Predicting the free calcium oxide content on the basis of rough sets, neural networks and data fusion

  • Authors:

  • Yunxing Shu;Shiwei Yun;Bo Ge

  • Affiliations:

  • School of Mechatronic Engineering, Wuhan University of Technology, Hubei, Wuhan, China and Luoyang Institute of Science and Technology, Luoyan, Henan, China;Luoyang Institute of Science and Technology, Luoyan, Henan, China;School of Mechatronic Engineering, Wuhan University of Technology, Hubei, Wuhan, China and Luoyang Institute of Science and Technology, Luoyan, Henan, China

  • Venue:
  • LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
  • Year:
  • 2007

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Abstract

This study first created a model to predict the content of free calcium oxide (fCaO) of the calcined clinker in the rotary kiln by adopting the technologies of rough sets, neural networks and data fusion. And then it was used to predict the quality of the calcined clinker in the rotary kiln and pleasant simulation results were obtained, indicating that the model is valid and has attained the goal of increasing the training speed and precision. Besides, it has solved many problems in the course of cement production, such as big inertia, lagging, time variation, serious nonlinearity, multiple parameters, serious coupling, and difficulty in creating systematic models.