Abstract
The quality of products and processes is a permanent challenge for industries, and such challenge is no different in steelmaking processes. One of the main problems affecting the quality of steel products is the existence of contaminants in alloy steel, and phosphorus (P) is a major contamination element interfering with the steelmaking process. The increased P levels can severely affect the physical integrity of steel bonds, thus threatening the quality of the final product. This paper proposes a robust approach to model the phosphorus concentration levels in the steelmaking process. The proposed approach consists in applying the artificial neural networks techniques for improving the energetic efficiency of the industrial process. We used the improved neural network models inspired in the human nervous system for processing the information. The different techniques used for modelling the phosphorus levels investigate the variables that have a significant influence on refining process. Based on the better predictive model, the increase of phosphorus levels in the final product is related to initial levels of carbon, oxygen, magnesium, manganese oxide and calcium oxide. The results illustrate the efficiency of the techniques used in the modelling, with emphasis on the adequacy of the predictive models constraints in the refining process. This study presents a relevant strategy to model characteristics’ of raw material based on forecasting strategy to the efficiency of alloys and steel industry.
