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<p>This study assessed the performance of an agricultural diesel engine running on biofuel derived from <i>Schleichera oleosa</i> (SO), processed through a simple, chemical-free method involving pressing, soaking, drying, and hydraulic pressing, yielding clean fuel oil ready for testing. An engine tune-up was performed to enhance efficiency and reduce fuel consumption. The ANN model analyzed inputs like injection pressure, fuel timing, SO blend ratio, engine speed, and throttle position to estimate brake torque, power, BSFC, and BTE. Meanwhile, the RSM model optimized engine performance using these estimates. The ANN effectively predicted brake torque, power, BSFC, and BTE, However, the R<sup>2</sup> value for BSFC was comparatively lower than other parameters, leading to slightly higher prediction errors. This lower R<sup>2</sup> for BSFC may reflect the complexities in predicting fuel consumption due to factors like SO unique combustion properties and variations in engine operating conditions. The RSM optimization under optimal conditions predicted (%Error) with 33.19 N-m torque (1.57), 5.902&#xa0;kW power (2.74), 441&#xa0;g/kW-hr BSFC (4.17), and 19.09% BTE (3.88) for the DI engine, and 30.78 (0.87) N-m torque (0.87), 5.261&#xa0;kW power (2.88), 375&#xa0;g/kW-hr BSFC (1.35), and 25.02% BTE (6.70) for the IDI engine. The comparative analysis suggests that the DI engine achieves higher brake torque with SO biofuel, while the IDI engine demonstrates reduced BSFC.</p>

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Experimental investigations on bio-diesel from Schleichera oleosa operated indirect injection and direct injection diesel engine: a combined application of ANN and RSM based optimization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月25日

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  • Patamaporn Chaikool,
  • Kemwat Intravised,
  • Chokchai Suiuay,
  • Sahassawas Poojeera

摘要

This study assessed the performance of an agricultural diesel engine running on biofuel derived from Schleichera oleosa (SO), processed through a simple, chemical-free method involving pressing, soaking, drying, and hydraulic pressing, yielding clean fuel oil ready for testing. An engine tune-up was performed to enhance efficiency and reduce fuel consumption. The ANN model analyzed inputs like injection pressure, fuel timing, SO blend ratio, engine speed, and throttle position to estimate brake torque, power, BSFC, and BTE. Meanwhile, the RSM model optimized engine performance using these estimates. The ANN effectively predicted brake torque, power, BSFC, and BTE, However, the R2 value for BSFC was comparatively lower than other parameters, leading to slightly higher prediction errors. This lower R2 for BSFC may reflect the complexities in predicting fuel consumption due to factors like SO unique combustion properties and variations in engine operating conditions. The RSM optimization under optimal conditions predicted (%Error) with 33.19 N-m torque (1.57), 5.902 kW power (2.74), 441 g/kW-hr BSFC (4.17), and 19.09% BTE (3.88) for the DI engine, and 30.78 (0.87) N-m torque (0.87), 5.261 kW power (2.88), 375 g/kW-hr BSFC (1.35), and 25.02% BTE (6.70) for the IDI engine. The comparative analysis suggests that the DI engine achieves higher brake torque with SO biofuel, while the IDI engine demonstrates reduced BSFC.