Sajjadi, B. and Davoody, M. and Abdul Raman, Abdul Aziz and Ibrahim, Shaliza (2017) Analysis and Optimization of Ultrasound-Assisted Alkaline Palm Oil Transesterification by RSM and ANN-GA. Chemical Engineering Communications, 204 (3). pp. 365-381. ISSN 0098-6445, DOI https://doi.org/10.1080/00986445.2015.1135427.
Full text not available from this repository.Abstract
In this study, the effects of ultrasound irradiation on transesterification process and characteristics of the synthesized biodiesel were investigated. The study was divided into two parts. In the first part, response surface methodology (RSM) and Central Composite Design (CCD) were employed to design experiments, develop the regression model, and evaluate individual and interactive impacts of five independent operational variables. The obtained results were then predicted by an optimized artificial neural network-genetic algorithm (ANN-GA) algorithm. The estimated results were compared with the experimental results. In the second part of the work, the impact of ultrasound irradiation on the main characteristics of the synthesized biodiesel was investigated. The analysis of the operating conditions indicated that reaction temperature and MeOH:oil molar ratio were the most important variables on reaction yield. The experimental results showed that there was a change in the main properties of the synthesized palm oil biodiesel with the density changed by about 0.3 kg/m3, viscosity by 0.12 mm2/s, pour/cloud point by 1–2°C, and flash point by 5°C, depending on different combinations of operational parameters. Besides, the numerical optimization technique was employed to optimize process variables in order to obtain the maximum FAME content (reaction yield) along with the best properties using both RSM and ANN-GA techniques. The maximum reaction yields of 95.2% and 95.1% were predicted by the RSM and ANN-GA models, respectively, at the optimum conditions. The conditions predicted by RSM and ANN-GA proved to be feasible for modeling and optimizing transesterfication yield with an accuracy of 99.18% and 99.14% and biodiesel properties of 98.61% and 98.28%, respectively.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Artificial neural networks; Biodiesel; Characteristics; Genetic algorithm; Response surface methodology; Transesterification; Ultrasound |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) T Technology > TP Chemical technology |
| Divisions: | Faculty of Engineering > Department of Chemical Engineering Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 05 Jul 2017 03:48 |
| Last Modified: | 09 May 2019 04:37 |
| URI: | http://eprints.um.edu.my/id/eprint/17435 |
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