Optimization of transesterification process for Ceiba pentandra oil: A comparative study between kernel-based extreme learning machine and artificial neural networks
Ant colony optimization; Artificial intelligence; Biodiesel; Esters; Knowledge acquisition; Mean square error; Neural networks; Transesterification; Ant Colony Optimization (ACO); Artificial neural network models; Ceiba pentandra oil; Coefficient of determination; Effect of parameters; Extreme learn...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Elsevier Ltd
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.uniten.dspace-23442 |
|---|---|
| record_format |
dspace |
| spelling |
my.uniten.dspace-234422023-05-29T14:40:32Z 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. 56611974900 39262559400 57175108000 55310784800 56192714800 56997615100 Ant colony optimization; Artificial intelligence; Biodiesel; Esters; Knowledge acquisition; Mean square error; Neural networks; Transesterification; Ant Colony Optimization (ACO); Artificial neural network models; Ceiba pentandra oil; Coefficient of determination; Effect of parameters; Extreme learning machine; Root mean squared errors; Transesterification process; Learning systems; algorithm; artificial neural network; biofuel; catalysis; chemical reaction; comparative study; machine learning; optimization; vegetable oil; Ceiba pentandra 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 Final 2023-05-29T06:40:31Z 2023-05-29T06:40:31Z 2017 Article 10.1016/j.energy.2017.05.196 2-s2.0-85020281569 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020281569&doi=10.1016%2fj.energy.2017.05.196&partnerID=40&md5=afe31a1529c2c0eb9402cd64f7448baf https://irepository.uniten.edu.my/handle/123456789/23442 134 24 34 All Open Access, Green Elsevier Ltd Scopus |
| 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/ |
| description |
Ant colony optimization; Artificial intelligence; Biodiesel; Esters; Knowledge acquisition; Mean square error; Neural networks; Transesterification; Ant Colony Optimization (ACO); Artificial neural network models; Ceiba pentandra oil; Coefficient of determination; Effect of parameters; Extreme learning machine; Root mean squared errors; Transesterification process; Learning systems; algorithm; artificial neural network; biofuel; catalysis; chemical reaction; comparative study; machine learning; optimization; vegetable oil; Ceiba pentandra |
| author2 |
56611974900 |
| author_facet |
56611974900 Kusumo F. Silitonga A.S. Masjuki H.H. Ong H.C. Siswantoro J. Mahlia T.M.I. |
| 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_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 |
| publisher |
Elsevier Ltd |
| publishDate |
2023 |
| _version_ |
1806425801182150656 |
| score |
13.214268 |