A new model for predicting sulfur solubility in sour gases based on hybrid intelligent algorithm

Abstract

The accurate prediction of solubility of elemental sulfur in high H2S-content gas (sour gas) is of critical importance in the exploitation of sour gas. Due to the measurement difficulties, high pressure and low sulfur content in the gas phase, there are limited experimental data about the solubility of sulfur in the literature to date. In order to determine the reliability of data about sulfur solubility in H2S, CO2 and CH4, an assessment test of experimental data for gas-solid system under high pressure is carried out. The assessment test is based on Gibbs-Duhem equation and P-R equation of state is used for modeling. The correlated parameters in the model are obtained by using chaos-based firefly algorithm (CFA). For the whole experimental data, the assessment results show that 28% data points are considered as thermodynamically consistent, 28% are inconsistent and 44% are deemed to be not fully consistent. After eliminating the unreliable data points, four optimization algorithms combined BP neural network and support vector regression (SVR) into eight hybrid intellect algorithms. The results show that the most accurate results can be obtained using CFA algorithm combined with support vector regression among eight hybrid intellect algorithms. Simultaneously, this new model can obtain more accurate results compared with previous proposed three empirical models. For sulfur solubility in sour gas, the result shows that the average relative deviation between the experimental data and calculated results (ARD) is 4.51%. For sulfur solubility in pure H2S and CO2, the ARDs are 2.11% and 10.12%, respectively.