Temperature intelligent prediction model of coke oven flue based on CBR and RBFNN

Yang He*, Gongfa Li, Ying Sun, Guozhang Jiang, Jianyi Kong, Du Jiang, Honghai Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The temperature of coke oven is an important process parameter, but it is difficult to obtain the temperature of the vertical flue in real-time. The establishment based on the case-based reasoning (CBR) and radial basis function neural network (RBFNN) of coke oven flue temperature intelligent prediction model, realise the real-time prediction of the temperature, and help to realise the coke oven production process of intelligent optimisation control. The real-time forecast under different conditions is realised by the selective intelligent forecasting model of the coke oven, and the forecasting performance of system model is simulated. The results show that the forecasting model is faster and more reliable than the traditional artificial forecast. Finally, combining with the actual data of a steel enterprise to verify, the results show that the model meet the actual working condition, it can provide relevant processing methods for the soft measurement of complex industrial production control process, and it has some practical significance for intelligent optimisation control.

Original languageEnglish
Pages (from-to)327-339
Number of pages13
JournalInternational Journal of Computing Science and Mathematics
Volume9
Issue number4
DOIs
Publication statusPublished - 5 Sep 2018

Keywords

  • Case-based reasoning
  • CBR
  • Coke oven
  • Intelligent forecast
  • Neural network
  • Temperature measurement

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