SCI和EI收录∣中国化工学会会刊

›› 2017, Vol. 25 ›› Issue (9): 1238-1248.DOI: 10.1016/j.cjche.2016.09.007

• Process Systems Engineering and Process Safety • 上一篇    下一篇

Development of a least squares support vector machine model for prediction of natural gas hydrate formation temperature

Mohammad Mesbah1, Ebrahim Soroush2, Mashallah Rezakazemi3   

  1. 1 Young Researchers and Elites Club, Science and Research Branch, Islamic Azad University, Tehran, Iran;
    2 Young Researchers and Elites Club, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran;
    3 Department of Chemical Engineering, Shahrood University of Technology, Shahrood, Iran
  • 收稿日期:2016-08-06 修回日期:2016-09-12 出版日期:2017-09-28 发布日期:2017-10-11
  • 通讯作者: Mohammad Mesbah,E-mail:mohammad_mesbah@ymail.com

Development of a least squares support vector machine model for prediction of natural gas hydrate formation temperature

Mohammad Mesbah1, Ebrahim Soroush2, Mashallah Rezakazemi3   

  1. 1 Young Researchers and Elites Club, Science and Research Branch, Islamic Azad University, Tehran, Iran;
    2 Young Researchers and Elites Club, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran;
    3 Department of Chemical Engineering, Shahrood University of Technology, Shahrood, Iran
  • Received:2016-08-06 Revised:2016-09-12 Online:2017-09-28 Published:2017-10-11

摘要: Hydrates always are considered as a threat to petroleum industry due to the operational problems it can cause. These problems could result in reducing production performance or even production stoppage for a long time. In this paper, we were intended to develop a LSSVM algorithm for prognosticating hydrate formation temperature (HFT) in a wide range of natural gas mixtures. A total number of 279 experimental data points were extracted from open literature to develop the LSSVM. The input parameters were chosen based on the hydrate structure that each gas species form. The modeling resulted in a robust algorithm with the squared correlation coefficients (R2) of 0.9918. Aside from the excellent statistical parameters of the model, comparing proposed LSSVM with some of conventional correlations showed its supremacy, particularly in the case of sour gases with high H2S concentrations, where the model surpasses all correlations and existing thermodynamic models. For detection of the probable doubtful experimental data, and applicability of the model, the Leverage statistical approach was performed on the data sets. This algorithm showed that the proposed LSSVM model is statistically valid for HFT prediction and almost all the data points are in the applicability domain of the model.

关键词: Hydrate formation temperature (HFT), Natural gas, Sour gases, Least squares support vector machine, Outlier diagnostics, Leverage approach

Abstract: Hydrates always are considered as a threat to petroleum industry due to the operational problems it can cause. These problems could result in reducing production performance or even production stoppage for a long time. In this paper, we were intended to develop a LSSVM algorithm for prognosticating hydrate formation temperature (HFT) in a wide range of natural gas mixtures. A total number of 279 experimental data points were extracted from open literature to develop the LSSVM. The input parameters were chosen based on the hydrate structure that each gas species form. The modeling resulted in a robust algorithm with the squared correlation coefficients (R2) of 0.9918. Aside from the excellent statistical parameters of the model, comparing proposed LSSVM with some of conventional correlations showed its supremacy, particularly in the case of sour gases with high H2S concentrations, where the model surpasses all correlations and existing thermodynamic models. For detection of the probable doubtful experimental data, and applicability of the model, the Leverage statistical approach was performed on the data sets. This algorithm showed that the proposed LSSVM model is statistically valid for HFT prediction and almost all the data points are in the applicability domain of the model.

Key words: Hydrate formation temperature (HFT), Natural gas, Sour gases, Least squares support vector machine, Outlier diagnostics, Leverage approach