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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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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DOI: https://doi.org/10.1007/s00521-017-3208-0