Thermal Behavior Prediction of MDPE Nanocomposite/Cloisite Na+ Using Artificial Neural Network and Neuro-Fuzzy Tools

Sargolzaei, Javad; Ahangari, Behdad

doi:10.1115/1.4002703

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…At the moment, it was cultivated and naturalized all over Mediterranean region since ancient times. The edible part of the pomegranate is called aril and constitutes about 52% w/w of the total fruit and comprises of 78% w/w juice and 22% w/w seeds [2,3].…”

Section: Introductionmentioning

confidence: 99%

Extraction of pomegranate seed oil using subcritical propane and supercritical carbon dioxide

Ahangari

Sargolzaei

2012

Theor Found Chem Eng

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Extraction of oil from pomegranate seeds as a waste product of the juice industry using supercrit ical carbon dioxide and subcritical propane was studied in this work. The influence of the main operating con ditions of extraction, namely, the temperature and pressure of extraction on the oil extraction yield and the correspondent fatty acid profile were analyzed and reported here. Soxhlet extraction with n-hexane was done for comparison with supercritical extraction resulting in a maximum yield of oil of 22.31 wt %. Supercritical car bon dioxide and subcritical propane extracted up to 58.53% (corresponding to maximum yield of 13.06 wt %) and 76.73% (corresponding to maximum yield of 17.12 wt %) of the total amount of pomegranate seed oil as measured using Soxhlet extraction, respectively. Results indicated that the subcritical propane is a suitable and selective solvent for the extraction of the pomegranate seed oil in function of smaller times and pressures employed compared to carbon dioxide extraction. The fatty acid composition of the extracted oil showed the presence of fatty acids of C 16 , C 18 , C 20 , C 22 , and C 24 carbon chains. Punicic (C 18.3 ) was the major fatty acids and comprise up to 70% of the total fatty acid content of the extracted oil among all samples. Finally, Lack's plug flow model as developed by Sovova was applied to both extraction systems and a good agreement with the experimental results was obtained.

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Section: Introductionmentioning

confidence: 99%

Extraction of pomegranate seed oil using subcritical propane and supercritical carbon dioxide

Ahangari

Sargolzaei

2012

Theor Found Chem Eng

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“…Recently, some researchers have attempted to model the TGA test results on a variety of composites using artificial neural networks. For example, some researcher in 2010 successfully prepared medium density polyethylene (MDPE) nanocomposite with 3 wt%, 6 wt%, and 9 wt% cloisite Na_, and the thermal stability of nanocomposite was investigated using the thermogravimetric analysis (TGA) [ 13 ]. The TGA in air atmosphere showed significantly improved thermal stability of 3 wt%, 6 wt%, and 9 wt% cloisite Na_ nanocomposite in comparison to pure MDPE.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Impact of Multiwalled Carbon Nanotubes on the Cement Hydration Products and Durability of Cementitious Matrix Using Artificial Neural Network Modeling Technique

Fakhim

Hassani

Rashidi³

et al. 2013

The Scientific World Journal

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In this study the feasibility of using the artificial neural networks modeling in predicting the effect of MWCNT on amount of cement hydration products and improving the quality of cement hydration products microstructures of cement paste was investigated. To determine the amount of cement hydration products thermogravimetric analysis was used. Two critical parameters of TGA test are PHPloss and CHloss. In order to model the TGA test results, the ANN modeling was performed on these parameters separately. In this study, 60% of data are used for model calibration and the remaining 40% are used for model verification. Based on the highest efficiency coefficient and the lowest root mean square error, the best ANN model was chosen. The results of TGA test implied that the cement hydration is enhanced in the presence of the optimum percentage (0.3 wt%) of MWCNT. Moreover, since the efficiency coefficient of the modeling results of CH and PHP loss in both the calibration and verification stages was more than 0.96, it was concluded that the ANN could be used as an accurate tool for modeling the TGA results. Another finding of this study was that the ANN prediction in higher ages was more precise.

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ANFIS-based prediction of moment capacity of reinforced concrete slabs exposed to fire

Bilgehan

Kurtoğlu

2015

Neural Comput & Applic

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Thermal Behavior Prediction of MDPE Nanocomposite/Cloisite Na+ Using Artificial Neural Network and Neuro-Fuzzy Tools

Cited by 4 publications

References 27 publications

Extraction of pomegranate seed oil using subcritical propane and supercritical carbon dioxide

Extraction of pomegranate seed oil using subcritical propane and supercritical carbon dioxide

Predicting the Impact of Multiwalled Carbon Nanotubes on the Cement Hydration Products and Durability of Cementitious Matrix Using Artificial Neural Network Modeling Technique

ANFIS-based prediction of moment capacity of reinforced concrete slabs exposed to fire

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