New predictive method for estimation of natural gas hydrate formation temperature using genetic programming

Abstract

Diagnosis of detailed conditions of hydrate formation, as an important issue of gas fuels, can help related industries a lot, particularly in storing, transportation and processing equipment. Hydrate formation temperature or pressure can be predicted by application of mathematical models, due to thermodynamic behavior of hydrate phenomenon. A number of thermodynamical approaches along with some mathematical techniques (analytical and numerical methods) have been used to estimate hydrate formation temperature. However, there are also a variety of other techniques which have not been investigated. Application of genetic programming in developing predictive models seems novel. In the present study, three new data-based models were produced for estimation of hydrate formation temperature of natural gas, as functions of equilibrium pressure and gas molecular weight by implementation of genetic programming methodology. A total of 891 experimental data covering large range of temperatures (10.31–89.33 °F), pressures (8.1511–10,004.7 psi) and molecular weights (16.04–58.12 g/mol) were collected from the literature and used in correlation developing. The correlation coefficient (R2 = 0.9673), root-mean-square deviation (RMSD = 2.2083 °F) and average absolute relative deviation percent (AARD = 3.0830%) show that the genetic-based new models have acceptable accuracy and efficiency.