Title

PREDICTION OF SINTER PLANT PRODUCTIVITY BY NEURAL NETWORK

PREDICTION OF SINTER PLANT PRODUCTIVITY BY NEURAL NETWORK

Authorship

Silva, Thiago Pinto; Silva, Alexandre Medeiros da; Resende, Bruno Alves; Silva., Braulio Viegas da

DOI

10.5151/2594-357X-22152

Downloads

heading.download_article_detail 367 Downloads

Abstract

The prediction of productivity represents an important resource in order to anticipate losses, increasing the performance of the sinter machine. In this context, a neural network model which relates the main operating parameters and the sintering productivity was developed. The kind of neural network chosen was multi-layer perceptron. The best configuration was obtained with one hidden layer and nine neurons, and the correlation coefficient obtained between predicted and actual productivity was 0.77. Furthermore, the neural network showed better predictive ability than the multiple linear regression technique. The model was applied to Sinter Machine#3 at Usiminas - Ipatinga Plant.

 

The prediction of productivity represents an important resource in order to anticipate losses, increasing the performance of the sinter machine. In this context, a neural network model which relates the main operating parameters and the sintering productivity was developed. The kind of neural network chosen was multi-layer perceptron. The best configuration was obtained with one hidden layer and nine neurons, and the correlation coefficient obtained between predicted and actual productivity was 0.77. Furthermore, the neural network showed better predictive ability than the multiple linear regression technique. The model was applied to Sinter Machine#3 at Usiminas - Ipatinga Plant.

Keywords

Sinter plant; Neural network; Productivity.

Sinter plant; Neural network; Productivity.