An expert model for estimation of distillation sieve tray efficiency based on artificial neural network approach

Abstract

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.