Abstract: In this paper, Artificial Neural Network (ANN) was used for modeling the nonlinear structure of a debutanizer column in a refinery gas process plant. The actual input-output data of the system were measured in order to be used for system identification based on root mean square error (RMSE) minimization approach. It was shown that the designed recurrent neural network is able to precisely predict and track the response of the actual system. The comparison between the results of this paper and those of the most recent published studies as NARX model indicates the significance of the proposed approach.
Amidpour, M. , Salehi, G. R. , Ghaffari, A. and Sahraei, H. (2013). Distillation Column Identification Using Artificial Neural Network. Gas Processing Journal, 1(2), 31-40. doi: 10.22108/gpj.2013.20166
MLA
Amidpour, M. , , Salehi, G. R. , , Ghaffari, A. , and Sahraei, H. . "Distillation Column Identification Using Artificial Neural Network", Gas Processing Journal, 1, 2, 2013, 31-40. doi: 10.22108/gpj.2013.20166
HARVARD
Amidpour, M., Salehi, G. R., Ghaffari, A., Sahraei, H. (2013). 'Distillation Column Identification Using Artificial Neural Network', Gas Processing Journal, 1(2), pp. 31-40. doi: 10.22108/gpj.2013.20166
CHICAGO
M. Amidpour , G. R. Salehi , A. Ghaffari and H. Sahraei, "Distillation Column Identification Using Artificial Neural Network," Gas Processing Journal, 1 2 (2013): 31-40, doi: 10.22108/gpj.2013.20166
VANCOUVER
Amidpour, M., Salehi, G. R., Ghaffari, A., Sahraei, H. Distillation Column Identification Using Artificial Neural Network. Gas Processing Journal, 2013; 1(2): 31-40. doi: 10.22108/gpj.2013.20166
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