Extension of an Artificial Neural Network Algorithm for Estimating Sulfur Content of Sour Gases at Elevated Temperatures and Pressures
Résumé
In this communication, we report all 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 oil 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 ail efficient tool to estimate sulfur content of various gases.