Browsing by Author "Yang, Zhifeng"

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    Influence of pipeline steel surface on the thermal stability of methane hydrate
    (Elsevier, 2022-10-19) Wu, Guozhong; Tian, Linqing; Ha, Li; Feng, Feng; Yang, Zhifeng; Feng, Jing-Chun; Coulon, Frederic; Jiang, Yuelu; Zhang, Ruifeng
    The thermal stability and surface adhesion of natural gas hydrate are critical for the safety of oil and gas pipelines. The roughness and hydrophobicity of the pipe surface often vary during long-distance transportation, but it remains unclear about how these variances influence the hydrate stability. In this study, twelve molecular models of solid steel pipeline surfaces with random morphology were evaluated and molecular dynamics simulations were performed to gain insights into the kinetics of methane hydrate dissociation, the nucleation and growth of gas bubbles during hydrate decomposition, and the free energy of hydrate adhesion to the solid steel surface. Results demonstrated that the stability of methane hydrate could be decreased by up to 85% by increasing the hydrophobicity of the pipe surface by 52%. The bubble nucleation site of the gas released from hydrate decomposition shifted from bulk water to the solid surface by increasing the surface hydrophobicity (εsw: 3.73–5.74 kJ mol−1), but a highly hydrophobic surface (εsw: 2.73 kJ mol−1) made it hard to form gas bubble on either smooth or rough surface. Moreover, the free energy of hydrate adhesion also depended on the roughness and hydrophobicity of the solid surface, while the largest energy barrier for the adhesion of methane hydrate was found on the hydrophobic surface with high roughness. The findings from this study provided theoretical support for better understanding the methane hydrate evolution principles when the surface properties of the pipe wall changed from naturally occurred events (e.g., metal corrosion) or artificial treatment (e.g. chemical coating).
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    Machine learning models for fast selection of amino acids as green thermodynamic inhibitors for natural gas hydrate
    (Elsevier, 2022-12-13) Wu, Guozhong; Coulon, Frederic; Feng, Jing-Chun; Yang, Zhifeng; Jiang, Yuelu; Zhang, Ruifeng
    Natural amino acids are non-toxic thermodynamic hydrate inhibitors without negative environmental impact, but it is difficult to accurately select the appropriate amino acid as a quick response to the operational conditions changes in the natural gas pipeline. The objective of this study was to develop mathematical models to predict the hydrate formation temperature (HFT) in presence of amino acids, capture the relationship between amino acid structure properties and their hydrate inhibition strength, and determine the optimal type and concentration to use. The HFT prediction was evaluated using multiple linear regression (MLR) and three machine learning methods including random forest (RF), M5 Rule (M5R) and support vector machine (SVM). After parameter optimization using the trial-and-error method, the coefficient of determination (R2) of the four models were 0.9328, 0.9793, 0.9795 and 0.9980, respectively. The SVM prediction of HFT outperformed other models as the root mean square error (RMSE) was 83%, 76% and 69% lower than that of the MLR, RF and M5R, respectively. Results also demonstrated that the relative importance of the amino acid concentration to the hydrate phase equilibrium was 5-fold higher than that of the intrinsic properties of the amino acid molecular. The SVM model proposed in this study served an easy-to-use tool for reliable prediction of HFT by just providing a new set of input data. This made it possible to accurately determine the minimum concentration of amino acids to be used during the gas pipeline transportation.