Optimization of transesterification process for Ceiba pentandra oil

A comparative study between kernel-based extreme learning machine and artificial neural networks

F. Kusumo, A. S. Silitonga, H. H. Masjuki, Hwai Chyuan Ong, J. Siswantoro, T.m. Indra Mahlia

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

In this study, kernel-based extreme learning machine (K-ELM) and artificial neural network (ANN) models were developed in order to predict the conditions of an alkaline-catalysed transesterification process. The reliability of these models was assessed and compared based on the coefficient of determination (R2), root mean squared error (RSME), mean average percent error (MAPE) and relative percent deviation (RPD). The K-ELM model had higher R2 (0.991) and lower RSME, MAPE and RPD (0.688, 0.388 and 0.380) compared to the ANN model (0.984, 0.913, 0.640 and 0.634). Based on these results, the K-ELM model is a more reliable prediction model and it was integrated with ant colony optimization (ACO) in order to achieve the highest Ceiba pentandra methyl ester yield. The optimum molar ratio of methanol to oil, KOH catalyst weight, reaction temperature, reaction time and agitation speed predicted by the K-ELM model integrated with ACO was 10:1, 1 %wt, 60 °C, 108 min and 1100 rpm, respectively. The Ceiba pentandra methyl ester yield attained under these optimum conditions was 99.80%. This novel integrated model provides insight on the effect of parameters investigated on the methyl ester yield, which may be useful for industries involved in biodiesel production.

Original languageEnglish
Pages (from-to)24-34
Number of pages11
JournalEnergy
Volume134
DOIs
Publication statusPublished - 01 Jan 2017

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Transesterification
Learning systems
Neural networks
Esters
Ant colony optimization
Oils
Biodiesel
Methanol
Catalysts

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction
  • Pollution
  • Energy(all)
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

Cite this

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abstract = "In this study, kernel-based extreme learning machine (K-ELM) and artificial neural network (ANN) models were developed in order to predict the conditions of an alkaline-catalysed transesterification process. The reliability of these models was assessed and compared based on the coefficient of determination (R2), root mean squared error (RSME), mean average percent error (MAPE) and relative percent deviation (RPD). The K-ELM model had higher R2 (0.991) and lower RSME, MAPE and RPD (0.688, 0.388 and 0.380) compared to the ANN model (0.984, 0.913, 0.640 and 0.634). Based on these results, the K-ELM model is a more reliable prediction model and it was integrated with ant colony optimization (ACO) in order to achieve the highest Ceiba pentandra methyl ester yield. The optimum molar ratio of methanol to oil, KOH catalyst weight, reaction temperature, reaction time and agitation speed predicted by the K-ELM model integrated with ACO was 10:1, 1 {\%}wt, 60 °C, 108 min and 1100 rpm, respectively. The Ceiba pentandra methyl ester yield attained under these optimum conditions was 99.80{\%}. This novel integrated model provides insight on the effect of parameters investigated on the methyl ester yield, which may be useful for industries involved in biodiesel production.",
author = "F. Kusumo and Silitonga, {A. S.} and Masjuki, {H. H.} and Ong, {Hwai Chyuan} and J. Siswantoro and Mahlia, {T.m. Indra}",
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Optimization of transesterification process for Ceiba pentandra oil : A comparative study between kernel-based extreme learning machine and artificial neural networks. / Kusumo, F.; Silitonga, A. S.; Masjuki, H. H.; Ong, Hwai Chyuan; Siswantoro, J.; Mahlia, T.m. Indra.

In: Energy, Vol. 134, 01.01.2017, p. 24-34.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Optimization of transesterification process for Ceiba pentandra oil

T2 - A comparative study between kernel-based extreme learning machine and artificial neural networks

AU - Kusumo, F.

AU - Silitonga, A. S.

AU - Masjuki, H. H.

AU - Ong, Hwai Chyuan

AU - Siswantoro, J.

AU - Mahlia, T.m. Indra

PY - 2017/1/1

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AB - In this study, kernel-based extreme learning machine (K-ELM) and artificial neural network (ANN) models were developed in order to predict the conditions of an alkaline-catalysed transesterification process. The reliability of these models was assessed and compared based on the coefficient of determination (R2), root mean squared error (RSME), mean average percent error (MAPE) and relative percent deviation (RPD). The K-ELM model had higher R2 (0.991) and lower RSME, MAPE and RPD (0.688, 0.388 and 0.380) compared to the ANN model (0.984, 0.913, 0.640 and 0.634). Based on these results, the K-ELM model is a more reliable prediction model and it was integrated with ant colony optimization (ACO) in order to achieve the highest Ceiba pentandra methyl ester yield. The optimum molar ratio of methanol to oil, KOH catalyst weight, reaction temperature, reaction time and agitation speed predicted by the K-ELM model integrated with ACO was 10:1, 1 %wt, 60 °C, 108 min and 1100 rpm, respectively. The Ceiba pentandra methyl ester yield attained under these optimum conditions was 99.80%. This novel integrated model provides insight on the effect of parameters investigated on the methyl ester yield, which may be useful for industries involved in biodiesel production.

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