Optimization of Blast Furnace Parameters using Artificial Neural Network

Inside the blast furnace (BF) the process is very complicated and very tough to model mathematically. Blast furnace is the heart of the steel industry as it produces molten pig iron which is the raw material for steel making. It is very important to minimise the operational cost, reduce fuel consumption, and optimise the overall efficiency of the blast furnace and also improve the productivity of the blast furnace. Therefore a multi input multi output (MIMO) artificial neural network (ANN) model has been developed to predict the parameters namely raceway adiabatic flame temperature (RAFT), shaft temperature and uptake temperature. The input parameters in the ANN model are oxygen enrichment, blast volume, blast pressure, top gas pressure, hot blast temperature (HBT), steam injection rate, stove cooler inlet temperature, & stove cooler outlet temperature. For the optimisation of the predictive output back propagation ANN model has been introduced. In this present work, Artificial Neural Network (ANN) has been used to predict and optimise the output parameters. All the input data were collected from Rourkela steel plant (RSP) of blast number IV during the one month of operation.

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