Evaluation of the engine performance and exhaust emissions of biodiesel-bioethanol-diesel blends using kernel-based extreme learning machine

A. S. Silitonga, H. H. Masjuki, Hwai Chyuan Ong, A. H. Sebayang, S. Dharma, F. Kusumo, J. Siswantoro, Jassinnee Milano, Khairil Daud, T.m. Indra Mahlia, Wei Hsin Chen, Bambang Sugiyanto

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1075-1087
Number of pages13
JournalEnergy
Volume159
DOIs
Publication statusPublished - 15 Sep 2018

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Bioethanol
Biodiesel
Brakes
Learning systems
Engines
Fuel consumption
Opacity
Smoke
Carbon monoxide
Nitrogen oxides
Engine cylinders
Ignition
Alternative fuels
Diesel engines
Compaction
Hot Temperature

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction
  • Pollution
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

Cite this

Silitonga, A. S., Masjuki, H. H., Ong, H. C., Sebayang, A. H., Dharma, S., Kusumo, F., ... Sugiyanto, B. (2018). Evaluation of the engine performance and exhaust emissions of biodiesel-bioethanol-diesel blends using kernel-based extreme learning machine. Energy, 159, 1075-1087. https://doi.org/10.1016/j.energy.2018.06.202
Silitonga, A. S. ; Masjuki, H. H. ; Ong, Hwai Chyuan ; Sebayang, A. H. ; Dharma, S. ; Kusumo, F. ; Siswantoro, J. ; Milano, Jassinnee ; Daud, Khairil ; Mahlia, T.m. Indra ; Chen, Wei Hsin ; Sugiyanto, Bambang. / Evaluation of the engine performance and exhaust emissions of biodiesel-bioethanol-diesel blends using kernel-based extreme learning machine. In: Energy. 2018 ; Vol. 159. pp. 1075-1087.
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abstract = "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.",
author = "Silitonga, {A. S.} and Masjuki, {H. H.} and Ong, {Hwai Chyuan} and Sebayang, {A. H.} and S. Dharma and F. Kusumo and J. Siswantoro and Jassinnee Milano and Khairil Daud and Mahlia, {T.m. Indra} and Chen, {Wei Hsin} and Bambang Sugiyanto",
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Silitonga, AS, Masjuki, HH, Ong, HC, Sebayang, AH, Dharma, S, Kusumo, F, Siswantoro, J, Milano, J, Daud, K, Mahlia, TMI, Chen, WH & Sugiyanto, B 2018, 'Evaluation of the engine performance and exhaust emissions of biodiesel-bioethanol-diesel blends using kernel-based extreme learning machine', Energy, vol. 159, pp. 1075-1087. https://doi.org/10.1016/j.energy.2018.06.202

Evaluation of the engine performance and exhaust emissions of biodiesel-bioethanol-diesel blends using kernel-based extreme learning machine. / Silitonga, A. S.; Masjuki, H. H.; Ong, Hwai Chyuan; Sebayang, A. H.; Dharma, S.; Kusumo, F.; Siswantoro, J.; Milano, Jassinnee; Daud, Khairil; Mahlia, T.m. Indra; Chen, Wei Hsin; Sugiyanto, Bambang.

In: Energy, Vol. 159, 15.09.2018, p. 1075-1087.

Research output: Contribution to journalArticle

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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. Indra

AU - Chen, Wei Hsin

AU - Sugiyanto, Bambang

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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