TY - JOUR
T1 - Evaluation of the engine performance and exhaust emissions of biodiesel-bioethanol-diesel blends using kernel-based extreme learning machine
AU - Silitonga, A. S.
AU - Masjuki, H. H.
AU - Ong, Hwai Chyuan
AU - Sebayang, A. H.
AU - Dharma, S.
AU - Kusumo, F.
AU - Siswantoro, J.
AU - Milano, Jassinnee
AU - Daud, Khairil
AU - Mahlia, T. M.I.
AU - Chen, Wei Hsin
AU - Sugiyanto, Bambang
N1 - Funding Information:
The authors wish to express their greatest appreciation to Direktorat Jenderal Penguatan Riset dan Pengembangan Kementerian Riset , Teknologi dan Pendidikan Tinggi Republik Indonesia (Hibah Kompetensi HIKOM-2018 ), Politeknik Negeri Medan, Medan, Indonesia , ( UPPM-2018 ) and also the Ministry of Education Malaysia and University of Malaya, Kuala Lumpur Malaysia under FRGS-MRSA ( MO014-2016 ). The authors also graciously acknowledge the financial support provided by Universiti Tenaga Nasional internal grant (UNIIG 2017 No. J510050691 ).
Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2018/9/15
Y1 - 2018/9/15
N2 - It is known that biodiesel and bioethanol are viable alternative fuels to replace diesel for compression ignition engines. In this study, an experimental investigation is carried out to evaluate the performance and exhaust emissions of a single cylinder diesel engine fuelled with biodiesel-bioethanol-diesel blends. The engine performance parameters evaluated are the brake specific fuel consumption and brake thermal efficiency whereas the exhaust emission parameters evaluated are carbon monoxide, nitrogen oxide, and smoke opacity. Kernel-based extreme learning machine is used to predict the engine performance and exhaust emission parameters of the fuel blends at full throttle conditions. Based on the experimental results, the brake specific fuel consumption is lower while the brake thermal efficiency is higher for the biodiesel-bioethanol-diesel blends. The carbon monoxide emissions and smoke opacity are also lower for these fuel blends. The mean absolute percentage error of the brake specific fuel consumption, brake thermal efficiency, carbon monoxide, nitrogen oxide, and smoke opacity is 1.363, 1.482, 4.597, 2.224, and 2.090%, respectively. Thus, it can be concluded that K-ELM is a reliable method to estimate the engine performance and exhaust emission parameters of a single cylinder compression ignition engine fuelled with biodiesel-bioethanol-diesel blends to reduce fuel consumption and exhaust emissions.
AB - It is known that biodiesel and bioethanol are viable alternative fuels to replace diesel for compression ignition engines. In this study, an experimental investigation is carried out to evaluate the performance and exhaust emissions of a single cylinder diesel engine fuelled with biodiesel-bioethanol-diesel blends. The engine performance parameters evaluated are the brake specific fuel consumption and brake thermal efficiency whereas the exhaust emission parameters evaluated are carbon monoxide, nitrogen oxide, and smoke opacity. Kernel-based extreme learning machine is used to predict the engine performance and exhaust emission parameters of the fuel blends at full throttle conditions. Based on the experimental results, the brake specific fuel consumption is lower while the brake thermal efficiency is higher for the biodiesel-bioethanol-diesel blends. The carbon monoxide emissions and smoke opacity are also lower for these fuel blends. The mean absolute percentage error of the brake specific fuel consumption, brake thermal efficiency, carbon monoxide, nitrogen oxide, and smoke opacity is 1.363, 1.482, 4.597, 2.224, and 2.090%, respectively. Thus, it can be concluded that K-ELM is a reliable method to estimate the engine performance and exhaust emission parameters of a single cylinder compression ignition engine fuelled with biodiesel-bioethanol-diesel blends to reduce fuel consumption and exhaust emissions.
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U2 - 10.1016/j.energy.2018.06.202
DO - 10.1016/j.energy.2018.06.202
M3 - Article
AN - SCOPUS:85053081434
SN - 0360-5442
VL - 159
SP - 1075
EP - 1087
JO - Energy
JF - Energy
ER -