Publication: Optimization of transesterification process for Ceiba pentandra oil: A comparative study between kernel-based extreme learning machine and artificial neural networks
Date
2017
Authors
Kusumo F.
Silitonga A.S.
Masjuki H.H.
Ong H.C.
Siswantoro J.
Mahlia T.M.I.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Ltd
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 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
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