (梁贵安)
    • 2
  • Wei Li (李玮)
    • 2
  • Shi-min Zhang (张世敏)
    • 2
  1. 1.Faculty of Metallurgical and Energy EngineeringKunming University of Science and TechnologyKunmingChina
  2. 2.Key Laboratory of Unconventional Metallurgy, Ministry of EducationKunming University of Science and TechnologyKunmingChina

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