Prediction model of burn-through point with fuzzy time series for iron ore sintering process

Abstract

Burn-through point (BTP) is an essential parameter in the iron ore sintering process. Operators usually judge whether the current production is stable by monitoring the BTP. It comes with significant application prospects to predict the BTP accurately. A prediction model of the BTP with fuzzy time series is designed in this paper. First, the fuzzy time series prediction method with the Fuzzy C-Means clustering is presented as the core modeling method. A prediction model of the response is constructed to obtain a timely response to the current BTP. The prediction model of the difference is established to estimate the present unmeasurable disturbance on the BTP. Then, a hybrid prediction model is built, which realizes the composition of these two models by an adjustment factor. Finally, a series of experiments is carried out using the raw time series data from an iron and steel plant. The experimental result shows that the designed model has better prediction performance for the BTP than existing models, which is an advantage resulting from the hybrid structure and the fuzzy time series prediction model with the Fuzzy C-Means clustering. This prediction model of the BTP implies the foundation for the stable control of the iron ore sintering process.