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Articles by G. Zahedi
Total Records ( 2 ) for G. Zahedi
  Ali Akbar Tajali , Gholamreza Amin , M.R. Chaichi and G. Zahedi
  Camphorosma monspeliaca L. were collected in full flowering stage from 3 different habitats in Iran. Essential oil of aerial parts was obtained using cellevenger apparatus and chemical composition were analyzed by GC and GC/MS and identified in comparison with authentic compounds. The yields of essential oils were to 0.15 v/w% and the major compounds in 3 habitats were α-pinene, citronellyl pentanoate, endo-bourbonanol, α-fenchene, trans-pinocarveol, limonene, pinocarvone, camphene and dill ether.
  G. Zahedi , F. Parvizian and M.R. Rahimi
  Knowledge of the efficiency of sieve tray columns as most common distillation equipments is necessary for the interpretation of separation and purification processes performance. In this study a new method based on Artificial Neural Network (ANN) for estimation of sieve tray efficiency has been proposed. In this case to develop data base several experimental data were collected from literatures. The network inputs are liquid and vapor density, liquid and vapor viscosity, liquid and vapor diffusivity, surface tension, slope of the equilibrium curve, hole diameter, weir height, weir length, liquid and gas flux, ratio of hole area to active area of the tray while the output is point efficiency. In order to find the best efficiency estimator of sieve tray, different training schemes for the back-propagation learning algorithm, such as; Scaled Conjugate Gradient (SCG), Levenberg-Marquardt (LM), Gradient Descent with Momentum (GDM), variable learning rate BP (GDA) and Resilient BP (RP) methods were examined. Finally among those trained networks, the SCG algorithm with ten neurons in the hidden layer shows the best suitable algorithm with the minimum average absolute relative error 0.029817. Finally, the capability of ANN and two recently published empirical models were compared. This ANN model reduced the prediction error by 64.03 and 92.64% relative to Garcia and Fair and Chan and Fair models, respectively. This is further proof that the proposed procedure can build a useful and robust model.
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