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APPLICATION OF NEURAL NETWORKS FOR MODELING STEELMAKING PROCESS

One of the major causes of unconformity in the steelmaking process is the presence of impurities in the steel alloys. One of the main impurities found in the steel alloys is the phosphorus. The phosphorus causes several deleterious effects in the steel. In that way this research has as main objective to predict the percentage of phosphorus in the final composition of the material in the production process of one chemical industry. Our approached used one model of Artificial Neural Network in the data of the production process disposed. After apply the network it was compared with regression models developed with the same objective using statistical measures of error minimization. The results present that the network obtained the best results, an error minimization of 3, 68%.