Abstract
Biodiesel total glycerol content is an important characteristic which must pass the EN 14214 and ASTM D6751 international biodiesel quality standards. In this study, the experimental data of glycerol removal by means of deep eutectic solvents (DESs) was used to design a new modeling approach based on Artificial Neural Networks (ANNs) in order to predict glycerol removal. The DESs were synthesized with choline chloride and methyl triphenyl phosphunium bromide as salts and different hydrogen bond donors. DESs composition and the mole fractions of DESs to biodiesel were used as inputs to the model. A feed-forward neural network with 4 hidden neurons was applied and training was done based on the Levenberg-Marquardt optimization method. The ANN prediction was in good agreement with the measured data with an absolute average deviation of 6.46%. The predicted results indicated that the DESs synthesized with glycerol as hydrogen bond donor has lower removal efficiencies. Furthermore, the phosphunium-based DESs were much efficient in attracting total glycerol in comparison with ammonium-based DESs.
| Original language | English |
|---|---|
| Pages (from-to) | 193-199 |
| Number of pages | 7 |
| Journal | Chemometrics and Intelligent Laboratory Systems |
| Volume | 118 |
| DOIs | |
| Publication status | Published - Aug 15 2012 |
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Keywords
- Biodiesel
- Deep eutectic solvent
- Glycerol
- Neural networks
- Removal
ASJC Scopus subject areas
- Analytical Chemistry
- Computer Science Applications
- Software
- Process Chemistry and Technology
- Spectroscopy
Cite this
Prediction of glycerol removal from biodiesel using ammonium and phosphunium based deep eutectic solvents using artificial intelligence techniques. / Shahbaz, Kaveh; Baroutian, Saeid; Mjalli, Farouq Sabri; Hashim, Mohd Ali; AlNashef, Inas Muen.
In: Chemometrics and Intelligent Laboratory Systems, Vol. 118, 15.08.2012, p. 193-199.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Prediction of glycerol removal from biodiesel using ammonium and phosphunium based deep eutectic solvents using artificial intelligence techniques
AU - Shahbaz, Kaveh
AU - Baroutian, Saeid
AU - Mjalli, Farouq Sabri
AU - Hashim, Mohd Ali
AU - AlNashef, Inas Muen
PY - 2012/8/15
Y1 - 2012/8/15
N2 - Biodiesel total glycerol content is an important characteristic which must pass the EN 14214 and ASTM D6751 international biodiesel quality standards. In this study, the experimental data of glycerol removal by means of deep eutectic solvents (DESs) was used to design a new modeling approach based on Artificial Neural Networks (ANNs) in order to predict glycerol removal. The DESs were synthesized with choline chloride and methyl triphenyl phosphunium bromide as salts and different hydrogen bond donors. DESs composition and the mole fractions of DESs to biodiesel were used as inputs to the model. A feed-forward neural network with 4 hidden neurons was applied and training was done based on the Levenberg-Marquardt optimization method. The ANN prediction was in good agreement with the measured data with an absolute average deviation of 6.46%. The predicted results indicated that the DESs synthesized with glycerol as hydrogen bond donor has lower removal efficiencies. Furthermore, the phosphunium-based DESs were much efficient in attracting total glycerol in comparison with ammonium-based DESs.
AB - Biodiesel total glycerol content is an important characteristic which must pass the EN 14214 and ASTM D6751 international biodiesel quality standards. In this study, the experimental data of glycerol removal by means of deep eutectic solvents (DESs) was used to design a new modeling approach based on Artificial Neural Networks (ANNs) in order to predict glycerol removal. The DESs were synthesized with choline chloride and methyl triphenyl phosphunium bromide as salts and different hydrogen bond donors. DESs composition and the mole fractions of DESs to biodiesel were used as inputs to the model. A feed-forward neural network with 4 hidden neurons was applied and training was done based on the Levenberg-Marquardt optimization method. The ANN prediction was in good agreement with the measured data with an absolute average deviation of 6.46%. The predicted results indicated that the DESs synthesized with glycerol as hydrogen bond donor has lower removal efficiencies. Furthermore, the phosphunium-based DESs were much efficient in attracting total glycerol in comparison with ammonium-based DESs.
KW - Biodiesel
KW - Deep eutectic solvent
KW - Glycerol
KW - Neural networks
KW - Removal
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UR - http://www.scopus.com/inward/citedby.url?scp=84868212961&partnerID=8YFLogxK
U2 - 10.1016/j.chemolab.2012.06.005
DO - 10.1016/j.chemolab.2012.06.005
M3 - Article
AN - SCOPUS:84868212961
VL - 118
SP - 193
EP - 199
JO - Chemometrics and Intelligent Laboratory Systems
JF - Chemometrics and Intelligent Laboratory Systems
SN - 0169-7439
ER -