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

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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.

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Abbreviations

AARD%:

Average absolute relative deviation

ANN:

Artificial neural network

ARD%:

Absolute relative deviation

GP:

Genetic programming

GRN:

Generalized regression neural networks

HFT:

Hydrate formation temperature

ICA:

Imperialist competitive algorithm

M w :

Molecular weight

n :

Number of samples in the dataset

P :

Pressure

PSO:

Particle swarm optimization

R 2 :

Squared correlation coefficient

RMSD:

Root-mean-square deviation

T :

Temperature

x i :

Mole fraction of compound i in the hydrate phase

y i :

Mole fraction of compound i in natural gas

\(y_{i}^{{{\text{cal}}.}}\) :

Predicted dependent variables

\(y_{i}^{\exp .}\) :

Experimental dependent variable

\(\bar{y}^{\exp .}\) :

Average of experimental dependent variables

γ g :

Gas specific gravity

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Correspondence to Ehsan Khamehchi.

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Abooali, D., Khamehchi, E. New predictive method for estimation of natural gas hydrate formation temperature using genetic programming. Neural Comput & Applic 31, 2485–2494 (2019). https://doi.org/10.1007/s00521-017-3208-0

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