Online estimation of electric arc furnace tap temperature by using fuzzy neural networks

Industrial factories require continuous analysis and reengineering over its production processes but always keeping a severe control of material costs and operation emissions. In the past electric steel mills have been subject of some operation models developed in order to improve the control of the arc furnace by means of mathematical techniques and, later on, with finite elements technique (FEM). However, these models have not reached the expected results and applicability. In this case, a model has been developed that allows improving the control through a better prediction of the final temperature and, as consequence, to reduce the consumption of energy in the electric arc furnace. Required information for this new model will be obtained gathering knowledge collected up from data obtained of a certain electric furnace and also considering the plant operators and technicians experience. The model has been constructed by using neural networks as classifier, and with a final fuzzy inference function to return a predicted temperature value.