Modelling and prediction approach for engine performance and exhaust emission based on artificial intelligence of sterculia foetida biodiesel

Sebayang, AH; Milano, J; Shamsuddin, AH; Alfansuri, M; Silitonga, AS; Kusumo, F; Prahmana, RA; Fayaz, H; Zamri, MFMA

Modelling and prediction approach for engine performance and exhaust emission based on artificial intelligence of sterculia foetida biodiesel

Sebayang, AH Milano, J Shamsuddin, AH Alfansuri, M Silitonga, AS Kusumo, F

Prahmana, RA Fayaz, H Zamri, MFMA

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Energy Reports, 2022, 8, pp. 8333-8345
Issue Date:: 2022-11-01

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Field	Value	Language
dc.contributor.author	Sebayang, AH
dc.contributor.author	Milano, J
dc.contributor.author	Shamsuddin, AH
dc.contributor.author	Alfansuri, M
dc.contributor.author	Silitonga, AS
dc.contributor.author	Kusumo, F https://orcid.org/0000-0002-6264-1132
dc.contributor.author	Prahmana, RA
dc.contributor.author	Fayaz, H
dc.contributor.author	Zamri, MFMA
dc.date.accessioned	2023-02-02T00:49:42Z
dc.date.available	2023-02-02T00:49:42Z
dc.date.issued	2022-11-01
dc.identifier.citation	Energy Reports, 2022, 8, pp. 8333-8345
dc.identifier.issn	2352-4847
dc.identifier.issn	2352-4847
dc.identifier.uri	http://hdl.handle.net/10453/165824
dc.description.abstract	Sterculia foetida derived biodiesel is a potential fuel for a diesel engine. The Sterculia foetida biodiesel required a pre-refining process called degumming and an acid pretreatment process before converting them to methyl ester using the transesterification process. This study blended fuel from Sterculia foetida biodiesel and diesel with different volume ratios (5% to 30% of biodiesel blend with 95% to 70% diesel fuel). Sterculia foetida biodiesel and blended fuels met the ASTM D6751 and EN 14214 standards. The blended fuel is examined to obtain its influences on the performance and emission when operating at a diesel engine (1300 rpm to 2400 rpm). From the outcome, the engine performance of the SFB5 blend shows better performance than diesel fuel in terms of BTE (28.84%) and BSFC (5.86%). Artificial neural networks and extreme learning machines were employed to predict engine performance and exhaust emissions. The developed models gave excellent results, where the coefficient of determination is more than 99% and 98% for BSFC and BTE, respectively. When the engine is operated with SFB5, there is a significant reduction in CO, HC, and smoke opacity emissions by 8.26%, 2.08%, and 3.08%, respectively, and at the same time, an increase in CO2 by 3.53% and NOX by 22.39%. The comparison is made with diesel fuel. The extreme learning machine modelling is powerful for predicting engine performance and exhaust emission compared to artificial neural networks in terms of prediction accuracy. Sterculia foetida biodiesel–diesel blends of 5% show its capability to replace diesel fuel by providing engine peak performance than diesel fuel.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Energy Reports
dc.relation.isbasedon	10.1016/j.egyr.2022.06.052
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering
dc.title	Modelling and prediction approach for engine performance and exhaust emission based on artificial intelligence of sterculia foetida biodiesel
dc.type	Journal Article
utslib.citation.volume	8
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2023-02-02T00:49:40Z
pubs.publication-status	Published
pubs.volume	8

Abstract:

Sterculia foetida derived biodiesel is a potential fuel for a diesel engine. The Sterculia foetida biodiesel required a pre-refining process called degumming and an acid pretreatment process before converting them to methyl ester using the transesterification process. This study blended fuel from Sterculia foetida biodiesel and diesel with different volume ratios (5% to 30% of biodiesel blend with 95% to 70% diesel fuel). Sterculia foetida biodiesel and blended fuels met the ASTM D6751 and EN 14214 standards. The blended fuel is examined to obtain its influences on the performance and emission when operating at a diesel engine (1300 rpm to 2400 rpm). From the outcome, the engine performance of the SFB5 blend shows better performance than diesel fuel in terms of BTE (28.84%) and BSFC (5.86%). Artificial neural networks and extreme learning machines were employed to predict engine performance and exhaust emissions. The developed models gave excellent results, where the coefficient of determination is more than 99% and 98% for BSFC and BTE, respectively. When the engine is operated with SFB5, there is a significant reduction in CO, HC, and smoke opacity emissions by 8.26%, 2.08%, and 3.08%, respectively, and at the same time, an increase in CO2 by 3.53% and NOX by 22.39%. The comparison is made with diesel fuel. The extreme learning machine modelling is powerful for predicting engine performance and exhaust emission compared to artificial neural networks in terms of prediction accuracy. Sterculia foetida biodiesel–diesel blends of 5% show its capability to replace diesel fuel by providing engine peak performance than diesel fuel.

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http://hdl.handle.net/10453/165824