A simulation study on NOx reduction efficiency in SCR catalysts utilizing a modern C3-CNN algorithm

The simulation of De-NOx system by selective catalytic reduction (SCR) catalyst is very important in industrial application, however, the simulation is always highly time-consuming. In this work feed-forward back-propagation artificial neural network (BPNN) and Cross-Channel Communication Convolutional Neural Network (C3-CNN) algorithm are first proposed as a tool for numerical simulation on De-NOx system by selective catalytic reduction (SCR) catalyst. Initially, one-dimensional Computational Fluid Dynamics (CFD) model allowed the analysis of the contribution of several parameters in SCR reaction (gas velocity, ammonia-to-nitrogen ratio, temperature, and channel length) to DeNOx efficiency. Then, 3600 derived data samples are trained by BPNN neural network which shows a high predictivity (R2 = 0.95542). Additionally, the influence on simulation results of algorithm parameters is analyzed. Furthermore, the introduced Cross-Channel Communication Convolutional Neural Network (C3-CNN) algorithm enhanced the accuracy, efficiency and reduced training time for the De-NOx system simulation. ; The research was supported by National Natural Science Foundation of China (52376104), Joint Funds of the National Natural Science Foundation of China (U20A20302), Innovative group projects in Hebei Province (E2021202006), the project of Science and Technology in the Universities of Hebei Province (JZX2023006).