Optimization of transesterification process for Ceiba pentandra oil: A comparative study between kernel-based extreme learning machine and artificial neural networks

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 determ...

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Main Authors: Kusumo, F., Silitonga, A.S., Masjuki, H.H., Ong, H.C., Siswantoro, J., Mahlia, T.M.I.
Format: Article
Language:en_US
Published: 2017
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spelling my.uniten.dspace-60832018-03-19T03:44:15Z 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, H.C. Siswantoro, J. Mahlia, T.M.I. 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. © 2017 Elsevier Ltd 2017-12-08T09:11:14Z 2017-12-08T09:11:14Z 2017 Article 10.1016/j.energy.2017.05.196 en_US A comparative study between kernel-based extreme learning machine and artificial neural networks. Energy, 134, 24-34
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language en_US
description 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. © 2017 Elsevier Ltd
format Article
author Kusumo, F.
Silitonga, A.S.
Masjuki, H.H.
Ong, H.C.
Siswantoro, J.
Mahlia, T.M.I.
spellingShingle Kusumo, F.
Silitonga, A.S.
Masjuki, H.H.
Ong, H.C.
Siswantoro, J.
Mahlia, T.M.I.
Optimization of transesterification process for Ceiba pentandra oil: A comparative study between kernel-based extreme learning machine and artificial neural networks
author_facet Kusumo, F.
Silitonga, A.S.
Masjuki, H.H.
Ong, H.C.
Siswantoro, J.
Mahlia, T.M.I.
author_sort Kusumo, F.
title Optimization of transesterification process for Ceiba pentandra oil: A comparative study between kernel-based extreme learning machine and artificial neural networks
title_short Optimization of transesterification process for Ceiba pentandra oil: A comparative study between kernel-based extreme learning machine and artificial neural networks
title_full Optimization of transesterification process for Ceiba pentandra oil: A comparative study between kernel-based extreme learning machine and artificial neural networks
title_fullStr Optimization of transesterification process for Ceiba pentandra oil: A comparative study between kernel-based extreme learning machine and artificial neural networks
title_full_unstemmed Optimization of transesterification process for Ceiba pentandra oil: A comparative study between kernel-based extreme learning machine and artificial neural networks
title_sort optimization of transesterification process for ceiba pentandra oil: a comparative study between kernel-based extreme learning machine and artificial neural networks
publishDate 2017
_version_ 1644493837670809600
score 13.214268