Biodiesel synthesis from Ceiba pentandra oil by microwave irradiation-assisted transesterification

ELM modeling and optimization

A. S. Silitonga, A. H. Shamsuddin, T.m. Indra Mahlia, Jassinne Milano, F. Kusumo, Joko Siswantoro, S. Dharma, A. H. Sebayang, H. H. Masjuki, Hwai Chyuan Ong

Research output: Contribution to journalArticle

Abstract

In this study, microwave irradiation-assisted transesterification was used to produce Ceiba pentandra biodiesel, which accelerates the rate of reaction and temperature within a shorter period. The improvement of biodiesel production requires a reliable model that accurately reflects the effects of input variables on output variables. In this study, an extreme learning machine integrated with cuckoo search algorithm was developed to predict and optimize the process parameters. This model will be useful for virtual experimentations in order to enhance biodiesel research and development. The optimum parameters of the microwave irradiation-assisted transesterification process conditions were obtained as follows: (1) methanol/oil ratio: 60%, (2) potassium hydroxide catalyst concentration: 0.84%(w/w), (3) stirring speed: 800 rpm, and (4) reaction time: 388 s. The corresponding Ceiba pentandra biodiesel yield was 96.19%. Three independent experiments were conducted using the optimum process parameters and the average biodiesel yield was found to be 95.42%. In conclusion, microwave irradiation-assisted transesterification is an effective method for biodiesel production because it is more energy-efficient, which will reduce the overall cost of biodiesel production.

Original languageEnglish
Pages (from-to)1278-1291
Number of pages14
JournalRenewable Energy
Volume146
DOIs
Publication statusPublished - 01 Feb 2020

Fingerprint

Microwave irradiation
Transesterification
Biodiesel
Potassium hydroxide
Oils
Learning systems
Methanol
Catalysts
Costs

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment

Cite this

Silitonga, A. S. ; Shamsuddin, A. H. ; Mahlia, T.m. Indra ; Milano, Jassinne ; Kusumo, F. ; Siswantoro, Joko ; Dharma, S. ; Sebayang, A. H. ; Masjuki, H. H. ; Ong, Hwai Chyuan. / Biodiesel synthesis from Ceiba pentandra oil by microwave irradiation-assisted transesterification : ELM modeling and optimization. In: Renewable Energy. 2020 ; Vol. 146. pp. 1278-1291.
@article{6bd43aacb04d4e0facb105eee5bdbd62,
title = "Biodiesel synthesis from Ceiba pentandra oil by microwave irradiation-assisted transesterification: ELM modeling and optimization",
abstract = "In this study, microwave irradiation-assisted transesterification was used to produce Ceiba pentandra biodiesel, which accelerates the rate of reaction and temperature within a shorter period. The improvement of biodiesel production requires a reliable model that accurately reflects the effects of input variables on output variables. In this study, an extreme learning machine integrated with cuckoo search algorithm was developed to predict and optimize the process parameters. This model will be useful for virtual experimentations in order to enhance biodiesel research and development. The optimum parameters of the microwave irradiation-assisted transesterification process conditions were obtained as follows: (1) methanol/oil ratio: 60{\%}, (2) potassium hydroxide catalyst concentration: 0.84{\%}(w/w), (3) stirring speed: 800 rpm, and (4) reaction time: 388 s. The corresponding Ceiba pentandra biodiesel yield was 96.19{\%}. Three independent experiments were conducted using the optimum process parameters and the average biodiesel yield was found to be 95.42{\%}. In conclusion, microwave irradiation-assisted transesterification is an effective method for biodiesel production because it is more energy-efficient, which will reduce the overall cost of biodiesel production.",
author = "Silitonga, {A. S.} and Shamsuddin, {A. H.} and Mahlia, {T.m. Indra} and Jassinne Milano and F. Kusumo and Joko Siswantoro and S. Dharma and Sebayang, {A. H.} and Masjuki, {H. H.} and Ong, {Hwai Chyuan}",
year = "2020",
month = "2",
day = "1",
doi = "10.1016/j.renene.2019.07.065",
language = "English",
volume = "146",
pages = "1278--1291",
journal = "Renewable Energy",
issn = "0960-1481",
publisher = "Elsevier BV",

}

Silitonga, AS, Shamsuddin, AH, Mahlia, TMI, Milano, J, Kusumo, F, Siswantoro, J, Dharma, S, Sebayang, AH, Masjuki, HH & Ong, HC 2020, 'Biodiesel synthesis from Ceiba pentandra oil by microwave irradiation-assisted transesterification: ELM modeling and optimization', Renewable Energy, vol. 146, pp. 1278-1291. https://doi.org/10.1016/j.renene.2019.07.065

Biodiesel synthesis from Ceiba pentandra oil by microwave irradiation-assisted transesterification : ELM modeling and optimization. / Silitonga, A. S.; Shamsuddin, A. H.; Mahlia, T.m. Indra; Milano, Jassinne; Kusumo, F.; Siswantoro, Joko; Dharma, S.; Sebayang, A. H.; Masjuki, H. H.; Ong, Hwai Chyuan.

In: Renewable Energy, Vol. 146, 01.02.2020, p. 1278-1291.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Biodiesel synthesis from Ceiba pentandra oil by microwave irradiation-assisted transesterification

T2 - ELM modeling and optimization

AU - Silitonga, A. S.

AU - Shamsuddin, A. H.

AU - Mahlia, T.m. Indra

AU - Milano, Jassinne

AU - Kusumo, F.

AU - Siswantoro, Joko

AU - Dharma, S.

AU - Sebayang, A. H.

AU - Masjuki, H. H.

AU - Ong, Hwai Chyuan

PY - 2020/2/1

Y1 - 2020/2/1

N2 - In this study, microwave irradiation-assisted transesterification was used to produce Ceiba pentandra biodiesel, which accelerates the rate of reaction and temperature within a shorter period. The improvement of biodiesel production requires a reliable model that accurately reflects the effects of input variables on output variables. In this study, an extreme learning machine integrated with cuckoo search algorithm was developed to predict and optimize the process parameters. This model will be useful for virtual experimentations in order to enhance biodiesel research and development. The optimum parameters of the microwave irradiation-assisted transesterification process conditions were obtained as follows: (1) methanol/oil ratio: 60%, (2) potassium hydroxide catalyst concentration: 0.84%(w/w), (3) stirring speed: 800 rpm, and (4) reaction time: 388 s. The corresponding Ceiba pentandra biodiesel yield was 96.19%. Three independent experiments were conducted using the optimum process parameters and the average biodiesel yield was found to be 95.42%. In conclusion, microwave irradiation-assisted transesterification is an effective method for biodiesel production because it is more energy-efficient, which will reduce the overall cost of biodiesel production.

AB - In this study, microwave irradiation-assisted transesterification was used to produce Ceiba pentandra biodiesel, which accelerates the rate of reaction and temperature within a shorter period. The improvement of biodiesel production requires a reliable model that accurately reflects the effects of input variables on output variables. In this study, an extreme learning machine integrated with cuckoo search algorithm was developed to predict and optimize the process parameters. This model will be useful for virtual experimentations in order to enhance biodiesel research and development. The optimum parameters of the microwave irradiation-assisted transesterification process conditions were obtained as follows: (1) methanol/oil ratio: 60%, (2) potassium hydroxide catalyst concentration: 0.84%(w/w), (3) stirring speed: 800 rpm, and (4) reaction time: 388 s. The corresponding Ceiba pentandra biodiesel yield was 96.19%. Three independent experiments were conducted using the optimum process parameters and the average biodiesel yield was found to be 95.42%. In conclusion, microwave irradiation-assisted transesterification is an effective method for biodiesel production because it is more energy-efficient, which will reduce the overall cost of biodiesel production.

UR - http://www.scopus.com/inward/record.url?scp=85068999599&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85068999599&partnerID=8YFLogxK

U2 - 10.1016/j.renene.2019.07.065

DO - 10.1016/j.renene.2019.07.065

M3 - Article

VL - 146

SP - 1278

EP - 1291

JO - Renewable Energy

JF - Renewable Energy

SN - 0960-1481

ER -