A Decomposition-based Encoder-Decoder Framework for Multi-step Prediction of Burn-Through Point in Sintering Process

Abstract

Sintering process is a critical step in the ironmaking process. Burn-through point (BTP), as a key performance index of sintering ore, has a great influence on the quality of the sintering product. The existing prediction methods attempt to use a single model to establish the relationship between variables. However, due to the strong volatility, uncertainty, and multivariable coupling of sintering process, the traditional prediction model cannot produce reliable predictions. In order to deal with the complex characteristics of sintering process, this paper proposes a decomposition-based encoder-decoder modeling framework, in which a sequence decomposition module is designed to decompose the input time series into different sub-sequences. Then, these sub-sequences are constructed by the encoder-decoder models separately. The effectiveness of the proposed multi-step ahead prediction modeling framework was evaluated in a real-world sintering process. Compared with the traditional prediction modeling framework, the proposed modeling framework has more accurate results in multi-step ahead prediction.