Hybrid prediction method of blast furnace gas generation considering multi-operation modes in steel plants near the city

Abstract

Blast furnace gas (BFG) generated in blast furnace (BF) ironmaking process can be converted into steam and electricity to provide energy for the nearby cities. Accurate prediction of BFG generation is crucial for assessing BFG surplus, which is the difficult problem faced by steel plants. A hybrid prediction method is proposed to solve it, which is named Radial Basis Function Neural Network (RBFNN) Based on Improved Genetic Algorithm (IGA) and Grey Relation Analysis (GRA). BF’s operation modes, including smooth, blown-down, blow-down to 0, blow-in, were classified to exclude the unrelated feature factors and set the adaptive prediction horizon. Then feature factors related to BFG generation are quantitatively analyzed by GRA in different operation mode, which affects the structure of RBFNN. And the key parameters of RBFNN is optimized by IGA. Proposed method is validated by a case study from a China’s steel plant. The results show the hybrid method has the obvious advantage with the MAPE of 3.31 %, 3.22 % and RMSE of 337.57 m3 in blow down, blown-in and smooth operation mode, respectively. This paper helps steel plants assess how much steam and electricity converted from BFG can be sent to its nearby cities, and contributes to energy resources utilization.