Multi-objective optimization of structural parameters of SCR system under Eley-Rideal reaction mechanism based on machine learning coupled with response surface methodology

Abstract

Selective catalytic reduction (SCR) is an important method to control nitrogen oxides (NO_x) emissions from diesel engines. Excellent SCR structural parameters are the key to effectively reduce NO_x and back pressure. The dynamic reaction processes of NO_x standard reaction, fast reaction and NO₂-SCR reaction are deeply explored by establishing the Eley-Rideal model. The results show that the wall thickness and washcoat thickness of the SCR are the main determinants of the catalyst performance, while the CPSI has a great influence on the pressure drop. In addition, regression prediction analysis of experimental data by random forest (RF), particle swarm optimized backpropagation artificial neural network (PSOBP-ANN) and response surface methodology (RSM) was performed to explore the coupling relation functions of structural parameters, and optimal test results were solved and verified. The denitrification efficiency of the structure-optimized SCR system increased by 22 % and the pressure drop decreased by 23 %.