期刊
ENERGY
卷 134, 期 -, 页码 24-34出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2017.05.196
关键词
Biodiesel; Ceiba pentandra oil; Kernel-based extreme learning machine; Ant colony optimization; Artificial neural network
资金
- Ministry of Education, Malaysia
- University of Malaya, Kuala Lumpur, Malaysia under the SATU Joint Research Scheme [RU021B-2015, PG036-2014B]
- Politeknik Negeri Medan, Medan, North Sumatra, Indonesia, under the Research and Community Service Unit (UPPM)
- Universiti Tenaga Nasional [20160101FRGS]
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 (R-2), root mean squared error (RSME), mean average percent error (MAPE) and relative percent deviation (RPD). The K-ELM model had higher R-2 (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 degrees 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. (C) 2017 Elsevier Ltd. All rights reserved.
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