Parametric Investigation And Multi Objective Optimization Of A Spark Ignition Engine Fuelled With Hydrogen-Blended Compressed Natural Gas Using Taguchi Grey Relational Analysis

Abstract

This work investigates the multi-objective optimization of a spark-ignition (SI) petrol engine operated with hydrogen-blended compressed natural gas (HCNG) at a constant load of 9 kg. A Taguchi-based Grey Relational Analysis (GRA) was adopted to simultaneously optimize the performances, combustion, energy, exergy, and emission responses. Four governing operating parameters, ignition timing, engine speed, injection timing, and hydrogen induction percentage were systematically varied using the Taguchi L32 orthogonal array to minimize experimental runs while preserving analytical robustness. The measured responses were normalized using higher-the-better and lower-the-better criteria, and Grey Relational Grades (GRG) were evaluated to identify the optimal operating combination. Analysis of variance revealed that hydrogen content was the most influential parameter affecting the overall GRG, contributing 14.69% of the total variation and exhibiting marginal statistical significance. The optimal operating condition was determined as 20° BTDC ignition timing, 1200 rpm engine speed, 30° BTDC injection timing, and 25% hydrogen enrichment. Under these optimized conditions, HCNG operation significantly enhanced combustion quality, improved thermal and second-law efficiencies, and markedly reduced carbon monoxide and hydrocarbon emissions, with only a marginal increase in Nox levels. The findings demonstrate the effectiveness of the Taguchi GRA approach for complex multi-response optimization and confirm the potential of HCNG as a viable cleaner transitional fuel for spark-ignition engines.