The Effect of Multi-Walled Carbon Nanotubes-Additive in Physicochemical Property of Rice Brand Methyl Ester: Optimization Analysis

Fitranto Kusumo, T.M.I. Mahlia, A.H. Shamsuddin, Hwai Chyuan Ong, Ahmad A.r, Z. Ismail, Z.C. Ong and A.S. Silitonga
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Fitranto Kusumo: Institute of Sustainable Energy, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia
T.M.I. Mahlia: School of Information, Systems and Modelling, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
A.H. Shamsuddin: Institute of Sustainable Energy, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia
Hwai Chyuan Ong: Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
Ahmad A.r: Department of Computer Science & Information Technology, College of Computer Science & Information Technology Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia
Z. Ismail: Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
Z.C. Ong: Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
A.S. Silitonga: Institute of Sustainable Energy, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia

Energies, 2019, vol. 12, issue 17, 1-19

Abstract: Biodiesel as an alternative to diesel fuel produced from vegetable oils or animal fats has attracted more and more attention because it is renewable and environmentally friendly. Compared to conventional diesel fuel, biodiesel has slightly lower performance in engine combustion due to the lower calorific value that leads to lower power generated. This study investigates the effect of multi-walled carbon nanotubes (MWCNTs) as an additive to the rice bran methyl ester (RBME). Artificial neural network (ANN) and response surface methodology (RSM) was used for predicting the calorific value. The interaction effects of parameters such as dosage of MWCNTs, size of MWCNTs and reaction time on the calorific value of RBME were studied. Comparison of RSM and ANN performance was evaluated based on the correlation coefficient ( R 2 ), the root mean square error (RMSE), the mean absolute percentage error (MAPE), and the average absolute deviation (AAD) showed that the ANN model had better performance ( R 2 = 0.9808, RMSE = 0.0164, MAPE = 0.0017, AAD = 0.173) compare to RSM ( R 2 = 0.9746, RMSE = 0.0170, MAPE = 0.0028, AAD = 0.279). The optimum predicted of RBME calorific value that is generated using the cuckoo search (CS) via lévy flight optimization algorithm is 41.78 (MJ/kg). The optimum value was obtained using 64 ppm of < 7 nm MWCNTs blending for 60 min. The predicted calorific value was validated experimentally as 41.05 MJ/kg. Furthermore, the experimental results have shown that the addition of MWCNTs was significantly increased the calorific value from 36.87 MJ/kg to 41.05 MJ/kg (11.6%). Also, the addition of MWCNTs decreased flashpoint (−18.3%) and acid value (−0.52%). As a conclusion, adding MWCNTs as an additive had improved the physicochemical properties characteristics of RBME. To our best knowledge, no research has yet been performed on the effect of multi-walled carbon nanotubes-additive in physicochemical property of rice brand methyl ester application so far.

Keywords: optimization; rice bran biodiesel; multi-walled carbon nanotube; additive; response surface methodology; artificial neural network; alternative fuel (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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