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- 저자 : Saeid Atashrouz (검색결과 3 건)
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Saeid Atashrouz,Hamed Mirshekar,Abdolhossein Hemmati-Sarapardeh,Mostafa Keshavarz Moraveji,Bahram Nasernejad 한국화학공학회 2017 Korean Journal of Chemical Engineering Vol.34 No.2
The main objective of this study was to develop soft computing approaches for prediction of physicochemical properties of IL mixtures including: density, heat capacity, thermal conductivity, and surface tension. The proposed models in this study are based on support vector machine (SVM), least square support vector machines (LSSVM), and group method of data handling type polynomial neural network (GMDH-PNN) systems. To find the LSSVM and SVM adjustable parameters, genetic algorithm (GA) as a meta-heuristic algorithm was utilized. The results showed that LSSVM is more robust and reliable for prediction of physicochemical properties of IL mixtures. The proposed GALSSVM model provides average absolute relative deviations of 0.38%, 0.18%, 0.77% and 1.18% for density, heat capacity, thermal conductivity, and surface tension, respectively, which demonstrates high accuracy of the model for prediction of physicochemical properties of IL mixtures.
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Mehrdad Mozaffarian,Saeid Atashrouz,Gholamreza Pazuki 한국화학공학회 2016 Korean Journal of Chemical Engineering Vol.33 No.9
The viscosity and rheological behavior of an ethylene glycol-water mixture based Fe3O4 nanofluid have been experimentally investigated. The nanofluids for this study were prepared by a two-step method in which Fe3O4 nanoparticles were added to a base fluid mixture consisting of 60% (w/w) ethylene glycol and 40% (w/w) water. The measurements were conducted at temperatures ranging from 288.15 to 343.15 K, and at nanoparticle volume fractions ranging from 0.0022 to 0.0055. Furthermore, the dependency of viscosity of nanofluids on shear rate was examined. The results indicate that increasing the shear rate leads to a reduction in the viscosity (shear thinning behavior). Finally, the obtained experimental data was correlated by both a thermodynamic model and a hybrid GMDH-type polynomial neural network, where the mean absolute relative deviation (MARD) of these models was calculated as 3.64% and 3.88%, respectively.
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Mahmoudzadeh Atena,Hadavimoghaddam Fahimeh,Atashrouz Saeid,Abedi Ali,Abuswer Meftah Ali,Mohaddespour Ahmad,Hemmati-Sarapardeh Abdolhossein 한국화학공학회 2024 Korean Journal of Chemical Engineering Vol.41 No.5
Several carbon capture techniques have been developed in response to the notable rise of atmospheric carbon dioxide ( CO2 ) levels. The utilization of diethanolamine (DEA) as an absorption method is prevalent in various industries due to its high reactivity and cost-effi ciency. Hence, comprehending the equilibrium solubility of CO2 in DEA solutions is an essential step in developing and optimizing absorption procedures. In order to predict the CO2 loading capacity in the DEA solutions, four advanced deep learning and machine learning models were developed: recurrent neural networks (RNN), deep neural networks (DNN), random forest (RF), and adaBoost-support vector regression (AdaBoost-SVR). The models predict the capacity of CO2 loading as a function of temperature, CO2 partial pressure, and the concentration of DEA in the solution. Intelligent models were developed employing an extensive database which includes new experimental data points published within recent years, which were not considered in the previous studies. The RNN model was found to outperform other models based on graphical and statistical assessments, as evidenced by its lower root mean square error ( RMSE = 0.285 ) and standard deviation ( SD = 0.032 ), and higher determination coeffi cient ( R2 = 0.992 ). While the RNN model resulted in the highest accuracy in predicting CO2 absorption, the DNN, RF, and AdaBoost-SVR models also demonstrated satisfactory accuracy in predicting CO2 solubility, placed in the following ranking. A sensitivity analysis was performed on the four developed models, revealing that the CO2 partial pressure has the strongest eff ect on the CO2 loading capacity. Furthermore, a trend analysis was performed on the RNN model, demonstrating that the developed model has a high degree of accuracy in following physical trends. The binary interaction analysis was conducted with two varying parameters and one constant parameter in the RNN model through 3-D image plots, which illustrated the simultaneous eff ect of two independent parameters on CO2 loading. Finally, outlier detection was conducted by employing the Leverage method to fi nd outlier data points in the data bank, demonstrating the applicability domain of intelligent models.
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