Prediction of sulfur solubility in supercritical sour gases using grey wolf optimizer-based support vector machine

• Published in: Journal of molecular liquids Vol. 261; pp. 431 - 438
• Main Authors: Bian, Xiao-Qiang, Zhang, Lu, Du, Zhi-Min, Chen, Jing, Zhang, Jian-Ye
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2018
• Subjects: Grey wolf optimizer; Sour gas; Sulfur solubility; Support vector machine; Sour gas; Sulfur solubility; Support vector machine; Grey wolf optimizer
• ISSN: 0167-7322; 1873-3166
• DOI: 10.1016/j.molliq.2018.04.070

Accurate knowledge of the solubility of sulfur in supercritical sour gas is essential in highly effective development of sour gas reservoirs. However, it is well-acknowledged that experimental measurements are expensive, time-consuming and cumbersome especially due to the highly nocuous H2S. As a direct consequence, a new meta-heuristic technique namely grey wolf optimizer-based support vector machine (GWO-SVM) was proposed to accurate prediction of the sulfur solubility in supercritical sour gases. The proposed GWO-SVM model considered the reservoir temperature, pressure and the mole fraction of methane, hydrogen sulfide and carbon dioxide as input parameters and the sulfur solubility as target parameter on the basis of grey correlation analysis. The accuracy and reliability of the presented model were evaluated through 170 data sets accessible to the literature and compared with three empirical correlations (Guo-Wang, Hu et al., Chrastil correlation) reported in previous literature. The results showed that the proposed model provides the closest agreement with experimental data with the global average absolute relative deviation of 4.65% and significantly outperforms all the existing methods considered in this work. Additionally, the outlier diagnostics was also operated for detection of the probable doubtful sulfur solubility data and identification of the validity and applicable range of all models considered in this work. [Display omitted] •The grey wolf optimizer, a new meta-heuristic optimization algorithm has been used.•The GWO-based support vector machine model was proposed for the sulfur solubility.•The grey correlation method was used to analyze factors affecting sulfur solubility.•The proposed GWO-SVM model is the best among all the models considered in this work.