Title : ( Sensitivity analysis of a CO2 Stripper Column Using Linear and Nonlinear modeling, a Case Study )
Authors: hamidreza sadeghi , Nasser Saghatoleslami , M.C. Amiri ,
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Abstract
In this work, artificial neural network method has been utilized to conduct sensitivity analysis for a carbon dioxide stripper column. The data were obtained from an Iranian oil refinery, namely Esfahan Oil Refining Company. The total number of data’s acquired at the time of this study was added up to 600 data. All data have been collected during three year. The stripper column data’s obtained from this oil refinery are operating parameter of column. Then, sensitivity analysis via artificial neural network (SAANN) and correlation coefficient (CC) were used to find the major and minor input variables from 5 input variables for the elimination of CO2 in the stripper column. The results revealed that the major and minor input variable for both methods was analogous.
Keywords
, Nonlinear Modeling, Artificial Neural Network, Sensitivity Analysis, Stripper Column.
@inproceedings{paperid:1029522,
author = {Sadeghi, Hamidreza and Saghatoleslami, Nasser and M.C. Amiri},
title = {Sensitivity analysis of a CO2 Stripper Column Using Linear and Nonlinear modeling, a Case Study},
booktitle = {International Conference on Nonlinear Modeling & Optimization},
year = {2012},
location = {Amol, IRAN},
keywords = {Nonlinear Modeling; Artificial Neural Network; Sensitivity Analysis; Stripper Column.},
}
%0 Conference Proceedings
%T Sensitivity analysis of a CO2 Stripper Column Using Linear and Nonlinear modeling, a Case Study
%A Sadeghi, Hamidreza
%A Saghatoleslami, Nasser
%A M.C. Amiri
%J International Conference on Nonlinear Modeling & Optimization
%D 2012
