Modeling the Solubility of Sulfur in Sour Gas Mixtures Using Improved Support Vector Machine Methods

Abstract

The study of sulfur solubility is of great significance to the safe development of sulfur-containing gas reservoirs. However, due to measurement difficulties, experimental research data on sulfur solubility thus far are limited. Under the research background of small samples and poor information, a weighted least-squares support vector machine (WLSSVM)-based machine learning model suitable for a wide temperature and pressure range is proposed to improve the prediction accuracy of sulfur solubility in sour gas. First, we use the comprehensive gray relational analysis method to extract important factors affecting sulfur solubility as the model input parameters. Then, we use the whale optimization algorithm (WOA) and gray wolf optimizer (GWO) intelligence algorithms to find the optimal solution of the penalty factor and kernel coefficient and bring them into three common kernel functions. The optimal kernel function is calculated, and the final WOA-WLSSVM and GWO-WLSSVM models are established. Finally, four evaluation indicators and an outlier diagnostic method are introduced to test the proposed model’s performance. The empirical results show that the WOA-WLSSVM model has better performance and reliability; the average absolute relative deviation is as low as 3.45%, determination coefficient (R 2) is as high as 0.9987, and the prediction accuracy is much higher than that of other models.

Roles of Carbon Dioxide and Methane in the Dissolution of Elemental Sulfur in Natural Gas

“…For example, Brunner et al verified that the sulfur solubility in sour gas increases rapidly with the H 2 S concentration under a given temperature and pressure by measuring the solubility of elemental sulfur in H 2 S, CO 2 , CH 4 , N 2 , and C 2 –C 6 alkane mixtures. Fu et al applied the Takagi–Sugeno (T–S) fuzzy neural network models to predict the sulfur solubility in sour gas mixtures and found the nonlinear and positive correlation between the H 2 S content and sulfur solubility …”

“…On the other hand, some empirical and semi-empirical models − have been proposed to estimate the sulfur solubility in natural gas, including those using machine learning methods. − For example, the commonly used Chrastil model was proposed by assuming a chemical equilibrium among undissolved and dissolved sulfur and sour gases. − In Karan’s model, the sulfur dissolution is considered as a phase transition described by the Peng–Robinson equation. However, all of these models contain parameters that are adjusted using the aforementioned measurements, and these parameters have to be readjusted case by case in practical applications.…”

Evaluation of Therapeutic Effects of Computed Tomography Imaging Classification Algorithm-Based Transcatheter Arterial Chemoembolization on Primary Hepatocellular Carcinoma

“…By the direct substitution of low dimension features into the kernel function, the high-dimension transformation of the features was acquired equivalently, and then the inner product of the transformed features was solved. Figure 2 displayed different feature mapping methods corresponding to different kernel functions as follows [15].…”