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Elemental, Thermal and Physicochemical Investigation of Novel Biodiesel from Wodyetia Bifurcata and Its Properties Optimization using Artificial Neural Network (ANN)

Automotive Experiences · Vol. 5 No. 1 (2021) · hlm. 3–15

DOI
10.31603/ae.6171
ISSN
2615-6636, 2615-6202
Terbit
2021-11-25
Penerbit
Universitas Muhammadiyah Magelang
Bahasa
en

Abstrak

In this study, an unexplored oil from the wodyetia bifurcata fruit was used for biodiesel production. The transesterification process was implemented to convert the raw oil into wodyetia bifurcata methyl ester (WBME) and the influence of process variables on WBME yield was examined with the response surface method (RSM) assisted Box-Behnken optimization. The results of RSM show that a maximum biodiesel yield of 94.67% was achieved and reaction time was identified as an influencing process variable. The fatty acid composition (FAC) from chromatography reveals the presence of highly unsaturated in WBME and the significant fuel properties of thermal and molecular meet the required fuel standards (ASTM). The obtained fuel properties of WBME are compared with other popularly used biodiesels and observed low kinematic viscosity (3.87mm2/sec) and moderated cetane number (53) for WBME. Furthermore, artificial neural network (ANN) tools are used for the prediction of WBME yield and show an improvement of 0.4% than RSM and low mean square error and a high coefficient of correlation was observed for ANN.

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Muhammad Latifur RochmanAfiliasi tidak diketahui9
Dori YuvendaAfiliasi tidak diketahui6
Budi WaluyoWalailak University6
Thirunavukkarasu RajaAfiliasi tidak diketahui4
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Asep Bayu Dani NandiyantoAfiliasi tidak diketahui4
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Retno RusdjijatiAfiliasi tidak diketahui3
Olusegun David SamuelAfiliasi tidak diketahui3
Rifky IsmailAfiliasi tidak diketahui3
Djoko Wahyu KarmiadjiBadan Pengkajian dan Penerapan Teknologi2
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Dwi Ahmad RizaniAfiliasi tidak diketahui1
Eko Muh WidodoSTIKOM Tunas Bangsa1
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Sumber metadata: OAI-PMH (metadata jurnal) http://journal.unimma.ac.id/index.php/AutomotiveExperiences/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Elemental, Thermal and Physicochemical Investigation of Novel Biodiesel from Wodyetia Bifurcata and Its Properties Optimization using Artificial Neural Network (ANN)</dc:title>
	<dc:creator xml:lang="en">Kolakoti, Aditya</dc:creator>
	<dc:creator xml:lang="en">Prasadarao, Bobbili</dc:creator>
	<dc:creator xml:lang="en">Satyanarayana, Katakam</dc:creator>
	<dc:creator xml:lang="en">Setiyo, Muji</dc:creator>
	<dc:creator xml:lang="en">Köten, Hasan</dc:creator>
	<dc:creator xml:lang="en">Raghu, Metta</dc:creator>
	<dc:subject xml:lang="en">Wodyetia Bifurcata</dc:subject>
	<dc:subject xml:lang="en">Biodiesel</dc:subject>
	<dc:subject xml:lang="en">Foxtail tree</dc:subject>
	<dc:subject xml:lang="en">Fuel properties</dc:subject>
	<dc:subject xml:lang="en">ANN</dc:subject>
	<dc:subject xml:lang="en">RSM</dc:subject>
	<dc:description xml:lang="en">In this study, an unexplored oil from the wodyetia bifurcata fruit was used for biodiesel production. The transesterification process was implemented to convert the raw oil into wodyetia bifurcata methyl ester (WBME) and the influence of process variables on WBME yield was examined with the response surface method (RSM) assisted Box-Behnken optimization. The results of RSM show that a maximum biodiesel yield of 94.67% was achieved and reaction time was identified as an influencing process variable. The fatty acid composition (FAC) from chromatography reveals the presence of highly unsaturated in WBME and the significant fuel properties of thermal and molecular meet the required fuel standards (ASTM). The obtained fuel properties of WBME are compared with other popularly used biodiesels and observed low kinematic viscosity (3.87mm2/sec) and moderated cetane number (53) for WBME. Furthermore, artificial neural network (ANN) tools are used for the prediction of WBME yield and show an improvement of 0.4% than RSM and low mean square error and a high coefficient of correlation was observed for ANN.</dc:description>
	<dc:publisher xml:lang="en">Universitas Muhammadiyah Magelang</dc:publisher>
	<dc:date>2021-11-25</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unimma.ac.id/AutomotiveExperiences/article/view/6171</dc:identifier>
	<dc:identifier>10.31603/ae.6171</dc:identifier>
	<dc:source xml:lang="en">Automotive Experiences; Vol. 5 No. 1 (2022); 3-15</dc:source>
	<dc:source>2615-6636</dc:source>
	<dc:source>2615-6202</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unimma.ac.id/AutomotiveExperiences/article/view/6171/2841</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2021 Aditya Kolakoti, Bobbili Prasadarao, Katakam Satyanarayana, Muji Setiyo, Hasan Köten, Metta Metta</dc:rights>
</oai_dc:dc>