Combining genetic algorithm and deep learning to optimize a chemical kinetic mechanism of ammonia under high pressure

Abstract

Ammonia is a potential zero-carbon fuel, but the difficulties of experimenting at high pressure limit its development of application in power engineering. In this work, deep learning and genetic algorithm are combined to optimize the chemical reaction kinetics mechanism of ammonia under high pressure. The result shows that deep learning model is able to regress experimental data of ignition delay time in the range of less than 0.2% |Elog| (logarithmic absolute error). At the same time, the trained model can be applied to the trend prediction of ignition delay time at high pressure, enriching combustion data of ammonia fuel that is not available from experiments. Optimization of PLOG reactions with a large population size was performed by strengthen elitist genetic algorithm, and the prediction accuracy of ammonia chemical reaction kinetics mechanism was improved by about 5% finally. The application value of artificial intelligence in combustion science is demonstrated.