Assessment of ammonia-diesel fuel blends on compression ignition engine performance and emissions using machine learning techniques

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This study investigates the performance and emission characteristics of a multi-cylinder, naturally aspirated, compression ignition diesel engine using ammonia as a secondary fuel under full load conditions across various engine speeds. Ammonia dispersed with neat diesel at the concentration of 5, 10, and 15 liters per minutes via inlet manifold. Experimental tests assessed the impact of diesel-ammonia fuel mixtures at operating speeds of 1000 rpm to 3000 rpm. The performance metrics such as brake thermal efficiency, brake specific fuel consumption, exhaust gas temperature, and emissions characteristics were determined. Moreover, the study employed predictive models such a simple extraction, linear regression, and Long Short-Term Memory (LSTM) networks to analyze engine behaviour. The results demonstrated that increasing ammonia concentration improved brake thermal efficiency and reduced brake specific fuel consumption and exhaust gas temperature across all tested speeds. Specifically, ammonia additions led to significant decreases in carbon monoxide and carbon dioxide emissions due to reduced carbon content in the fuel mix, while nitrogen of oxides emissions presented a more complex pattern. Inclusion of the ammonia increases the NOx due to the inherent nitrogen content. Particulate matter emissions were also reduced, highlighting ammonia’s potential to mitigate particulate formation. Compared to difference prediction models, LSTM showed superior capability in capturing complex non-linear relationships and temporal dependencies in engine performance data, outperforming traditional linear models.