Prediction of SO₂ and NO_x in sintering flue gas based on PSO-BP neural network model

ABSTRACT

In the long process of iron and steel, the sintering process has the largest amount of flue gas emissions, many types of pollutants and high concentrations. The source control of SO₂ and NOx in sintering flue gas through digital technology has become a new emission reduction technology. In this study, the BP neural network model (BP-NN) is optimized by using the particle swarm algorithm (PSO) to form the PSO-BPNN model, which effectively improves the characteristics of BP-NN with slow convergence speed and easily falls into local minima, and improves the learning ability and generalization. The test results show that the PSO-BP-NN algorithm not only has fast convergence speed and high prediction accuracy, but also has smaller training and inspection errors. In addition, this model combines process theory and feature engineering selection of parameters, which effectively improves the accuracy of the model and the interpretability of the results based on the linkage of process knowledge, and has certain analytical significance for the source management and post-treatment of sintered flue gas.

KEYWORDS:

• Sintering flue
• the PSO algorithm
• BP neural network
• prediction
• feature engineering
• grey correlation analysis
• sintering process analysis
• PSO-BP neural network

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was funded by Hebei Basic Research Projects of Higher Education Institutions (No. JQN2020032), National Nature Science Foundation of China (No. 52004096), and Hebei Province High-end iron and Steel Metallurgical Joint Research Fund Project (No. E2020209208).