Biodiesel production from Calophyllum inophyllum-Ceiba pentandra oil mixture

Optimization and characterization

Hwai Chyuan Ong, Jassinnee Milano, Arridina Susan Silitonga, Masjuki Haji Hassan, Abd Halim Shamsuddin, Chin Tsan Wang, T.m. Indra Mahlia, Joko Siswantoro, Fitranto Kusumo, Joko Sutrisno

Research output: Contribution to journalArticle

Abstract

In this study, a novel modeling approach (artificial neural networks (ANN) and ant colony optimization (ACO)) was used to optimize the process variables for alkaline-catalyzed transesterification of CI40CP60 oil mixture (40 wt% of Calophyllum inophyllum oil mixed with 60 wt% of Ceiba pentandra oil) in order to maximize the biodiesel yield. The optimum values of the methanol-to-oil molar ratio, potassium hydroxide catalyst concentration, and reaction time predicted by the ANN-ACO model are 37%, 0.78 wt%, and 153 min, respectively, at a constant reaction temperature and stirring speed of 60 °C and 1000 rpm, respectively. The ANN-ACO model was validated by performing independent experiments to produce the CI40CP60 methyl ester (CICPME) using the optimum transesterification process variables predicted by the ANN-ACO model. There is very good agreement between the average CICPME yield determined from experiments (95.18%) and the maximum CICPME yield predicted by the ANN-ACO model (95.87%) for the same optimum values of process variables, which corresponds to a difference of 0.69%. Even though the ANN-ACO model is only implemented to optimize the transesterification of process variables in this study. It is believed that the model can be used to optimize other biodiesel production processes such as seed oil extraction and acid-catalyzed esterification for various types of biodiesels and biodiesel blends.

Original languageEnglish
Pages (from-to)183-198
Number of pages16
JournalJournal of Cleaner Production
Volume219
DOIs
Publication statusPublished - 10 May 2019

Fingerprint

Ant colony optimization
Biodiesel
artificial neural network
ant
Neural networks
oil
Transesterification
ester
Esters
Potassium hydroxide
Oilseeds
Esterification
hydroxide
methanol
Oils
Oil
Artificial neural network
Methanol
potassium
experiment

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Environmental Science(all)
  • Strategy and Management
  • Industrial and Manufacturing Engineering

Cite this

Ong, H. C., Milano, J., Silitonga, A. S., Hassan, M. H., Shamsuddin, A. H., Wang, C. T., ... Sutrisno, J. (2019). Biodiesel production from Calophyllum inophyllum-Ceiba pentandra oil mixture: Optimization and characterization. Journal of Cleaner Production, 219, 183-198. https://doi.org/10.1016/j.jclepro.2019.02.048
Ong, Hwai Chyuan ; Milano, Jassinnee ; Silitonga, Arridina Susan ; Hassan, Masjuki Haji ; Shamsuddin, Abd Halim ; Wang, Chin Tsan ; Mahlia, T.m. Indra ; Siswantoro, Joko ; Kusumo, Fitranto ; Sutrisno, Joko. / Biodiesel production from Calophyllum inophyllum-Ceiba pentandra oil mixture : Optimization and characterization. In: Journal of Cleaner Production. 2019 ; Vol. 219. pp. 183-198.
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title = "Biodiesel production from Calophyllum inophyllum-Ceiba pentandra oil mixture: Optimization and characterization",
abstract = "In this study, a novel modeling approach (artificial neural networks (ANN) and ant colony optimization (ACO)) was used to optimize the process variables for alkaline-catalyzed transesterification of CI40CP60 oil mixture (40 wt{\%} of Calophyllum inophyllum oil mixed with 60 wt{\%} of Ceiba pentandra oil) in order to maximize the biodiesel yield. The optimum values of the methanol-to-oil molar ratio, potassium hydroxide catalyst concentration, and reaction time predicted by the ANN-ACO model are 37{\%}, 0.78 wt{\%}, and 153 min, respectively, at a constant reaction temperature and stirring speed of 60 °C and 1000 rpm, respectively. The ANN-ACO model was validated by performing independent experiments to produce the CI40CP60 methyl ester (CICPME) using the optimum transesterification process variables predicted by the ANN-ACO model. There is very good agreement between the average CICPME yield determined from experiments (95.18{\%}) and the maximum CICPME yield predicted by the ANN-ACO model (95.87{\%}) for the same optimum values of process variables, which corresponds to a difference of 0.69{\%}. Even though the ANN-ACO model is only implemented to optimize the transesterification of process variables in this study. It is believed that the model can be used to optimize other biodiesel production processes such as seed oil extraction and acid-catalyzed esterification for various types of biodiesels and biodiesel blends.",
author = "Ong, {Hwai Chyuan} and Jassinnee Milano and Silitonga, {Arridina Susan} and Hassan, {Masjuki Haji} and Shamsuddin, {Abd Halim} and Wang, {Chin Tsan} and Mahlia, {T.m. Indra} and Joko Siswantoro and Fitranto Kusumo and Joko Sutrisno",
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Ong, HC, Milano, J, Silitonga, AS, Hassan, MH, Shamsuddin, AH, Wang, CT, Mahlia, TMI, Siswantoro, J, Kusumo, F & Sutrisno, J 2019, 'Biodiesel production from Calophyllum inophyllum-Ceiba pentandra oil mixture: Optimization and characterization', Journal of Cleaner Production, vol. 219, pp. 183-198. https://doi.org/10.1016/j.jclepro.2019.02.048

Biodiesel production from Calophyllum inophyllum-Ceiba pentandra oil mixture : Optimization and characterization. / Ong, Hwai Chyuan; Milano, Jassinnee; Silitonga, Arridina Susan; Hassan, Masjuki Haji; Shamsuddin, Abd Halim; Wang, Chin Tsan; Mahlia, T.m. Indra; Siswantoro, Joko; Kusumo, Fitranto; Sutrisno, Joko.

In: Journal of Cleaner Production, Vol. 219, 10.05.2019, p. 183-198.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Biodiesel production from Calophyllum inophyllum-Ceiba pentandra oil mixture

T2 - Optimization and characterization

AU - Ong, Hwai Chyuan

AU - Milano, Jassinnee

AU - Silitonga, Arridina Susan

AU - Hassan, Masjuki Haji

AU - Shamsuddin, Abd Halim

AU - Wang, Chin Tsan

AU - Mahlia, T.m. Indra

AU - Siswantoro, Joko

AU - Kusumo, Fitranto

AU - Sutrisno, Joko

PY - 2019/5/10

Y1 - 2019/5/10

N2 - In this study, a novel modeling approach (artificial neural networks (ANN) and ant colony optimization (ACO)) was used to optimize the process variables for alkaline-catalyzed transesterification of CI40CP60 oil mixture (40 wt% of Calophyllum inophyllum oil mixed with 60 wt% of Ceiba pentandra oil) in order to maximize the biodiesel yield. The optimum values of the methanol-to-oil molar ratio, potassium hydroxide catalyst concentration, and reaction time predicted by the ANN-ACO model are 37%, 0.78 wt%, and 153 min, respectively, at a constant reaction temperature and stirring speed of 60 °C and 1000 rpm, respectively. The ANN-ACO model was validated by performing independent experiments to produce the CI40CP60 methyl ester (CICPME) using the optimum transesterification process variables predicted by the ANN-ACO model. There is very good agreement between the average CICPME yield determined from experiments (95.18%) and the maximum CICPME yield predicted by the ANN-ACO model (95.87%) for the same optimum values of process variables, which corresponds to a difference of 0.69%. Even though the ANN-ACO model is only implemented to optimize the transesterification of process variables in this study. It is believed that the model can be used to optimize other biodiesel production processes such as seed oil extraction and acid-catalyzed esterification for various types of biodiesels and biodiesel blends.

AB - In this study, a novel modeling approach (artificial neural networks (ANN) and ant colony optimization (ACO)) was used to optimize the process variables for alkaline-catalyzed transesterification of CI40CP60 oil mixture (40 wt% of Calophyllum inophyllum oil mixed with 60 wt% of Ceiba pentandra oil) in order to maximize the biodiesel yield. The optimum values of the methanol-to-oil molar ratio, potassium hydroxide catalyst concentration, and reaction time predicted by the ANN-ACO model are 37%, 0.78 wt%, and 153 min, respectively, at a constant reaction temperature and stirring speed of 60 °C and 1000 rpm, respectively. The ANN-ACO model was validated by performing independent experiments to produce the CI40CP60 methyl ester (CICPME) using the optimum transesterification process variables predicted by the ANN-ACO model. There is very good agreement between the average CICPME yield determined from experiments (95.18%) and the maximum CICPME yield predicted by the ANN-ACO model (95.87%) for the same optimum values of process variables, which corresponds to a difference of 0.69%. Even though the ANN-ACO model is only implemented to optimize the transesterification of process variables in this study. It is believed that the model can be used to optimize other biodiesel production processes such as seed oil extraction and acid-catalyzed esterification for various types of biodiesels and biodiesel blends.

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