Neural modeling helps the BOS process to achieve aimed end-point conditions in liquid steel

Abstract

This paper describes the development of neural models and their industrial applications to the basic oxygen steel-making (BOS) plant of the Companhia Siderúrgica Nacional (CSN—Volta Redonda/Brazil). The BOS is a transient process, highly complex and is also subject to oscillations in raw material composition. A precise model is essential to adjust end-blow oxygen and coolant requirements to match with the targets of end-point temperature and carbon percentage in liquid steel. An inverse neural model was developed in order to calculate the end-blow process adjustments. At the end of 40 industrial runs, 82.5% of simultaneous agreement with the targets was obtained, against 66% obtained from the commercial model usually employed at CSN's plant. The inverse model was then on-line implemented to automatically control the BOS process. The neural model has been retrained from previous weights and biases as soon as the performance decreases. Average hitting rate decreased related to the previous industrial investigation, however, it is still higher than that obtained from the commercial model application. As a consequence, liquid steel reprocessing is avoided and a high level of steel productivity is obtained.