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

Mohammad Mesbah; Ebrahim Soroush; Mashallah Rezakazemi

doi:10.1016/j.cjche.2016.09.007

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

Process Systems Engineering and Process Safety | Updated：2026-01-06

- Development of a least squares support vector machine model for prediction of natural gas hydrate formation temperature
- Chinese Journal of Chemical Engineering Vol. 25, Issue 9, Pages: 1238-1248(2017)
- Affiliations：
  
  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
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2016.09.007
  CLC：
- Published：2017
- Accepted：
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Mohammad Mesbah, Ebrahim Soroush, Mashallah Rezakazemi. Development of a least squares support vector machine model for prediction of natural gas hydrate formation temperature[J]. , 2017, 25(9): 1238-1248.
DOI：

Mohammad Mesbah, Ebrahim Soroush, Mashallah Rezakazemi. Development of a least squares support vector machine model for prediction of natural gas hydrate formation temperature[J]. , 2017, 25(9): 1238-1248. DOI： 10.1016/j.cjche.2016.09.007.

摘要

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 (

) 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 H

S 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 alg

orithm 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.

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