CFD-DPM data-driven GWO-SVR for fast prediction of nitrate decomposition in blast furnaces with nozzle arrangement optimization

Abstract

In the industrial production process, it is important to improve the productivity of blast furnaces in response to the worldwide call for energy saving and emission reduction. In this study, a data-driven method using computational fluid dynamics (CFD) is developed for the rapid prediction of nitrate decomposition in a blast furnace. The method includes the creation of a CFD database, for which nozzle arrangement patterns, relative distances, spray cone angles and temperatures are simulated and collected. Support vector regression (SVR) hyperparameters are optimized using genetic algorithm (GA), particle swarm optimization (PSO), and gray wolf optimizer (GWO). Machine learning interpretability methods shapley additive explanations (SHAP) and partial dependence plots (PDP) are used to analyze the impact of features on prediction results. The results show that the hybrid GWO-SVR model has a better performance, with the degree of fit (R2) improving from 0.7752 to 0.9719. When the distance is taken around 0.58, increasing the temperature and cone angle is more favorable to improve the decomposition rate of the blast furnace. SHAP method shows that only the arrangement 2 and 4 have a positive effect on improving the decomposition rate. The response time for accurate prediction of nitrate decomposition rate is 259200 times shorter than that of industrial-scale CFD simulations. The method is also used to determine the best combination of solutions. This cross-disciplinary approach provides a time-saving and cost-effective tool for improving the response of blast furnace productivity to changes in nozzle arrangement.