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Artificial neural network method modeling of microwave-assisted esterification of PFAD over mesoporous TiO2‒ZnO catalyst

An artificial neural network (ANN) was employed to predict biodiesel yield through microwave-assisted esterification of palm fatty acid distillate (PFAD) oil over TiO2‒ZnO mesostructured catalyst. The experimental data of biodiesel content (%) was carried out via changing three input factors (i.e. m...

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Main Authors: Soltani, Soroush, Shojaei, Taha Roodbar, Khanian, Nasrin, Shean, Thomas Yaw Choong, Asim, Nilofar, Yue, Zhao
Format: Article
Published: Elsevier 2022
Online Access:http://psasir.upm.edu.my/id/eprint/100390/
https://www.sciencedirect.com/science/article/pii/S0960148122001331
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spelling my.upm.eprints.1003902023-12-26T04:29:04Z http://psasir.upm.edu.my/id/eprint/100390/ Artificial neural network method modeling of microwave-assisted esterification of PFAD over mesoporous TiO2‒ZnO catalyst Soltani, Soroush Shojaei, Taha Roodbar Khanian, Nasrin Shean, Thomas Yaw Choong Asim, Nilofar Yue, Zhao An artificial neural network (ANN) was employed to predict biodiesel yield through microwave-assisted esterification of palm fatty acid distillate (PFAD) oil over TiO2‒ZnO mesostructured catalyst. The experimental data of biodiesel content (%) was carried out via changing three input factors (i.e. methanol:PFAD molar ratio, catalyst concentration, and reaction time). The results indicated that ANN is an appropriate approach for modeling and optimizing fatty acid methyl ester (FAME) yield performed over the microwave-assisted esterification process. The network was trained by five different algorithms (i.e. batch backpropagation (BBP), incremental backpropagation (IBP), Levenberg‒Marquardt (LM), genetic algorithm (GA), and quick propagation (QP)). The evaluation disclosed that the QP algorithm gave the least root mean squared error (RMSE), absolute average deviation (AAD), and the highest determination coefficient (R2) for both training and testing data groups. The confirmation test results of the ANN-based on QP-3-10-1 revealed that the RMSE, AAD, and the highest R2 were 0.741, 0.776, and 0.997, correspondingly. All in all, QP‒3‒10‒1 model offered the best possible mathematical qualities amongst all algorithms. Over this method, the FAME yield was determined at 97.45% (relating to the actual FAME yield of 97.33%) which was attained over 3 wt% mesoporous TiO2‒ZnO catalyst, methanol:PFAD molar ratio of 9:1 within 25 min of operating time. The esterification reaction conditions predicted by ANN showed to be potential for modeling and predicting FAME yield with an extremely well precision of 97.06%. Elsevier 2022-03 Article PeerReviewed Soltani, Soroush and Shojaei, Taha Roodbar and Khanian, Nasrin and Shean, Thomas Yaw Choong and Asim, Nilofar and Yue, Zhao (2022) Artificial neural network method modeling of microwave-assisted esterification of PFAD over mesoporous TiO2‒ZnO catalyst. Renewable Energy, 187. 760 - 773. ISSN 0960-1481 https://www.sciencedirect.com/science/article/pii/S0960148122001331 10.1016/j.renene.2022.01.123
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description An artificial neural network (ANN) was employed to predict biodiesel yield through microwave-assisted esterification of palm fatty acid distillate (PFAD) oil over TiO2‒ZnO mesostructured catalyst. The experimental data of biodiesel content (%) was carried out via changing three input factors (i.e. methanol:PFAD molar ratio, catalyst concentration, and reaction time). The results indicated that ANN is an appropriate approach for modeling and optimizing fatty acid methyl ester (FAME) yield performed over the microwave-assisted esterification process. The network was trained by five different algorithms (i.e. batch backpropagation (BBP), incremental backpropagation (IBP), Levenberg‒Marquardt (LM), genetic algorithm (GA), and quick propagation (QP)). The evaluation disclosed that the QP algorithm gave the least root mean squared error (RMSE), absolute average deviation (AAD), and the highest determination coefficient (R2) for both training and testing data groups. The confirmation test results of the ANN-based on QP-3-10-1 revealed that the RMSE, AAD, and the highest R2 were 0.741, 0.776, and 0.997, correspondingly. All in all, QP‒3‒10‒1 model offered the best possible mathematical qualities amongst all algorithms. Over this method, the FAME yield was determined at 97.45% (relating to the actual FAME yield of 97.33%) which was attained over 3 wt% mesoporous TiO2‒ZnO catalyst, methanol:PFAD molar ratio of 9:1 within 25 min of operating time. The esterification reaction conditions predicted by ANN showed to be potential for modeling and predicting FAME yield with an extremely well precision of 97.06%.
format Article
author Soltani, Soroush
Shojaei, Taha Roodbar
Khanian, Nasrin
Shean, Thomas Yaw Choong
Asim, Nilofar
Yue, Zhao
spellingShingle Soltani, Soroush
Shojaei, Taha Roodbar
Khanian, Nasrin
Shean, Thomas Yaw Choong
Asim, Nilofar
Yue, Zhao
Artificial neural network method modeling of microwave-assisted esterification of PFAD over mesoporous TiO2‒ZnO catalyst
author_facet Soltani, Soroush
Shojaei, Taha Roodbar
Khanian, Nasrin
Shean, Thomas Yaw Choong
Asim, Nilofar
Yue, Zhao
author_sort Soltani, Soroush
title Artificial neural network method modeling of microwave-assisted esterification of PFAD over mesoporous TiO2‒ZnO catalyst
title_short Artificial neural network method modeling of microwave-assisted esterification of PFAD over mesoporous TiO2‒ZnO catalyst
title_full Artificial neural network method modeling of microwave-assisted esterification of PFAD over mesoporous TiO2‒ZnO catalyst
title_fullStr Artificial neural network method modeling of microwave-assisted esterification of PFAD over mesoporous TiO2‒ZnO catalyst
title_full_unstemmed Artificial neural network method modeling of microwave-assisted esterification of PFAD over mesoporous TiO2‒ZnO catalyst
title_sort artificial neural network method modeling of microwave-assisted esterification of pfad over mesoporous tio2‒zno catalyst
publisher Elsevier
publishDate 2022
url http://psasir.upm.edu.my/id/eprint/100390/
https://www.sciencedirect.com/science/article/pii/S0960148122001331
_version_ 1787137196581453824
score 13.211869

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