2
MSc in Chemical Engineering/University of Guilan
Abstract
Natural gas is the most important fossil fuels. However, natural gas contains a lot of non-hydrocarbon compounds such as hydrogen sulfide and carbon dioxide which are removed through a process called sweetening. In this study, neural network was used to predict the amount of hydrogen sulfide and carbon dioxide from absorption tower of a sweetening unit. The network was developed and evaluated by operational data of South Pars gas refinery. Time, the temperature of sea water, flow rate of input sour gas, the amount of hydrogen sulfide absorbed by amine, flow rate of input amine, and the inlet low pressure steam to amine reboiler were considered as inputs of network. For the tested data, the value of mean square error was equal to 0.0011 and the values of regression factor were respectively equal to 0.9796 and 0.9617 for first and second output, which showed good agreement with the empirical data.
Sayyad Amin, J., & Keshavarz Babaee Nejad2, &. (2016). Prediction of hydrogen sulfide and carbon dioxide of sweetening tower in South Pars gas refinery by the neural network. Farayandno, 10(52), 179-188.
MLA
Javad Sayyad Amin; ‎Samira Keshavarz Babaee Nejad2. "Prediction of hydrogen sulfide and carbon dioxide of sweetening tower in South Pars gas refinery by the neural network". Farayandno, 10, 52, 2016, 179-188.
HARVARD
Sayyad Amin, J., Keshavarz Babaee Nejad2, &. (2016). 'Prediction of hydrogen sulfide and carbon dioxide of sweetening tower in South Pars gas refinery by the neural network', Farayandno, 10(52), pp. 179-188.
VANCOUVER
Sayyad Amin, J., Keshavarz Babaee Nejad2, &. Prediction of hydrogen sulfide and carbon dioxide of sweetening tower in South Pars gas refinery by the neural network. Farayandno, 2016; 10(52): 179-188.
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