The effect of multi-walled carbon nanotubes-additive in physicochemical property of rice brand methyl ester: Optimization analysis
Additives; Alternative fuels; Biodiesel; Blending; Calorific value; Diesel engines; Diesel fuels; Esters; Fuel additives; Mean square error; Nanotubes; Neural networks; Oils and fats; Optimization; Physicochemical properties; Surface properties; Average absolute deviation; Correlation coefficient; F...
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my.uniten.dspace-244942023-05-29T15:23:58Z The effect of multi-walled carbon nanotubes-additive in physicochemical property of rice brand methyl ester: Optimization analysis Kusumo F. Mahlia T.M.I. Shamsuddin A.H. Ong H.C. Ahmad A.R. Ismail Z. Ong Z.C. Silitonga A.S. 56611974900 56997615100 35779071900 55310784800 56706969500 7003288472 36508537800 39262559400 Additives; Alternative fuels; Biodiesel; Blending; Calorific value; Diesel engines; Diesel fuels; Esters; Fuel additives; Mean square error; Nanotubes; Neural networks; Oils and fats; Optimization; Physicochemical properties; Surface properties; Average absolute deviation; Correlation coefficient; Flight optimization; Mean absolute percentage error; Optimization analysis; Response surface methodology; Rice brans; Root mean square errors; Multiwalled carbon nanotubes (MWCN) 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 (R2), 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 (R2 = 0.9808, RMSE = 0.0164, MAPE = 0.0017, AAD = 0.173) compare to RSM (R2 = 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 l�vy 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. � 2019 by the authors. Final 2023-05-29T07:23:58Z 2023-05-29T07:23:58Z 2019 Article 10.3390/en12173291 2-s2.0-85071651657 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071651657&doi=10.3390%2fen12173291&partnerID=40&md5=4025b2e5daa68a1deaedf734dfd54cc0 https://irepository.uniten.edu.my/handle/123456789/24494 12 17 3291 All Open Access, Gold, Green MDPI AG Scopus |
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Additives; Alternative fuels; Biodiesel; Blending; Calorific value; Diesel engines; Diesel fuels; Esters; Fuel additives; Mean square error; Nanotubes; Neural networks; Oils and fats; Optimization; Physicochemical properties; Surface properties; Average absolute deviation; Correlation coefficient; Flight optimization; Mean absolute percentage error; Optimization analysis; Response surface methodology; Rice brans; Root mean square errors; Multiwalled carbon nanotubes (MWCN) |
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56611974900 |
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56611974900 Kusumo F. Mahlia T.M.I. Shamsuddin A.H. Ong H.C. Ahmad A.R. Ismail Z. Ong Z.C. Silitonga A.S. |
| format |
Article |
| author |
Kusumo F. Mahlia T.M.I. Shamsuddin A.H. Ong H.C. Ahmad A.R. Ismail Z. Ong Z.C. Silitonga A.S. |
| spellingShingle |
Kusumo F. Mahlia T.M.I. Shamsuddin A.H. Ong H.C. Ahmad A.R. Ismail Z. Ong Z.C. Silitonga A.S. The effect of multi-walled carbon nanotubes-additive in physicochemical property of rice brand methyl ester: Optimization analysis |
| author_sort |
Kusumo F. |
| title |
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_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_sort |
effect of multi-walled carbon nanotubes-additive in physicochemical property of rice brand methyl ester: optimization analysis |
| publisher |
MDPI AG |
| publishDate |
2023 |
| _version_ |
1806425925582061568 |
| score |
13.18916 |