Multi-response optimization of biogasoline engine performance and exhaust emissions using the taguchi method
Keywords:
Taguchi method, Biogasoline, Compression ratio, Ignition timing, Spark plug type, Performance, Exhaust emissionsAbstract
The application of high-ethanol bio gasoline in small spark-ignition (SI) engines offers a promising pathway to reduce exhaust emissions while supporting renewable fuel utilization in motorcycle transportation. However, ethanol-rich fuels create complex interactions between combustion characteristics, engine performance, and emission formation, requiring a comprehensive optimization strategy. This study aims to conduct multi-response optimization of a bio gasoline-fueled motorcycle SI engine by simultaneously evaluating exhaust emissions and performance characteristics. A Taguchi experimental design using an L₉ (3⁴) orthogonal array was applied to investigate the effects of bio gasoline blend ratio (E70–E80), compression ratio, ignition timing, and spark plug type. The response variables include hydrocarbon (HC) and carbon monoxide (CO) emissions, as well as engine torque and power. Signal-to-noise ratio analysis and analysis of variance were employed to determine the significance of each control factor and to evaluate response robustness. The results show that emission characteristics are mainly influenced by fuel composition and ignition-related parameters, with bio gasoline type having the greatest contribution to HC reduction, while ignition timing and spark plug type significantly affect CO emissions. Conversely, engine performance responses are primarily controlled by compression ratio, indicating its dominant influence on combustion pressure development and thermal efficiency. The optimal parameter settings for minimizing emissions and maximizing performance are not identical, demonstrating a trade-off between these objectives. Therefore, a response-priority-based multi-response optimization approach was adopted, prioritizing emission reduction while maintaining acceptable engine performance. Experimental validation shows good agreement between predicted and observed results, confirming the reliability of the proposed optimization framework.
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