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Heat transfer and fluid flow modeling in serpentine microtubes using adaptive neuro-fuzzy approach
Masoud Rahimi,Reza Beigzadeh,Marziyeh Hajialyani 한국화학공학회 2016 Korean Journal of Chemical Engineering Vol.33 No.5
An adaptive neuro-fuzzy inference system (ANFIS) is applied to predict thermal and flow characteristics in serpentine microtubes. Heat transfer rate and pressure drop were experimentally measured for six serpentine microtubes with different geometrical parameters. Thermal and flow characteristics were obtained in various flow conditions. The ANFIS models were trained using the experimental data to predict Nusselt number (Nu) and friction factor (f) in the studied serpentine microtubes as a function of geometric parameters and flow conditions. The model was validated through testing data set, which were not previously introduced to the developed ANFIS. For Nu prediction, the root mean square error (RMSE), mean relative error (MRE), and absolute fraction of variance (R2) between the predicted results and experimental data were found 0.2058, 1.74%, and 0.9987, respectively. The corresponding calculated values for f were 0.0056, 2.98%, and 0.9981, respectively. The prediction accuracy of the ANFIS models was compared with that of corresponding classical power-law correlations and its advantages are illustrated.
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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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