Department of Mechanical Engineering, CK college of Engineering and Technology, Jayaram Nagar, Chellangkuppam, Cuddalore, 607003, India.
Department of Automobile Engineering, Madras Institute of Technology (MIT) Campus, Anna University, Chromepet, Chennai, Tamil Nadu, 600044, India.
Environ Sci Pollut Res Int. 2020 Jul;27(20):24702-24722. doi: 10.1007/s11356-019-06222-7. Epub 2019 Sep 5.
This study is focused on artificial neural network (ANN) modelling of non-modified diesel engine keyed up by the combination of two low viscous biofuels to forecast the parameters of emission and performance. The diesel engine is energised with five different test fuels of the combination of citronella and Cymbopogon flexuous biofuel (C50CF50) with diesel at precise blends of B20, B30, B40, B50 and B100 in which these numbers represent the contents of combination of biofuel and the investigation is carried out from zero to full load condition. The experimental result was found that the B20 blend had improved BTE at all load states compared with the remaining biofuel blends. At 100% load state, BTE (31.5%) and fuel consumption (13.01 g/kW-h) for the B20 blend was closer to diesel. However, the B50 blend had minimal HC (0.04 to 0.157 g/kW-h), CO (0.89 to 2.025 g/kW-h) and smoke (7.8 to 60.09%) emission than other test fuels at low and high load states. The CO emission was the penalty for complete combustion. The NO emission was higher for all the biodiesel blends than diesel by 6.12%, 8%, 11.53%, 14.81% and 3.15% for B20, B30, B40, B50 and B100 respectively at 100% load condition. The reference parameters are identified as blend concentration percentage and brake power values. The trained ANN models exhibit a magnificent value of 97% coefficient of determination and the high R values ranging between 0.9076 and 0.9965 and the low MAPE values ranging between 0.98 and 4.26%. The analytical results also provide supportive evidence for the B20 blend which in turn concludes B20 as an effective alternative fuel for diesel.
本研究专注于通过组合两种低粘性生物燃料来对未改性柴油机进行人工神经网络 (ANN) 建模，以预测排放和性能参数。该柴油机由五种不同的测试燃料提供动力，这些燃料是由香茅和香茅草生物燃料（C50CF50）与柴油按精确比例混合而成的，其中这些数字代表生物燃料的组合含量，研究范围从零到全负荷条件。实验结果表明，与其余生物燃料混合相比，B20 混合燃料在所有负载状态下都提高了 BTE。在 100%负载状态下，B20 混合燃料的 BTE（31.5%）和燃料消耗（13.01 g/kW-h）更接近柴油。然而，在低负荷和高负荷状态下，B50 混合燃料的 HC（0.04 至 0.157 g/kW-h）、CO（0.89 至 2.025 g/kW-h）和烟度（7.8 至 60.09%）排放最低。CO 排放是不完全燃烧的代价。与柴油相比，所有生物柴油混合燃料的 NO 排放都更高，在 100%负载条件下，B20、B30、B40、B50 和 B100 的 NO 排放分别比柴油高 6.12%、8%、11.53%、14.81%和 3.15%。参考参数被确定为混合浓度百分比和制动功率值。训练有素的 ANN 模型表现出 97%的决定系数和 0.9076 到 0.9965 之间的高 R 值以及 0.98 到 4.26%之间的低 MAPE 值。分析结果也为 B20 混合燃料提供了支持性证据，从而得出 B20 是柴油的有效替代燃料的结论。
