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RISS 인기검색어
- 검색키워드
- 저자 : Alizadehdakhel, Asghar (검색결과 3 건)
- 무료
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Numerical and experimental investigation on a new modified valve in a valve tray column
Asghar Alizadehdakhel,Ammar Abdulaziz Alsairafi,Masoud Rahimi 한국화학공학회 2009 Korean Journal of Chemical Engineering Vol.26 No.2
This paper reports experimental and computational fluid dynamics (CFD) modeling studies on the performance of three modified valves operating in a valve tray column. The original and modified valves including vnotched, warped and double-valve are tested experimentally. The experimental rig was a Perspex column having a single valve equipped with a fluctuating plate to measure the quality of gas distribution by using laser sensors. Two-stage nested designs were employed to show the repeatability and consistency of the measurements. In the CFD modeling, the volume of fluid (VOF) method was used to model the gas-liquid two-phase flow inside the column. A good agreement was observed between experimental data and the CFD predictions. The results showed that the double-valve layout provides a better gas distribution, smaller bubbles with greater interface area and lower pressure drop in comparison with the original and the two other modified valves.
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A DFT and QSAR Study of Several Sulfonamide Derivatives in Gas and Solvent
Abadi, Robabeh Sayyadi kord,Alizadehdakhel, Asghar,Paskiabei, Soghra Tajadodi Korean Chemical Society 2016 대한화학회지 Vol.60 No.4
The activity of 34 sulfonamide derivatives has been estimated by means of multiple linear regression (MLR), artificial neural network (ANN), simulated annealing (SA) and genetic algorithm (GA) techniques. These models were also utilized to select the most efficient subsets of descriptors in a cross-validation procedure for non-linear -log (IC50) prediction. The results obtained using GA-ANN were compared with MLR-MLR, MLR-ANN, SA-ANN and GA-ANN approaches. A high predictive ability was observed for the MLR-MLR, MLR-ANN, SA-ANN and MLR-GA models, with root mean sum square errors (RMSE) of 0.3958, 0.1006, 0.0359, 0.0326 and 0.0282 in gas phase and 0.2871, 0.0475, 0.0268, 0.0376 and 0.0097 in solvent, respectively (N=34). The results obtained using the GA-ANN method indicated that the activity of derivatives of sulfonamides depends on different parameters including DP03, BID, AAC, RDF035v, JGI9, TIE, R7e+, BELM6 descriptors in gas phase and Mor 32u, ESpm03d, RDF070v, ATS8m, MATS2e and R4p, L1u and R3m in solvent. In conclusion, the comparison of the quality of the ANN with different MLR models showed that ANN has a better predictive ability.
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A DFT and QSAR Study of Several Sulfonamide Derivatives in Gas and Solvent
Robabeh Sayyadi kord Abadi,Asghar Alizadehdakhel,Soghra Tajadodi Paskiabei 대한화학회 2016 대한화학회지 Vol.60 No.4
The activity of 34 sulfonamide derivatives has been estimated by means of multiple linear regression (MLR), artificial neural network (ANN), simulated annealing (SA) and genetic algorithm (GA) techniques. These models were also utilized to select the most efficient subsets of descriptors in a cross-validation procedure for non-linear -log (IC50) prediction. The results obtained using GA-ANN were compared with MLR-MLR, MLR-ANN, SA-ANN and GA-ANN approaches. A high predictive ability was observed for the MLR-MLR, MLR-ANN, SA-ANN and MLR-GA models, with root mean sum square errors (RMSE) of 0.3958, 0.1006, 0.0359, 0.0326 and 0.0282 in gas phase and 0.2871, 0.0475, 0.0268, 0.0376 and 0.0097 in solvent, respectively (N=34). The results obtained using the GA-ANN method indicated that the activity of derivatives of sulfonamides depends on different parameters including DP03, BID, AAC, RDF035v, JGI9, TIE, R7e+, BELM6 descriptors in gas phase and Mor 32u, ESpm03d, RDF070v, ATS8m, MATS2e and R4p, L1u and R3m in solvent. In conclusion, the comparison of the quality of the ANN with different MLR models showed that ANN has a better predictive ability.
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