Effect of Ammonia on the Characteristics of a Diesel Engine

Growing concerns over fossil fuel depletion and tightening global emission standards have compelled the engineering community to look beyond conventional diesel as the sole fuel for compression-ignition engines. Among the alternatives receiving serious research attention, ammonia (NH₃) stands out because it carries no carbon whatsoever — meaning its combustion produces no carbon dioxide, no carbon monoxide from its own chain, and no soot. It can also be synthesised entirely from renewable electricity and atmospheric nitrogen, giving it a credible pathway to a genuinely carbon-neutral fuel cycle. This project investigates, through a structured review of published experimental literature and a supporting machine learning analysis, how the progressive substitution of diesel with ammonia — in a dual-fuel compression-ignition configuration — alters engine performance, combustion behaviour, and exhaust emission characteristics. Data and findings are drawn from eight carefully selected peer-reviewed studies spanning the period 2011 to 2024, covering a range of engine types, operating speeds, load conditions, and ammonia energy fractions from zero to fifty percent. The review reveals that moderate ammonia substitution, up to roughly fifteen to twenty percent of total fuel energy, can reduce CO₂ emissions by eighteen to twenty percent and smoke opacity by forty to fifty percent with an acceptable brake thermal efficiency (BTE) penalty of less than three percentage points. Beyond this threshold, combustion instability grows, ammonia slip increases in the exhaust, and BTE declines more steeply. NOx emissions, driven primarily by fuel-bound nitrogen rather than the thermal mechanism familiar from pure diesel operation, rise sharply even at low ammonia fractions and represent the most significant emissions management challenge. Selective catalytic reduction aftertreatment, which can utilise exhaust ammonia as its own reductant, emerges as the natural and necessary partner technology. Three machine learning models — an Artificial Neural Network, a Random Forest ensemble, and XGBoost — were trained on a dataset assembled from the reviewed literature to predict NOx emissions and BTE. The Random Forest model delivered the highest accuracy, with R² values of 0.974 for NOx and 0.968 for BTE. Feature importance analysis confirmed that ammonia energy fraction is the single most influential variable for NOx, while engine load dominates BTE prediction. These findings provide quantitative guidance for engine calibration and control strategy development in future ammonia-capable powertrains.