TY - JOUR
T1 - A data-driven approach for the quick prediction of in-furnace phenomena of pulverized coal combustion in an ironmaking blast furnace
AU - Liu, Yiran
AU - Zhang, Huiming
AU - Shen, Yansong
PY - 2022/10/12
Y1 - 2022/10/12
N2 - 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.
AB - 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.
KW - Blast furnace
KW - Combustion
KW - Computational fluid dynamic
KW - Machine learning
KW - PCI
KW - Random forest
UR - http://www.scopus.com/inward/record.url?scp=85135172636&partnerID=8YFLogxK
U2 - 10.1016/j.ces.2022.117945
DO - 10.1016/j.ces.2022.117945
M3 - Article
SN - 0009-2509
VL - 260
JO - Chemical Engineering Science
JF - Chemical Engineering Science
M1 - 117945
ER -