Estimating sulfur content of hydrogen sulfide at elevated temperatures and pressures using an artificial neural network algorithm

Amir H. Mohammadi (1) , Dominique Richon (1)

1 CEP/Fontainebleau

In this communication, we report an artificial neural network algorithm for estimating sulfur content of hydrogen sulfide at elevated temperatures and pressures. This model eliminates any need for characterization parameters, due to the tendency of sulfurs to react, required in thermodynamic models. To develop this algorithm, reliable experimental data reported in the literature on sulfur content of hydrogen sulfide are used. The developed model is then used to predict independent experimental data (not used in developing the model). It is shown that artificial neural network algorithm can be used as an efficient tool to estimate sulfur content of hydrogen sulfide.

Mots clés: Aircraft; Backpropagation; Hydrogen; Hydrogen sulfide; Image classification; Sulfur; Sulfur determination; Artificial neural network algorithms; Developed models; Elevated temperatures

Domaines: Sciences de l'ingénieur [physics]; Génie des procédés

Amir H. Mohammadi, Dominique Richon. Estimating sulfur content of hydrogen sulfide at elevated temperatures and pressures using an artificial neural network algorithm. Industrial and engineering chemistry research, 2008, 47 (21), pp.8499-8504. ⟨10.1021/ie8004463⟩. ⟨hal-00797790⟩