THE ADAPTIVE AUTOREGRESSIVE MODELS FOR THE SYSTEM DYNAMICS AND PREDICTION OF BLAST FURNACE

Abstract

The blast furnace can be viewed as a time-varying stochastic system. The Adaptive Autoregressive (AAR) models are proposed to characterize such systems. AAR identification is a method of successive parameter estimation by using recursive formulas with variable forgetting factors to closely track time-varying parameters. A simple example is presented to illustrate the parameter tracking capability of the AAR models. Based on the prediction errors, the AAR models of blast furnace are compared with the conventional time series models. Through this comparison, the AAR models prove to be superior to the other time series models, since the latter are suitable only for time-invariant systems. It is concluded that during smooth operation, just the AAR scalar model is required for forecasting as operational guide. When the operation is uneven, the AAR vector model provides the better results. To control the performance of this process the data should be sampled under uneven operating condition, where the AAR vector model is the best among all the models considered and can properly express the dynamic relationship between the input and output variables.

KEYWORDS:

• Adaptive Autoregressive (AAR) models
• Time series analysis System identification
• Time-varying systems
• Blast furnace modeling

Additional information

Notes on contributors

YUNG-CHENG CHAO

To whom correspondence should be addressed.