Operation Status Prediction Based on Top Gas System Analysis for Blast Furnace

Top gas is a critical reference for scheduling workers to analyze the operation status of a blast furnace (BF). An accurate prediction of top gas indices can help to forecast the operation status of BF. In this brief, an operation status analytical method is proposed based on top gas indices forecast for BF. The Hammerstein system is used to approximate the actual model of top gas indices series. Bayesian technique and reversible jump Markov Chain Monte Carlo method are adopted for parameter and model order identification in the proposed prediction algorithm. In the system, model order, parameters, and regularization parameters cannot be obtained directly and we compute these parameters based on sampling theory. The operation status of BF is reflected by indices such as the contents of CO and CO
₂
, temperature, and pressure. The confidence regions are computed based on large amounts of top gas indices data, in which the prediction of the indices is considered to be normal. To verify the effectiveness of the proposed method, the top gas of #2 BF from Liuzhou Iron and Steel is employed in the experiments. A series of experiments are reported and the confidence regions of top gas indices are presented for the operation status prediction of BF.