Solubility of hydrogen
sulfide (H2S) in 46 single and
blended physical absorbents, amines, ionic liquids, and hybrid absorbents
of amines + ionic liquids and amines + physical absorbents was successfully
predicted based on artificial neural networks (ANNs). Three neural
network algorithms of Levenberg–Marquardt (LM), Bayesian regularization
(BR), and scaled conjugate gradient (SCG) were applied for architecting
the ANN models. The results showed that both the number of hidden
neurons and the prediction algorithm affected the prediction of H2S solubility. Based on the mean square error (MSE) and determination
coefficient (R
2), the most attractive
model was the LM-ANN model with 17 hidden neurons. As a result, very
satisfactory prediction performance (for the testing data set) with
an MSE of 0.0014 and an R
2 of 0.9817 was
obtained from the developed LM-ANN model. Additionally, a parity chart
confirmed that the predicted solubility of H2S well aligned
with the experimental data. To effectively absorb H2S and
maintain high solubility of H2S, the absorbent should be
well complied with the operating pressure. For a low-pressure range
of less than 100 kPa, amines are very attractive. As the pressure
elevated to 100–1000 kPa, amines and hybrid amine + physical
absorbents are suggested. Lastly, at a high pressure over 1000 kPa,
physical absorbents and ionic liquids are recommended.
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