A data-driven approach for the quick prediction of in-furnace phenomena of pulverized coal combustion in an ironmaking blast furnace

Abstract

Pulverized coal injection (PCI) is a dominant technology in ironmaking blast furnaces (BFs) for energy efficiency and cost reduction, while the relevant in-furnace phenomena are experimentally inaccessible. It is desired to understand these in-furnace phenomena in a timely manner. In this study, a data-driven approach is developed for rapid predicting the multi-objective in-furnace combustion characteristics related to PCI operation in a BF. The approach includes a database of computational fluid dynamics (CFD) 243 simulations in terms of flow field, temperature field, gas species concentration and coal burnout within the raceway; and a machine learning (ML) model where random forest regression model is selected due to its higher accuracy than others. The results show that this approach can predict the multi-objective in-furnace phenomena with high accuracy in aspects of temperature, gas species concentrations and combustion efficiency in the raceway. Furthermore, three additional cases - no. 244–246 scenarios outside the database, were tested to demonstrate the ML prediction effectiveness through virtualizing and comparing the full in-furnace phenomena. The response time of this approach is nearly 16,000 times shorter than the CFD simulations while achieving similar accuracy. This prediction approach provides a time- and cost-effective tool for optimizing the responses of in-furnace phenomena to PCI operation changes.