The Effect of Multi-Walled Carbon Nanotubes-Additive in Physicochemical Property of Rice Brand Methyl Ester: Optimization Analysis
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 calo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-08-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/17/3291 |
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| author | Fitranto Kusumo T.M.I. Mahlia A.H. Shamsuddin Hwai Chyuan Ong A.R Ahmad Z. Ismail Z.C. Ong A.S. Silitonga |
| author_facet | Fitranto Kusumo T.M.I. Mahlia A.H. Shamsuddin Hwai Chyuan Ong A.R Ahmad Z. Ismail Z.C. Ong A.S. Silitonga |
| author_sort | Fitranto Kusumo |
| collection | DOAJ |
| description | 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 (<i>R</i><sup>2</sup>), 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 (<i>R</i><sup>2</sup> = 0.9808, RMSE = 0.0164, MAPE = 0.0017, AAD = 0.173) compare to RSM (<i>R</i><sup>2</sup> = 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. |
| first_indexed | 2024-04-12T19:56:34Z |
| format | Article |
| id | doaj.art-645350428d244ecea3b1c7105f6fcd8c |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-12T19:56:34Z |
| publishDate | 2019-08-01 |
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| series | Energies |
| spelling | doaj.art-645350428d244ecea3b1c7105f6fcd8c2022-12-22T03:18:39ZengMDPI AGEnergies1996-10732019-08-011217329110.3390/en12173291en12173291The Effect of Multi-Walled Carbon Nanotubes-Additive in Physicochemical Property of Rice Brand Methyl Ester: Optimization AnalysisFitranto Kusumo0T.M.I. Mahlia1A.H. Shamsuddin2Hwai Chyuan Ong3A.R Ahmad4Z. Ismail5Z.C. Ong6A.S. Silitonga7Institute of Sustainable Energy, Universiti Tenaga Nasional, Kajang 43000, Selangor, MalaysiaSchool of Information, Systems and Modelling, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, AustraliaInstitute of Sustainable Energy, Universiti Tenaga Nasional, Kajang 43000, Selangor, MalaysiaDepartment of Mechanical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, MalaysiaDepartment of Computer Science & Information Technology, College of Computer Science & Information Technology Universiti Tenaga Nasional, Kajang 43000, Selangor, MalaysiaDepartment of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, MalaysiaDepartment of Mechanical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, MalaysiaInstitute of Sustainable Energy, Universiti Tenaga Nasional, Kajang 43000, Selangor, MalaysiaBiodiesel 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 (<i>R</i><sup>2</sup>), 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 (<i>R</i><sup>2</sup> = 0.9808, RMSE = 0.0164, MAPE = 0.0017, AAD = 0.173) compare to RSM (<i>R</i><sup>2</sup> = 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.https://www.mdpi.com/1996-1073/12/17/3291optimizationrice bran biodieselmulti-walled carbon nanotubeadditiveresponse surface methodologyartificial neural networkalternative fuel |
| spellingShingle | Fitranto Kusumo T.M.I. Mahlia A.H. Shamsuddin Hwai Chyuan Ong A.R Ahmad Z. Ismail Z.C. Ong A.S. Silitonga The Effect of Multi-Walled Carbon Nanotubes-Additive in Physicochemical Property of Rice Brand Methyl Ester: Optimization Analysis Energies optimization rice bran biodiesel multi-walled carbon nanotube additive response surface methodology artificial neural network alternative fuel |
| title | The Effect of Multi-Walled Carbon Nanotubes-Additive in Physicochemical Property of Rice Brand Methyl Ester: Optimization Analysis |
| title_full | The Effect of Multi-Walled Carbon Nanotubes-Additive in Physicochemical Property of Rice Brand Methyl Ester: Optimization Analysis |
| title_fullStr | The Effect of Multi-Walled Carbon Nanotubes-Additive in Physicochemical Property of Rice Brand Methyl Ester: Optimization Analysis |
| title_full_unstemmed | The Effect of Multi-Walled Carbon Nanotubes-Additive in Physicochemical Property of Rice Brand Methyl Ester: Optimization Analysis |
| title_short | The Effect of Multi-Walled Carbon Nanotubes-Additive in Physicochemical Property of Rice Brand Methyl Ester: Optimization Analysis |
| title_sort | effect of multi walled carbon nanotubes additive in physicochemical property of rice brand methyl ester optimization analysis |
| topic | optimization rice bran biodiesel multi-walled carbon nanotube additive response surface methodology artificial neural network alternative fuel |
| url | https://www.mdpi.com/1996-1073/12/17/3291 |
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