Diffusion coefficient of carbon dioxide (CO2), a significant parameter describing the mass transfer process, exerts a profound influence on the safety of CO2 storage in depleted reservoirs, saline aquifers, and marine ecosystems. However, experimental determination of diffusion coefficient in CO2-brine system is time-consuming and complex because the procedure requires sophisticated laboratory equipment and reasonable interpretation methods. To facilitate the acquisition of more accurate values, an intelligent model, termed MKSVM-GA, is developed using a hybrid technique of support vector machine (SVM), mixed kernels (MK), and genetic algorithm (GA). Confirmed by the statistical evaluation indicators, our proposed model exhibits excellent performance with high accuracy and strong robustness in a wide range of temperatures (273–473.15 K), pressures (0.1–49.3 MPa), and viscosities (0.139–1.950 mPa·s). Our results show that the proposed model is more applicable than the artificial neural network (ANN) model at this sample size, which is superior to four commonly used traditional empirical correlations. The technique presented in this study can provide a fast and precise prediction of CO2 diffusivity in brine at reservoir conditions for the engineering design and the technical risk assessment during the process of CO2 injection.

References

References
1.
Ota
,
M.
,
Saito
,
T.
,
Aida
,
T.
,
Watanabe
,
M.
,
Sato
,
Y.
,
Smith
,
R. L.
, and
Inomata
,
H.
,
2007
, “
Macro and Microscopic CH4-CO2 Replacement in CH4 Hydrate Under Pressurized CO2
,”
AIChE J.
,
53
(
10
), pp.
2715
2721
.
2.
Bai
,
D.
,
Zhang
,
X.
,
Chen
,
G.
, and
Wang
,
W.
,
2012
, “
Replacement Mechanism of Methane Hydrate With Carbon Dioxide From Microsecond Molecular Dynamics Simulations
,”
Energy Environ. Sci.
,
5
(
5
), pp.
7033
7041
.
3.
Pruess
,
K.
,
2006
, “
Enhanced Geothermal Systems (EGS) Using CO2 as Working Fluid—A Novel Approach for Generating Renewable Energy With Simultaneous Sequestration of Carbon
,”
Geothermics
,
35
(
4
), pp.
351
367
.
4.
Zhang
,
L.
,
Cui
,
G.
,
Zhang
,
Y.
,
Ren
,
B.
,
Ren
,
S.
, and
Wang
,
X.
,
2016
, “
Influence of Pore Water on the Heat Mining Performance of Supercritical CO2 Injected for Geothermal Development
,”
J. CO2 Util.
,
16
, pp.
287
300
.
5.
Cui
,
G.
,
Ren
,
S.
,
Rui
,
Z.
,
Ezekiel
,
J.
,
Zhang
,
L.
, and
Wang
,
H.
,
2018
, “
The Influence of Complicated Fluid-Rock Interactions on the Geothermal Exploitation in the CO2 Plume Geothermal System
,”
Appl. Energy
,
227
, pp.
49
63
.
6.
Mohamed
,
I. M.
,
He
,
J.
, and
Nasr-El-Din
,
H. A.
,
2012
, “
Experimental Analysis of CO2 Injection on Permeability of Vuggy Carbonate Aquifers
,”
ASME J. Energy Resour. Technol.
,
135
(
1
), p.
013301
.
7.
Cui
,
G.
,
Wang
,
Y.
,
Rui
,
Z.
,
Chen
,
B.
,
Ren
,
S.
, and
Zhang
,
L.
,
2018
, “
Assessing the Combined Influence of Fluid-Rock Interactions on Reservoir Properties and Injectivity During CO2 Storage in Saline Aquifers
,”
Energy
,
155
, pp.
281
296
.
8.
Rau
,
G. H.
, and
Caldeira
,
K.
,
1999
, “
Enhanced Carbonate Dissolution: A Means of Sequestering Waste CO2 as Ocean Bicarbonate
,”
Energy Convers. Manage.
,
40
(
17
), pp.
1803
1813
.
9.
Chen
,
B.
, and
Reynolds
,
A. C.
,
2018
, “
CO2 Water-Alternating-Gas Injection for Enhanced Oil Recovery: Optimal Well Controls and Half-Cycle Lengths
,”
Comput. Chem. Eng.
,
113
, pp.
44
56
.
10.
Li
,
A.
,
Ren
,
X.
,
Fu
,
S.
,
Lv
,
J.
,
Li
,
X.
,
Liu
,
Y.
, and
Lu
,
Y.
,
2018
, “
The Experimental Study on the Flooding Regularities of Various CO2 Flooding Modes Implemented on Ultralow Permeability Cores
,”
ASME J. Energy Resour. Technol.
,
140
(
7
), p.
072902
.
11.
Ren
,
B.
,
Zhang
,
L.
,
Huang
,
H.
,
Ren
,
S.
,
Chen
,
G.
, and
Zhang
,
H.
,
2015
, “
Performance Evaluation and Mechanisms Study of Near-Miscible CO2 Flooding in a Tight Oil Reservoir of Jilin Oilfield China
,”
J. Nat. Gas Sci. Eng.
,
27
, pp.
1796
1805
.
12.
Li
,
S.
,
Li
,
B.
,
Zhang
,
Q.
,
Li
,
Z.
, and
Yang
,
D.
,
2018
, “
Effect of CO2 on Heavy Oil Recovery and Physical Properties in Huff-n-Puff Processes Under Reservoir Conditions
,”
ASME J. Energy Resour. Technol.
,
140
(
7
), p.
072907
.
13.
Mutoru
,
J. W.
,
Leahy-Dios
,
A.
, and
Firoozabadi
,
A.
,
2011
, “
Modeling Infinite Dilution and Fickian Diffusion Coefficients of Carbon Dioxide in Water
,”
AIChE J.
,
57
(
6
), pp.
1617
1627
.
14.
Farajzadeh
,
R.
,
Zitha
,
P. L.
, and
Bruining
,
J.
,
2009
, “
Enhanced Mass Transfer of CO2 Into Water: Experiment and Modeling
,”
Ind. Eng. Chem. Res.
,
48
(
13
), pp.
6423
6431
.
15.
Trevisan
,
L.
,
Pini
,
R.
,
Cihan
,
A.
,
Birkholzer
,
J. T.
,
Zhou
,
Q.
, and
Illangasekare
,
T. H.
,
2014
, “
Experimental Investigation of Supercritical CO2 Trapping Mechanisms at the Intermediate Laboratory Scale in Well-Defined Heterogeneous Porous Media
,”
Energy Procedia
,
63
, pp.
5646
5653
.
16.
Cadogan
,
S. P.
,
Hallett
,
J. P.
,
Maitland
,
G. C.
, and
Trusler
,
J. M.
,
2014
, “
Diffusion Coefficients of Carbon Dioxide in Brines Measured Using 13C Pulsed-Field Gradient Nuclear Magnetic Resonance
,”
J. Chem. Eng. Data
,
60
(
1
), pp.
181
184
.
17.
Vivian
,
J. E.
, and
King
,
C. J.
,
1964
, “
Diffusivities of Slightly Soluble Gases in Water
,”
AIChE J.
,
10
(
2
), pp.
220
221
.
18.
Mazarei
,
A. F.
, and
Sandall
,
O. C.
,
1980
, “
Diffusion Coefficients for Helium, Hydrogen, and Carbon Dioxide in Water at 25 °C
,”
AIChE J.
,
26
(
1
), pp.
154
157
.
19.
Maharajh
,
D. M.
, and
Walkley
,
J.
,
1973
, “
The Temperature Dependence of the Diffusion Coefficients of Ar, CO2, CH4, CH3Cl, CH3Br, and CHCl2F in Water
,”
Can. J. Chem.
,
51
(
6
), pp.
944
952
.
20.
Tamimi
,
A.
,
Rinker
,
E. B.
, and
Sandall
,
O. C.
,
1994
, “
Diffusion Coefficients for Hydrogen Sulfide, Carbon Dioxide, and Nitrous Oxide in Water Over the Temperature Range 293-368 K
,”
J. Chem. Eng. Data
,
39
(
2
), pp.
330
332
.
21.
Frank
,
M. J.
,
Kuipers
,
J. A.
, and
van Swaaij
,
W. P.
,
1996
, “
Diffusion Coefficients and Viscosities of CO2 + H2O, CO2 + CH3OH, NH3 + H2O, and NH3 + CH3OH Liquid Mixtures
,”
J. Chem. Eng. Data
,
41
(
2
), pp.
297
302
.
22.
Ng
,
W. Y.
, and
Walkley
,
J.
,
1969
, “
Diffusion of Gases in Liquids: The Constant Size Bubble Method
,”
Can. J. Chem.
,
47
(
6
), pp.
1075
1077
.
23.
Jähne
,
B.
,
Heinz
,
G.
, and
Dietrich
,
W.
,
1987
, “
Measurement of the Diffusion Coefficients of Sparingly Soluble Gases in Water
,”
J. Geophys. Res.
,
92
(
C10
), pp.
10767
10776
.
24.
Hirai
,
S.
,
Okazaki
,
K.
,
Yazawa
,
H.
,
Ito
,
H.
,
Tabe
,
Y.
, and
Hijikata
,
K.
,
1997
, “
Measurement of CO2 Diffusion Coefficient and Application of LIF in Pressurized Water
,”
Energy
,
22
(
2–3
), pp.
363
367
.
25.
Guzmán
,
J.
, and
Garrido
,
L.
,
2012
, “
Determination of Carbon Dioxide Transport Coefficients in Liquids and Polymers by NMR Spectroscopy
,”
J. Phys. Chem. B
,
116
(
20
), pp.
6050
6058
.
26.
Liger-Belair
,
G.
,
Prost
,
E.
,
Parmentier
,
M.
,
Jeandet
,
P.
, and
Nuzillard
,
J. M.
,
2003
, “
Diffusion Coefficient of CO2 Molecules as Determined by 13C NMR in Various Carbonated Beverages
,”
J. Agric. Food Chem.
,
51
(
26
), pp.
7560
7563
.
27.
Maharajh
,
D.
,
1973
, “
Solubility and Diffusion of Gases in Water
,” Ph.D. thesis, Simon Fraser University, Burnaby, BC, Canada.
28.
Versteeg
,
G. F.
, and
Van Swaalj
,
W.
,
1988
, “
Solubility and Diffusivity of Acid Gases (Carbon Dioxide, Nitrous Oxide) in Aqueous Alkanolamine Solutions
,”
J. Chem. Eng. Data
,
33
(
1
), pp.
29
34
.
29.
Himmelblau
,
D. M.
,
1964
, “
Diffusion of Dissolved Gases in Liquids
,”
Chem. Rev.
,
64
(
5
), pp.
527
550
.
30.
Thomas
,
W. J.
, and
Adams
,
M. J.
,
1965
, “
Measurement of the Diffusion Coefficients of Carbon Dioxide and Nitrous Oxide in Water and Aqueous Solutions of Glycerol
,”
Trans. Faraday Soc.
,
61
, pp.
668
673
.
31.
Brignole
,
E. A.
, and
Echarte
,
R.
,
1981
, “
Mass Transfer in Laminar Liquid Jets: Measurement of Diffusion Coefficients
,”
Chem. Eng. Sci.
,
36
(
4
), pp.
705
711
.
32.
Nijsing
,
R.
,
Hendriksz
,
R. H.
, and
Kramers
,
H.
,
1959
, “
Absorption of CO2 in Jets and Falling Films of Electrolyte Solutions, With and Without Chemical Reaction
,”
Chem. Eng. Sci.
,
10
(
1–2
), pp.
88
104
.
33.
Tan
,
K. K.
, and
Thorpe
,
R. B.
,
1992
, “
Gas Diffusion Into Viscous and Non-Newtonian Liquids
,”
Chem. Eng. Sci.
,
47
(
13–14
), pp.
3565
3572
.
34.
Tham
,
M. J.
,
Bhatia
,
K. K.
, and
Gubbins
,
K. F.
,
1967
, “
Steady-State Method for Studying Diffusion of Gases in Liquids
,”
Chem. Eng. Sci.
,
22
(
3
), pp.
309
311
.
35.
Vivian
,
J. E.
, and
Peaceman
,
D. W.
,
1956
, “
Liquid-Side Resistance in Gas Absorption
,”
AIChE J.
,
2
(
4
), pp.
437
443
.
36.
Pratt
,
K. C.
,
Slater
,
D. H.
, and
Wakeham
,
W. A.
,
1973
, “
A Rapid Method for the Determination of Diffusion Coefficients of Gases in Liquids
,”
Chem. Eng. Sci.
,
28
(
10
), pp.
1901
1903
.
37.
Bodnar
,
L. H.
, and
Himmelblau
,
D. M.
,
1962
, “
Continuous Measurement of Diffusion Coefficients of Gases in Liquids Using Glass Scintillators
,”
Int. J. Appl. Radiat. Isot.
,
13
(
1
), pp.
1
6
.
38.
Choudhari
,
R.
, and
Doraiswamy
,
L. K.
,
1972
, “
Physical Properties in Reaction of Ethylene and Hydrogen Chloride in Liquid Media. Diffusivities and Solubilities
,”
J. Chem. Eng. Data
,
17
(
4
), pp.
428
432
.
39.
Reddy
,
K. A.
, and
Doraiswamy
,
L. K.
,
1967
, “
Estimating Liquid Diffusivity
,”
Ind. Eng. Chem. Fundam.
,
6
(
1
), pp.
77
79
.
40.
Lu
,
W.
,
Guo
,
H.
,
Chou
,
I. M.
,
Burruss
,
R. C.
, and
Li
,
L.
,
2013
, “
Determination of Diffusion Coefficients of Carbon Dioxide in Water Between 268 and 473 K in a High-Pressure Capillary Optical Cell With in Situ Raman Spectroscopic Measurements
,”
Geochim. Cosmochim. Acta
,
115
, pp.
183
204
.
41.
Cadogan
,
S. P.
,
Maitland
,
G. C.
, and
Trusler
,
J. M.
,
2014
, “
Diffusion Coefficients of CO2 and N2 in Water at Temperatures Between 298.15 K and 423.15 K at Pressures Up to 45 MPa
,”
J. Chem. Eng. Data
,
59
(
2
), pp.
519
525
.
42.
Jang
,
H. W.
,
Yang
,
D.
, and
Li
,
H.
,
2018
, “
A Power-Law Mixing Rule for Predicting Apparent Diffusion Coefficients of Binary Gas Mixtures in Heavy Oil
,”
ASME J. Energy Resour. Technol.
,
140
(
5
), p.
052904
.
43.
Shi
,
Y.
,
Zheng
,
S.
, and
Yang
,
D.
,
2017
, “
Determination of Individual Diffusion Coefficients of Alkane Solvent(s)–CO2–Heavy Oil Systems With Consideration of Natural Convection Induced by Swelling Effect
,”
Int. J. Heat Mass Transfer
,
107
, pp.
572
585
.
44.
Zheng
,
S.
, and
Yang
,
D.
,
2017
, “
Experimental and Theoretical Determination of Diffusion Coefficients of CO2-Heavy Oil Systems by Coupling Heat and Mass Transfer
,”
ASME J. Energy Resour. Technol.
,
139
(
2
), p.
022901
.
45.
Zheng
,
S.
, and
Yang
,
D.
,
2017
, “
Determination of Individual Diffusion Coefficients of C3H8/n-C4H10/CO2/Heavy-Oil Systems at High Pressures and Elevated Temperatures by Dynamic Volume Analysis
,”
SPE J.
,
22
, pp.
799
816
.
46.
Li
,
H. A.
,
Sun
,
H.
, and
Yang
,
D.
,
2017
, “
Effective Diffusion Coefficients of Gas Mixture in Heavy Oil Under Constant-Pressure Conditions
,”
Heat Mass Transfer
,
53
(
5
), pp.
1527
1540
.
47.
Zheng
,
S.
,
Sun
,
H.
, and
Yang
,
D.
,
2016
, “
Coupling Heat and Mass Transfer for Determining Individual Diffusion Coefficient of a Hot C3H8–CO2 Mixture in Heavy Oil Under Reservoir Conditions
,”
Int. J. Heat Mass Transfer
,
102
, pp.
251
263
.
48.
Zheng
,
S.
,
Li
,
H. A.
,
Sun
,
H.
, and
Yang
,
D.
,
2016
, “
Determination of Diffusion Coefficient for Alkane Solvent–CO2 Mixtures in Heavy Oil With Consideration of Swelling Effect
,”
Ind. Eng. Chem. Res.
,
55
(
6
), pp.
1533
1549
.
49.
Li
,
H.
, and
Yang
,
D.
,
2016
, “
Determination of Individual Diffusion Coefficients of Solvent/CO2 Mixture in Heavy Oil With Pressure-Decay Method
,”
SPE J.
,
21
(
1
), pp.
131
143
.
50.
Sun
,
H.
,
Li
,
H.
, and
Yang
,
D.
,
2014
, “
Coupling Heat and Mass Transfer for a Gas Mixture–Heavy Oil System at High Pressures and Elevated Temperatures
,”
Int. J. Heat Mass Transfer
,
74
, pp.
173
184
.
51.
Yang
,
D.
,
Tontiwachwuthikul
,
P.
, and
Gu
,
Y.
,
2006
, “
Dynamic Interfacial Tension Method for Measuring Gas Diffusion Coefficient and Interface Mass Transfer Coefficient in a Liquid
,”
Ind. Eng. Chem. Res.
,
45
(
14
), pp.
4999
5008
.
52.
Othmer
,
D. F.
, and
Thakar
,
M. S.
,
1953
, “
Correlating Diffusion Coefficient in Liquids
,”
Ind. Eng. Chem.
,
45
(
3
), pp.
589
593
.
53.
Wilke
,
C. R.
, and
Chang
,
P.
,
1955
, “
Correlation of Diffusion Coefficients in Dilute Solutions
,”
AIChE J.
,
1
(
2
), pp.
264
270
.
54.
Moultos
,
O. A.
,
Tsimpanogiannis
,
I. N.
,
Panagiotopoulos
,
A. Z.
, and
Economou
,
I. G.
,
2016
, “
Self-Diffusion Coefficients of the Binary (H2O + CO2) Mixture at High Temperatures and Pressures
,”
J. Chem. Thermodyn.
,
93
, pp.
424
429
.
55.
Cadogan
,
S.
,
2015
, “
Diffusion of CO2 in Fluids Relevant to Carbon Capture, Utilisation and Storage
,”
Ph.D. thesis
, Imperial College London, London.https://core.ac.uk/download/pdf/77007460.pdf
56.
Shokrollahi
,
A.
,
Arabloo
,
M.
,
Gharagheizi
,
F.
, and
Mohammadi
,
A. H.
,
2013
, “
Intelligent Model for Prediction of CO2–Reservoir Oil Minimum Miscibility Pressure
,”
Fuel
,
112
, pp.
375
384
.
57.
Le Van
,
S.
, and
Chon
,
B. H.
,
2018
, “
Effective Prediction and Management of a CO2 Flooding Process for Enhancing Oil Recovery Using Artificial Neural Networks
,”
ASME J. Energy Resour. Technol.
,
140
(
3
), p.
032906
.
58.
Zhang
,
J.
,
Feng
,
Q.
,
Zhang
,
X.
,
Zhang
,
X.
,
Yuan
,
N.
,
Wen
,
S.
,
Wang
,
S.
, and
Zhang
,
A.
,
2015
, “
The Use of an Artificial Neural Network to Estimate Natural Gas/Water Interfacial Tension
,”
Fuel
,
157
, pp.
28
36
.
59.
Khaksar Manshad
,
A.
,
Rostami
,
H.
,
Moein Hosseini
,
S.
, and
Rezaei
,
H.
,
2016
, “
Application of Artificial Neural Network–Particle Swarm Optimization Algorithm for Prediction of Gas Condensate Dew Point Pressure and Comparison With Gaussian Processes Regression–Particle Swarm Optimization Algorithm
,”
ASME J. Energy Resour. Technol.
,
138
(
3
), p.
032903
.
60.
Paul
,
A.
,
Bhowmik
,
S.
,
Panua
,
R.
, and
Debroy
,
D.
,
2018
, “
Artificial Neural Network-Based Prediction of Performances-Exhaust Emissions of Diesohol Piloted Dual Fuel Diesel Engine Under Varying Compressed Natural Gas Flowrates
,”
ASME J. Energy Resour. Technol.
,
140
(
11
), p.
112201
.
61.
Chen
,
B.
,
Harp
,
D. R.
,
Lin
,
Y.
,
Keating
,
E. H.
, and
Pawar
,
R. J.
,
2018
, “
Geologic CO2 Sequestration Monitoring Design: A Machine Learning and Uncertainty Quantification Based Approach
,”
Appl. Energy
,
225
, pp.
332
345
.
62.
Kamari
,
A.
,
Arabloo
,
M.
,
Shokrollahi
,
A.
,
Gharagheizi
,
F.
, and
Mohammadi
,
A. H.
,
2015
, “
Rapid Method to Estimate the Minimum Miscibility Pressure (MMP) in Live Reservoir Oil Systems During CO2 Flooding
,”
Fuel
,
153
, pp.
310
319
.
63.
Tatar
,
A.
,
Barati-Harooni
,
A.
,
Najafi-Marghmaleki
,
A.
,
Najafi-Marghmaleki
,
A.
,
Mohebbi
,
A.
,
Ghiasi
,
M. M.
,
Mohammadi
,
A. H.
, and
Hajinezhad
,
A.
,
2016
, “
Comparison of Two Soft Computing Approaches for Predicting CO2 Solubility in Aqueous Solution of Piperazine
,”
Int. J. Greenhouse Gas Control
,
53
, pp.
85
97
.
64.
Linstrom
,
P. J.
, and
Mallard
,
W. G. E.
, 2016, “
NIST Chemistry WebBook
,” National Institute of Standards and Technology, Gaithersburg, MD, accessed Aug. 17, 2016, NIST Standard Reference Database No. 69, http://webbook.nist.gov
65.
Cortes
,
C.
, and
Vapnik
,
V.
,
1995
, “
Support-Vector Networks
,”
Mach. Learn.
,
20
(
3
), pp.
273
297
.
66.
Vapnik
,
V.
,
1995
,
The Nature of Statistical Learning Theory
,
Springer
,
New York
.
67.
Boser
,
B. E.
,
Guyon
,
I. M.
, and
Vapnik
,
V. N.
,
1992
, “
A Training Algorithm for Optimal Margin Classifiers
,” Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, PA, July 27–29, pp.
144
152
.
68.
Drucker
,
H.
,
Burges
,
C. J.
,
Kaufman
,
L.
,
Smola
,
A.
, and
Vapnik
,
V.
,
1997
, “
Support Vector Regression Machines
,”
Advances in Neural Information Processing Systems 9
, MIT Press, Cambridge, MA, pp. 155–161.
69.
Geng
,
Y.
,
Chen
,
J.
,
Fu
,
R.
,
Bao
,
G.
, and
Pahlavan
,
K.
,
2016
, “
Enlighten Wearable Physiological Monitoring Systems: On-Body of Characteristics Based Human Motion Classification Using a Support Vector Machine
,”
IEEE Trans. Mobile Comput.
,
15
(
3
), pp.
656
671
.
70.
Lee
,
Y. J.
, and
Mangasarian
,
O. L.
,
2001
, “
SSVM: A Smooth Support Vector Machine for Classification
,”
Comput. Optim. Appl.
,
20
, pp.
5
22
.
71.
Bian
,
X. Q.
,
Han
,
B.
,
Du
,
Z. M.
,
Jaubert
,
J. N.
, and
Li
,
M. J.
,
2016
, “
Integrating Support Vector Regression With Genetic Algorithm for CO2-Oil Minimum Miscibility Pressure (MMP) in Pure and Impure CO2 Streams
,”
Fuel
,
182
, pp.
550
557
.
72.
Filgueiras
,
P. R.
,
Portela
,
N. A.
,
Silva
,
S. R.
,
Castro
,
E. V.
,
Oliveira
,
L. M.
,
Dias
,
J. C.
,
Neto
,
A. C.
,
Romão
,
W.
, and
Poppi
,
R. J.
,
2016
, “
Determination of Saturates, Aromatics, and Polars in Crude Oil by 13C NMR and Support Vector Regression With Variable Selection by Genetic Algorithm
,”
Energy Fuels
,
30
(
3
), pp.
1972
1978
.
73.
Fiacco
,
A. V.
, and
McCormick
,
G. P.
,
1964
, “
The Sequential Unconstrained Minimization Technique for Nonlinear Programing, A Primal-Dual Method
,”
Manag. Sci.
,
10
(
2
), pp.
360
366
.
74.
Smola
,
A. J.
, and
Schölkopf
,
B.
,
2004
, “
A Tutorial on Support Vector Regression
,”
Stat. Comput.
,
14
(
3
), pp.
199
222
.
75.
Schölkopf
,
B.
, and
Burges
,
C. J.
,
1999
,
Advances in Kernel Methods: Support Vector Learning
,
MIT Press
,
Cambridge, MA
.
76.
Tehrany
,
M. S.
,
Pradhan
,
B.
, and
Jebur
,
M. N.
,
2014
, “
Flood Susceptibility Mapping Using a Novel Ensemble Weights-of-Evidence and Support Vector Machine Models in GIS
,”
J. Hydrol.
,
512
, pp.
332
343
.
77.
Smits
,
G. F.
, and
Jordaan
,
E. M.
,
2002
, “
Improved SVM Regression Using Mixtures of Kernels
,”
International Joint Conference on Neural Networks
, Vol.
3
, pp.
2785
2790
.
78.
Holland
,
J. H.
,
1992
, “
Genetic Algorithms
,”
Sci. Am.
,
267
(
1
), pp.
66
72
.
79.
Khadse
,
A.
,
Blanchette
,
L.
,
Kapat
,
J.
,
Vasu
,
S.
,
Hossain
,
J.
, and
Donazzolo
,
A.
,
2018
, “
Optimization of Supercritical CO2 Brayton Cycle for Simple Cycle Gas Turbines Exhaust Heat Recovery Using Genetic Algorithm
,”
ASME J. Energy Resour. Technol.
,
140
(
7
), p.
071601
.
80.
Salmachi
,
A.
,
Sayyafzadeh
,
M.
, and
Haghighi
,
M.
,
2013
, “
Infill Well Placement Optimization in Coal Bed Methane Reservoirs Using Genetic Algorithm
,”
Fuel
,
111
, pp.
248
258
.
81.
Velez-Langs
,
O.
,
2005
, “
Genetic Algorithms in Oil Industry: An Overview
,”
J. Pet. Sci. Eng.
,
47
(
1–2
), pp.
15
22
.
82.
Davis
,
L.
,
1991
,
Handbook of Genetic Algorithms
,
Van Nostrand Reinhold
,
New York
.
83.
Chatterjee
,
S.
, and
Hadi
,
A. S.
,
2015
,
Regression Analysis by Example
,
Wiley
,
New York
.
84.
Rousseeuw
,
P. J.
, and
Leroy
,
A. M.
,
2005
,
Robust Regression and Outlier Detection
,
Wiley
,
New York
.
85.
Mohammadi
,
A. H.
,
Eslamimanesh
,
A.
,
Gharagheizi
,
F.
, and
Richon
,
D.
,
2012
, “
A Novel Method for Evaluation of Asphaltene Precipitation Titration Data
,”
Chem. Eng. Sci.
,
78
, pp.
181
185
.
86.
Mohammadi
,
A. H.
,
Gharagheizi
,
F.
,
Eslamimanesh
,
A.
, and
Richon
,
D.
,
2012
, “
Evaluation of Experimental Data for Wax and Diamondoids Solubility in Gaseous Systems
,”
Chem. Eng. Sci.
,
81
, pp.
1
7
.
87.
Feng
,
Q.
,
Zhang
,
J.
,
Zhang
,
X.
, and
Wen
,
S.
,
2015
, “
Proximate Analysis Based Prediction of Gross Calorific Value of Coals: A Comparison of Support Vector Machine, Alternating Conditional Expectation and Artificial Neural Network
,”
Fuel Process. Technol.
,
129
, pp.
120
129
.
88.
Togun
,
N. K.
, and
Baysec
,
S.
,
2010
, “
Prediction of Torque and Specific Fuel Consumption of a Gasoline Engine by Using Artificial Neural Networks
,”
Appl. Energy
,
87
(
1
), pp.
349
355
.
89.
Pradhan
,
B.
, and
Lee
,
S.
,
2010
, “
Landslide Susceptibility Assessment and Factor Effect Analysis: Backpropagation Artificial Neural Networks and Their Comparison With Frequency Ratio and Bivariate Logistic Regression Modelling
,”
Environ. Modell. Software
,
25
(
6
), pp.
747
759
.
90.
Li
,
Q.
,
Meng
,
Q.
,
Cai
,
J.
,
Yoshino
,
H.
, and
Mochida
,
A.
,
2009
, “
Predicting Hourly Cooling Load in the Building: A Comparison of Support Vector Machine and Different Artificial Neural Networks
,”
Energy Convers. Manage.
,
50
(
1
), pp.
90
96
.
91.
Sorgun
,
M.
,
Murat Ozbayoglu
,
A. A.
, and
Evren Ozbayoglu
,
M. M.
,
2014
, “
Support Vector Regression and Computational Fluid Dynamics Modeling of Newtonian and Non-Newtonian Fluids in Annulus With Pipe Rotation
,”
ASME J. Energy Resour. Technol.
,
137
(
3
), p.
032901
.
92.
Pradhan
,
B.
,
2013
, “
A Comparative Study on the Predictive Ability of the Decision Tree, Support Vector Machine and Neuro-Fuzzy Models in Landslide Susceptibility Mapping Using GIS
,”
Comput. Geosci.
,
51
, pp.
350
365
.
93.
Rostami
,
A.
,
Hemmati-Sarapardeh
,
A.
, and
Shamshirband
,
S.
,
2018
, “
Rigorous Prognostication of Natural Gas Viscosity: Smart Modeling and Comparative Study
,”
Fuel
,
222
, pp.
766
778
.
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