Desenvolvimento de soft sensor para predição do estado térmico do ferro gusa em alto-forno usando fuzzy c-médias e modelo exógeno auto-regressivo não linear.

Abstract

The temperature and silicon content of hot metal are essential parameters for the thermal control of a blast furnace. However, the physical structure of the blast furnace prevents direct and online methods from accurately predicting these parameters. In this study, it was proposed a new algorithm based on fuzzy c-means (FCM) and exogenous nonlinear autoregressive model (NARX) to develop a soft sensor for predicting the temperature and silicon content of hot metal. FCM is a data modeling technique that works by clustering similar data objects while separating dissimilar ones. FCM is highly effective in the identification of natural groupings in the observed data; in this case, determination of groups of operational conditions. The NARX neural network presents a model for the accurate prediction of temperature and silicon content of hot metal. The proposed algorithm was evaluated based on its efficiency in simulating the industrial process for manufacturing hot metal in a blast furnace. The results showed that a soft sensor based on FCM-NARX models presents a high performance, with a determination coefficient for the prediction of temperature and silicon content in hot metal of 99.7% and 99.6%, respectively.