Online monitoring of spatial-temporal distribution of harmful gases during advanced oxidation of NO by convolutional networks and gated recurrent units

Online monitoring of the spatial-temporal distribution of harmful gases has always been a complex problem in the environmental field. This paper proposes a novel mathematical method for online monitoring of the spatial temporal distribution of reactants by machine learning, which can help to remove harmful gases efficiently. In this model, we take the advanced oxidation of NO as an example to evaluate the model performance. The spatial features were extracted by CNN, and GRU extracted the temporal features in the sequence of spatial features. Five physical field variables (mass fraction of ozone, velocity, temperature, the wind direction of the horizontal plane, and the wind direction of the vertical plane) were put into the network to predict NO's spatial-temporal mass fraction distribution. Furthermore, the impact of sampling time interval on monitoring performance was also evaluated. The results show that both the instantaneous and continuous CFD (Computational Fluid Mechanics) and predicted values show high consistency, which indicates that the model can online monitor the spatial-temporal distribution of reactants successfully. In addition, the most suitable sampling time interval is 0.5 s, with low training error (RMSE=0.06 and nRMSE=0.3) and high relation coefficient (r=0.99), which shows the model has great perceived and predicted performance under this condition.