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

Kusumo, F and Silitonga, A S and Ong, Hwai Chyuan and Siswantoro, Joko and Mahlia, T M I (2017) Optimization of transesterification process for Ceiba pentandra oil: A comparative study between kernel-based extreme learning machine and artificial neural networks. Energy, 134. pp. 24-34. ISSN 0360-5442

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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 KELM 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 KELM 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.

Item Type: Article
Uncontrolled Keywords: Biodiesel Ceiba pentandra oil Kernel-based extreme learning machine Ant colony optimization Artificial neural network
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TP Chemical technology
Divisions: Faculty of Engineering > Department of Informatic
Depositing User: Joko Siswantoro
Date Deposited: 22 Mar 2018 01:59
Last Modified: 22 Mar 2018 01:59
URI: http://repository.ubaya.ac.id/id/eprint/31881

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