Title

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

Publication Name

Renewable Energy

Abstract

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%.

Open Access Status

This publication is not available as open access

Volume

187

First Page

760

Last Page

773

Funding Number

UPM.RMC.800-3/3/1/GPB/2021/9696400

Funding Sponsor

Universiti Putra Malaysia

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Link to publisher version (DOI)

http://dx.doi.org/10.1016/j.renene.2022.01.123