Abstract.Blast Furnace Gas (BFG) system of an iron and steel works was considered. The relationship of gas amount and factors about BFG generation and consumption was analyzed by grey correlationand the BP neural network prediction model of blast furnace gaswas established based on artificial neural network for forecasting thesupply and demandof BFGinthe iron and steel-making processes.The scientific forecasting of BFG generation and consumption in each process was discussed undernormal production and accidental maintenance condition. The results show that established forecasting model is high precision, small errors, and can solve effectively actual production of BFG prediction problem and decreasing BFG flare, providing theoretical basis for establishing reasonable plans in the iron and steel works.
Technical re-equipment and improvement of the technology for blast-furnace production at Azovstal Iron and Steel Works
