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Prediction and Optimization of Blast Furnace Parameters using Artificial Neural Network


Lutan Beerendra Yadav , JNTUCEA; Dr. B. Om Prakash, JNTUCEA


ANN, Blast Furnace, RAFT, MIMO


Blast furnace (BF) is a giant countercurrent reactor and heat exchanger, and is the first step towards the production of the steel. It is one of the most complex industrial reactor and is impossible to model mathematically. The operation and control of an industrial blast furnace is a serious problem, hence to overcome this we are using Artificial Neural Network (ANN). It is very important to predict the various temperatures i.e., Raceaway Adiabatic Flame Temperature (RAFT), Stack temperature and uptake temperature. Optimizing these temperature distribution would lead to considerable savings of input material of blast furnace. Productivity as well as quality can be improved by knowing these parameters in advance. In this paper, we are using the multi input multi output (MIMO) artificial neural network. By this we have to optimize overall efficiency, minimize operational cost, and reduce fuel consumption which leads to improve productivity. The input parameters used are Oxygen enrichment, Blast volume, Blast pressure, Top gas pressure, Hot Blast temperature, steam injection rate, cold blast flow, cold blast temperature and Pulverized coal injection rate. For prediction and optimization back propagated, feed forward artificial neural network is applied. All the input data were collected from the Vizag steel plant (VSP) during the period of 5 months.

Other Details

Paper ID: IJSRDV7I70013
Published in: Volume : 7, Issue : 7
Publication Date: 01/10/2019
Page(s): 15-22

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