Neuro fuzzy modelling of Basic Oxygen Furnace and its comparison with Neural Network and GRNN models

Abstract

The primary objective of steelmaking through Basic Oxygen Furnace (BOF) process is to achieve desired end point carbon content, temperature and percentage composition at the lowest cost and in the shortest possible time. As of now, most widely used models for prediction of parameters of converter steelmaking are mechanistic model, statistical model and neural network model for the prediction of the end point carbon content and temperature from BOF process parameters with reasonable accuracy. The (BOF) process is a widely preferred and effective steelmaking process due to its higher productivity and low production cost. The process of converter steel making is complicated and not completely understood as it involves multiphase physical chemical reaction at high temperature. Obtaining molten steel of desired chemical composition is the objective of the process. Obviously, in the converter steel making, the end point carbon content and temperature of the molten steel are important controlling parameters to ascertain whether the molten steel of desired quality is achieved or not. In the present paper, the authors have made an attempt to develop model for end point carbon and temperature with the latest methodology i.e., Adaptive Neural Fuzzy Inference System (ANFIS) and then have brought out the comparison of the results achieved in Neural Network and GRNN models. Results from ANFIS model predict more accurately in contrast to those from BPNN model vis-a-vis the measured carbon content and temperature.