Predicting the Ammonia Synthesis Performance of Plasma Catalysis Using an Artificial Neural Network Model

Ammonia synthesis in a plasma catalysis system coupling dielectric barrier discharge and an alumina-loaded ruthenium catalyst was investigated. The discharge energy, the N2 to H2 ratio, and the reactant flow rate greatly affect the production of NH3 and the efficiency of energy. Higher ammonia synthesis rates and higher energy yields were obtained at high discharge powers and flow rates. An artificial neural network (ANN) was built to describe the influence of operating parameters on the NH3 synthesis performance, including ammonia synthesis rate and energy yield. The proposed ANN model was trained using experimental data. The results showed the N2 to H2 ratio was the most impactful parameter with a relative importance of 41.7% on the model, followed by the flow rate and discharge power of 32 and 26.3%, respectively. This ANN model can effectively help to optimize the operating parameters of the plasma catalysis system for NH3 synthesis and predict the catalysis performance under specific situations.