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RISS 인기검색어
- 검색키워드
- 저자 : Alireza Fazlali (검색결과 2 건)
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Alireza Fazlali,Parvaneh Koranian,Reza Beigzadeh,Masoud Rahimi 한국화학공학회 2013 Korean Journal of Chemical Engineering Vol.30 No.9
A feed forward three-layer artificial neural network (ANN) model was developed for VLE prediction of ternary systems including ionic liquid (IL) (water+ethanol+1-butyl-3- methyl-imidazolium acetate), in a relatively wide range of IL mass fractions up to 0.8, with the mole fractions of ethanol on IL-free basis fixed separately at 0.1,0.2, 0.4, 0.6, 0.8, and 0.98. The output results of the ANN were the mole fraction of ethanol in vapor phase and the equilibrium temperature. The validity of the model was evaluated through a test data set, which were not employed in the training case of the network. The performance of the ANN model for estimating the mole fraction and temperature in the ternary system including IL was compared with the non-random-two-liquid (NRTL) and electrolyte non-randomtwo-liquid (eNRTL) models. The results of this comparison show that the ANN model has a superior performance in predicting the VLE of ternary systems including ionic liquid.
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Mohammad Reza Mirani,Alireza Fazlali,Masoud Rahimi 한국화학공학회 2021 Korean Journal of Chemical Engineering Vol.38 No.5
We investigated the performance of a T-type microchannel for mercaptan extraction from light straightrun naphtha (LSRN) with sodium hydroxide solution. The aim of this work is to introduce the microfluidic system as a potential tool for mercaptan extraction from light petroleum products. Modeling the extraction process of mercaptan from LSRN has not been carried out previously. In this regard, mercaptan extraction was modeled by response surface methodology (RSM) and artificial neural network (ANN) to analyze the effect of operating parameters on the mercaptan extraction process. The independent variables are considered as temperature, sodium hydroxide concentration, and the volume ratio of sodium hydroxide to LSRN. Two models were compared based on error analysis of the predicted data. Root mean square error, mean relative error, and determination coefficient for the neural network were 0.5650, 0.4341, and 0.9862, respectively. The values of these parameters for the RSM model were 0.6854, 0.7648, and 0.9798. The results showed that the prediction accuracy for both models is appropriate, but the precision of the neural network model is slightly higher than that of the RSM model. The genetic algorithm (GA) technique determined the optimal values of the independent variables with the aim of maximizing the extraction percentage. The mercaptan extraction percentage value of 85.08% was achieved at 303.15 K, the sodium hydroxide concentration of 20 wt%, and the volume ratio of sodium hydroxide to LSRN of 0.128. Furthermore, results showed a higher mercaptan extraction percentage of the microfluidic system compared to a conventional extractor at the same process condition.
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