Research on Improving the Energy Efficiency of Plasma-Catalyzed Ammonia Synthesis Based on XGBoost and Bayesian Optimization

Ammonia synthesis is vital for fertilizer production, but the traditional Haber-Bosch process is energyintensive and environmentally burdensome due to its high-temperature and high-pressure operations.Plasma-catalytic ammonia synthesis offers a sustainable alternative, generating large datasets under various experimental conditions.To optimize energy efficiency, we established a database with 305 data points and 7 experimental parameters, each linked to its corresponding energy efficiency.We employed an Extreme Gradient Boosting (XGBoost) regression tree model, achieving an average R value of 0.9434 for predictions.Bayesian Optimization (BO), using Gaussian Process Regression as a surrogate model, systematically explored the experimental parameter space.It utilized XGBoost predictions to identify parameter combinations that maximized energy efficiency.After 50 iterations, the optimal parameters were identified: 6.4 g catalyst mass, 50 mm grounding electrode length, nickel metal catalyst, AlO catalyst support, 5 W power, 160 mlmin flow rate, and a 1:2 feed ratio.Under these conditions, the energy efficiency of plasma-catalytic ammonia synthesis improved to 1.49 gkWh, a 22.1% increase from the highest value of 1.22 gkWh in the dataset.