Hybrid Method for Endpoint Prediction in a Basic Oxygen Furnace

Abstract

Strict monitoring and prediction of endpoints in a Basic Oxygen Furnace (BOF) are essential for end-product quality and overall process efficiency. Existing control models are mostly developed based on thermodynamic principles or by deploying advanced sensors. This article aims to propose a novel hybrid algorithm for endpoint temperature, carbon, and phosphorus, based on heat and mass balance and a data-driven technique. Three types of static models were established in this study: firstly, theoretical models, based on user-specified inputs, were formulated based on mass and energy balance; secondly, artificial neural networks (ANN) were developed for endpoints predictions; finally, the proposed hybrid model was established, based upon exchanging outputs among theoretical models and ANNs. Data of steelmaking production details collected from 28,000 heats from Tata Steel India were used for this article. Machine learning model validation was carried out with five-fold cross-validation to ensure generalizations in model predictions. ANNs are found to achieve better predictive accuracies than theoretical models in all three endpoints. However, they cannot be directly applied in any steelmaking plants, due to possible variations in the production setting. After applying the hybrid algorithm, normalized root mean squared errors are reduced for endpoint carbon and phosphorus by 3.7% and 9.77%.