Predictive modeling and optimization of <scp>PPCCI</scp> engine emissions using signal‐to‐noise ratio and response surface methodologies

Abstract Fuel and design parameters are the two major parameters for controlling the emission characteristics of a Partially Premixed Charge Compression Ignition (PPCCI) engine and much works were not focussed to investigate their combined influence by simultaneously varying these parameters which require an extensive experimental work. This unique work is aimed to develop and test an integrated predictive PPCCI engine model to reduce its NOx emissions while minimizing adverse effects on smoke emissions and engine performance. MATLAB R2022 software was used by recalibrating a conventional compression ignition (CI) engine in the existing MATLAB simulation framework. Optimal factor levels were obtained through Response Surface Methodology (RSM) and Multi‐Response Signal‐to‐Noise Ratio (MRSN) optimization techniques and compared. In meeting the objective of this work, main injection fuel is the most influencing factor followed by main fuel injection timing and these two factors were collectively contributed around 88.5% as the influencing factors. Fuel‐related factors had a greater cumulative influence (43%) than design‐related factors (39.5%). When comparing the optimized results of the developed model with a conventional CI engine parameters, MRSN optimization technique resulted in a marginal decrease in NOx emission with an increase in smoke emission and decrease in brake thermal efficiency (BTE) and RSM has achieved a simultaneous reduction in NOx (17.7%) and smoke emissions (9.2%) along with a marginal (3%) increase in engine performance which shows that RSM was more effective than MRSN in meeting the objective of this work.