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- 저자 : Henrique Gasparetto (검색결과 2 건)
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Henrique Gasparetto,Ana Luiza Barrachini Nunes,Fernanda de Castilhos,Nina Paula Gonçalves Salau 한국공업화학회 2022 Journal of Industrial and Engineering Chemistry Vol.113 No.-
Soybean oil extraction using two green solvents was investigated from solvent selection to thermodynamics:ethyl acetate and 1-butanol. The screening of the solvents was performed using the Hansenparameters and Infinite Dilution Activity Coefficient (IDAC) obtained through the COnductor-likeScreening MOdels – Segment Activity Coefficient (COSMO-SAC) theory. The solvent selection was performedon ethyl acetate and 1-butanol in comparison with ethanol, a well-studied green solvent, andhexane, a non-renewable and industrially used solvent. The effects of temperature and solvent/solid ratioon the yield of soybean oil extraction were investigated through response surface methodology (RSM). The RSM obtained satisfactory statistical results, with R2adj of 0.9958 for ethyl acetate and 0.9729 for 1-butanol. The kinetic of the extractions were evaluated using two different models: mass transfer kineticand So and Macdonald. The last one obtained the best correlation to the data (R2 > 0.9964). The thermodynamicassessment showed endothermic, and spontaneous processes for both solvents. 1-Butanol, ethylacetate, and hexane have a better performance on the yield of soybean oil extraction than using ethanol;however, ethyl acetate is the best candidate to replace the industrial use of hexane due to its highest rateof soybean oil extraction at the process beginning.
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Henrique Gasparetto,Ana Carolina Ferreira Piazzi Fuhr,Nina Paula Gonçalves Salau 한국공업화학회 2023 Journal of Industrial and Engineering Chemistry Vol.123 No.-
This work presents a thermo-statistical assessment using soft computing models to describe green soybeanoil extraction by cyclopentyl methyl ether (CPME). Experimental data were collected based on anexperimental factorial design and modeled by an Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS), as the empirical model was unable to accurately predict the experimentalresults. The ANFIS structure is related to the best statistical metrics, while the ANN achieves the bestthermodynamic fit. The results suggest higher yields for higher temperatures and lower solvent-to-solidmass ratios. The extraction temperature can be significantly reduced with CPME to achieve the sameyield as n-hexane. The second-order model was the most accurate (SAE = 0.1266, MSE = 5.5410-5 andR2 = 0.9876) in representing the extraction kinetics, resulting in an extraction rate constant of1.9782 min1. It was noticed that small positive induced charges given by the oxygen atom of CPME couldcontribute to the potential of this solvent to deplete the oil matrix and that its entropy is similar to that ofthe n-hexane molecule. The extracted oil presented the typical constitution regarding fatty acids composition;free fatty acid, mono, di, and triacylglycerol contents; and infrared spectrum.
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