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Extension of an artificial neural network algorithm for estimating sulfur content of sour gases at elevated temperatures and pressures / Mehdi Mehrpooya in Industrial & engineering chemistry research, Vol. 49 N° 1 (Janvier 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 1 (Janvier 2010) . - pp. 439–442 Titre : Extension of an artificial neural network algorithm for estimating sulfur content of sour gases at elevated temperatures and pressures Type de document : texte imprimé Auteurs : Mehdi Mehrpooya, Auteur ; Amir H. Mohammadi, Auteur ; Richon, Dominique, Auteur Année de publication : 2010 Article en page(s) : pp. 439–442 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Extension--Artificial--Neural--Network--Algorithm--Estimating--Sulfur--Gases--Elevated--Temperatures--Pressures Résumé : In this communication, we report an extended artificial neural network algorithm to estimate sulfur content of sour/acid gases. The main advantage of this algorithm is that it eliminates any need for characterization parameters, due to the tendency of sulfurs to react, required in thermodynamic models. To develop this tool, reliable experimental data found in the literature on sulfur content of various gases are used. To estimate the sulfur content of a gas, the information on temperature, pressure, gravity of acid gas free gas, and the concentrations of hydrogen sulfide and carbon dioxide in the gas are required. The developed algorithm is then used to predict independent experimental data (not used in its development). It is shown that the artificial neural network algorithm can be used as an efficient tool to estimate sulfur content of various gases. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900399b [article] Extension of an artificial neural network algorithm for estimating sulfur content of sour gases at elevated temperatures and pressures [texte imprimé] / Mehdi Mehrpooya, Auteur ; Amir H. Mohammadi, Auteur ; Richon, Dominique, Auteur . - 2010 . - pp. 439–442. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 1 (Janvier 2010) . - pp. 439–442 Mots-clés : Extension--Artificial--Neural--Network--Algorithm--Estimating--Sulfur--Gases--Elevated--Temperatures--Pressures Résumé : In this communication, we report an extended artificial neural network algorithm to estimate sulfur content of sour/acid gases. The main advantage of this algorithm is that it eliminates any need for characterization parameters, due to the tendency of sulfurs to react, required in thermodynamic models. To develop this tool, reliable experimental data found in the literature on sulfur content of various gases are used. To estimate the sulfur content of a gas, the information on temperature, pressure, gravity of acid gas free gas, and the concentrations of hydrogen sulfide and carbon dioxide in the gas are required. The developed algorithm is then used to predict independent experimental data (not used in its development). It is shown that the artificial neural network algorithm can be used as an efficient tool to estimate sulfur content of various gases. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900399b