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Machine Learning Assisted Analysis of an Ammonia Engine Performance


 Currently, the interest in utilizing ammonia in internal combustion engines stems from the trend toward decarbonization, as ammonia is a zero carbon footprint fuel. Existing research on ammonia engines is limited, while most of the available literature mainly considers the ammonia application in gasoline converted engines. Accordingly, the objective of this study was to increase the knowledge of diesel engines modified for dedicated ammonia operation. A spark plug was added to the original compression ignition engine to control and initiate the ammonia combustion process. The machine learning model was utilized to assist in analyzing the experimentally measured ammonia engine performance. The results showed that the random forest algorithm suffered from boundary underfitting, while the gradient boosting regression trees algorithm encountered overfitting problems. Moreover, the artificial neural network algorithm performs better than support vector regression, effectively learning the relationship between engine control variables and ammonia engine performance. The parametric studies conducted by the well-trained machine learning model suggested that the combustion law of heavy duty ammonia engines was in consistent with that of traditional spark ignition engines. Mmost importantly, the regular compression ratio of diesel engines allowed dedicated ammonia combustion effectively with equivalence ratios as lean as 0.7 despite the slow laminar flame speed of ammonia-air mixture. Further, a compression ratio of 18 contributed to the optimal spark timing at 8&amp;#176;CA BTDC when operated at stoichiometry, which was favorable for engine control. Overall, converting compression ignition engines to ammonia spark ignition operation is promising.