Deep learning-assisted pulsed discharge plasma catalysis modeling

In this paper, a multi-layer feed-forward deep neural network was introduced into the plasma/plasma catalysis kinetics modeling. The deep learning-assisted modeling enables the initial input parameters for kinetics simu-lation, such as reduced electric field (E/N), to be extracted from specific experimental data after the neural network has been well-trained. The specific amplitudes of E/N and time t are set as the input data of the deep neural network, and the target product densities in time t calculated by the kinetics modeling are set as the output data. Replacing the kinetics simulation, the neural network can efficiently predict the target product densities under the fresh amplitudes of E/N. This method is validated in the plasma model for CH4/Ar pulsed discharge and the plasma catalysis model for N2/H2 pulsed discharge. The results indicate that the extended results calculated by the neural network are in good agreement with the numerical results calculated by the kinetics model and the relative error is 1.15 x 10-3 and 4.19 x 10-4, respectively, which might provide the possibility to assimilate experimental data and simulated data for optimizing research processes and integrating research results.

Automated summary

In this paper, a multi-layer feed-forward deep neural network is used for plasma/plasma catalysis kinetics modeling. The deep learning-assisted modeling extracts initial input parameters from experimental data after the neural network is trained. The neural network efficiently predicts target product densities under specific input parameters in place of kinetics simulation. This method is validated in plasma and plasma catalysis models and shows good agreement with numerical results, providing the potential for integrating experimental and simulated data in research optimization.