AI Article Synopsis

  • Sintering is a key preproduction process in ironmaking, and improving its operational performance is a persistent challenge for operators.
  • A new method using a prediction model based on Gaussian process regression and mutual information analysis is introduced to enhance this performance.
  • The method effectively assesses performance grades and adjusts control measures, demonstrating high prediction accuracy and significant improvements in operating performance through experimental validation.

Article Abstract

Sintering is the preproduction process of ironmaking, whose products are the basis of ironmaking. How to improve the operating performance of the iron ore sintering process has always been a problem that operators are committed to solve. An operating performance improvement method based on prediction and grade assessment is presented in this article. First, considering the data distribution characteristics of the process, a performance index prediction model based on the Gaussian process regression is built, in which the mutual information analysis method is used to select the inputs of the performance index prediction model. Then, the operating performance grade is assessed by a threshold division method. Next, the operating performance grade guides the control of the burn-through point to improve the operating performance. Finally, experimental verification is performed based on the actual running data. The results show that the proposed method has high prediction accuracy, and it is also significant in improving the operating performance. Therefore, this approach provides an effective solution to predict and improve operating performance.

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Source
http://dx.doi.org/10.1109/TCYB.2021.3071665DOI Listing

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