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Control Theory and Technology

, Volume 17, Issue 1, pp 24–36 | Cite as

Prediction method for energy consumption per ton of fused magnesium furnaces using data driven and mechanism model

  • Dan Guo
  • Zhiwei WuEmail author
  • Tianyou Chai
  • Jie Yang
  • Jinliang Ding
Article
  • 10 Downloads

Abstract

The electric energy consumed in every ton of acceptable product, namely energy consumption per ton (ECT), is an important overall index for the production process of a fused magnesium furnace. The furnace is the equipment for producing the fused magnesia. The ECT value depends on the current in the smelting process. The optimal operation for a fused magnesium furnace is supposed to have the ECT as low as possible, where the key is to predict ECT accurately. By introducing an unknown high-order nonlinear term, this paper builds a dynamic ECT model for different production batches based on the static ECT model for one batch. The average current is taken as the input of the dynamic ECT model, which is composed of the unknown high-order nonlinear term and a nonlinear model with unknown parameters. The order of the nonlinear term is determined by the distance correlation and the nonlinear term is estimated by the stochastic configuration network, while the parameters of the nonlinear model is identified by the least square method. The estimation of the nonlinear term alternates with the parameter identification. This paper proposes a prediction method for ECT, which is composed of the order identification of the nonlinear term, the alternating identification of the model and the ECT prediction model. The simulation experiments are conducted by the on-site data, and the results verify the effectiveness of the proposed prediction method.

Keywords

Fused magnesia energy consumption per ton alternating identification stochastic configuration network distance correlation 

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Copyright information

© Editorial Board of Control Theory & Applications, South China University of Technology and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Dan Guo
    • 1
  • Zhiwei Wu
    • 1
    • 2
    Email author
  • Tianyou Chai
    • 1
    • 2
  • Jie Yang
    • 1
  • Jinliang Ding
    • 1
    • 2
  1. 1.State Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyang LiaoningChina
  2. 2.National Engineering Research Center of Metallurgical Automation (Shenyang)Shenyang LiaoningChina

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