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Comparative analysis of hydrate formation pressure applying cubic Equations of State (EoS), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)

Year 2012, Volume: 15 Issue: 2, 91 - 101, 17.05.2012

Abstract

The objective of this work is making comparison between thermodynamic models and data-driven techniques accuracy in prediction of hydrate formation pressure as a function of temperature and composition of gas mixtures. The Peng-Rabinson (PR) and Patel-Teja (PT) equations of state are used for thermodynamic modeling and Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are used as data-driven models. The capability of each method is evaluated by comparison with the experimental data collected from literature. It is shown that there is a good agreement between thermodynamic modeling and the experimental data in most of the cases; however, the prediction relative errors are more than 10% in some cases. The data-driven models are trained and tested using a set of experimental data and their optimum structures are selected based on the prediction error of the test data set. The accuracy of ANN for prediction of hydrate formation pressure is slightly better than those of PR and PT. The prediction errors of ANFIS for all cases are less than 1% which is very promising and proves the potential of ANFIS as a capable tool for prediction of the hydrate formation pressure.

References

  • Avlonitis, D. (1992). Thermodynamics of Gas Hydrate Equilibria. Ph.D. Thesis, Department of Petroleum Engineering, Heriot-Watt University, Edinburgh.
  • Avlonitis, D., Danesh, A., Todd, A.C. (1994). Prediction of VL and VLL Equilibria of Mixtures Containing Petroleum Reservoir Fluids and Methanol with a Cubic EoS. Fluid Phase Equil., 94, 181-216.
  • Beale, R., Jackson, T. (1990). Neural Computing: An Introduction. First ed., IOP Publishing Ltd., UK.
  • Chapoy, A., Coquelet, C., Richon, D. (2003). Solubility Measurement and Modeling of Methane/Water Binary System at Temperatures from 283.15 to 318.15 K and Pressures up to 35 MPa. Fluid Phase Equil., 214, 101– 117.
  • Chapoy, A., Haghighi, H., Burgass, R., Tohidi, B. (2010). Gas Hydrates in Low Water Content Gases: Experimental Measurements and Modelling Using the CPA Equation of State. J. Chem. Therm., 40, 1030– 1037.
  • Chapoy, A., Haghighi, H., Tohidi, B. (2008). Development of a Henry’s Constant Correlation and Solubility Measurements of N-Pentane, I-Pentane, Cyclopentane, N-Hexane, and Toluene in Water. J. Chem. Therm., 40, 1030-1037.
  • Chapoy, A., Mohammadi, A.H., Richon, D. (2007). Predicting the Hydrate Stability Zones of Natural Gases Using Artificial Neural Networks. Oil Gas Sci. Tech., 62, 701–706.
  • Danesh, A. (1998). PVT and Phase Behaviour of Petroleum Reservoir Fluids. Elsevier, Amsterdam. Das, A., Maiti, J., Banerjee, R. N. (2010). Process Control Strategies for a Steel Making Furnace Using ANN with Bayesian Regularization and ANFIS. Expert Syst. Appl., 37, 1075–1085.
  • Demuth, H., Beale, M. (2002). Neural Network Toolbox User’s Guide. The Math Works Inc.
  • Dharmawardhana, P.B., Parrish, W.R., Sloan, E.D. (1980). Experimental Thermodynamic Parameters for the Prediction of Natural Gas Hydrate Dissociation Conditions. Ind. Eng. Chem. Fund., 19, 410–414.
  • Elgibaly, A.A., Elkamel, A.M. (1998). A New Correlation for Predicting Hydrate Formation Conditions for Various Gas Mixtures and Inhibitors. Fluid Phase Equil., 152, 23–42.
  • Erdogmus, M. (2000). Development of a Modified PatelTeja Equation of State. PhD. Dissertation, Pennsylvania State University, University Park, PA.
  • Esen, H., Inalli, M. (2010). ANN and ANFIS Models for Performance Evaluation of a Vertical Ground Source Heat Pump System. Expert Syst. Appl., 37, 8134–8147.
  • Ferrando, N., Lugo, R., Mougin, P. (2006). Coupling Activity Coefficient Models, Henry Constant Equations, and Equations of State to Calculate Vapor–Liquid and Solid–Liquid Equilibrium Data. Chem. Eng. Process, 45, 773-782.
  • Habiballah, W.A., Startzman, R.A., Barrufet, M.A. (1996). Use of Neural Networks for Prediction of Vapor Liquid Equilibrium K-Values for Light-Hydrocarbon Mixtures. SPE Reservoir Eng., 11, 121–126.
  • Jang, J.S.R. (1993). ANFIS: Adaptive-Network-Based Fuzzy Inference System, IEEE Trans. Syst. Man. Cybern., 23, 665–685.
  • Jang, J.S.R., Gulley, N. (2002). Fuzzy Logic Toolbox User’s Guide. The Math Works Inc.
  • John, V.T., Papadopoulos, K.D., Holder, G.D. (1985). A Generalized Model for Predicting Equilibrium Conditions for Gas Hydrates. AIChE J., 31, 252–259.
  • Kwak, T.Y., Mansoori, G.A. (1986). Van Der Waals Mixing Rules for Cubic Equations of State, Applications for Supercritical Fluid Extraction Modeling. Chem. Eng. Sci., 41, 1303-1309.
  • Marion, G.M., Catling, D.C., Kargel, J.S. (2006). Modeling Gas Hydrate Equilibria in Electrolyte Solutions. Calphad, 30, 248–259.
  • Ma, Q.L., Chen, G.J., Guo, T.M. (2003). Modeling the Gas Hydrate Formation of Inhibitor Containing Systems. Fluid Phase Equil., 205, 291–302.
  • Mohammadi, A. H., Belandria, V., Richon, D. (2010). Use of an Artificial Neural Network Algorithm to Predict Hydrate Dissociation Conditions for Hydrogen + Water and Hydrogen + Tetra-N-Butyl Ammonium Bromide + Water Systems. Chem. Eng. Sci., 65, 4302–4305.
  • Mohammadi, A. H., Richon, D. (2010). Hydrate Phase Equilibria for Hydrogen + Water and Hydrogen + Tetra Hydro Furan + Water Systems: Predictions of Dissociation Conditions Using an Artificial Neural Network Algorithm. Chem. Eng. Sci., 65, 3352–3355.
  • Munck, J., Skjold-Jorgensen, S., Rasmussen, P. (1988). Computations of the Formation of Gas Hydrates. Chem. Eng. Sci., 43, 2661-2672.
  • Paranjpe, S.G., Patil, S.L., Kamath, V.A., Godbole, S.P. (1989). Hydrate Equilibria for Binary and Ternary Mixtures of Methane, Propane, Isobutane, and NButane: Effect of Salinity. SPE Reservoir Eng., 4, 446– 454.
  • Parrish, W.R., Prausnitz, J.M. (1972). Dissociation Pressures of Gas Hydrates Formed by Gas Mixtures. Ind. Eng. Chem. Process Des. Dev., 11, 26–35.
  • Patel, N.C., Teja, A.S. (1982). A New Cubic Equation of State for Fluids and Fluid Mixtures. Chem. Eng. Sci., 37, 463-473.
  • Peng, D.Y., Robinson, D.B. (1976). A New Two Constant Equation of State. Ind. Eng. Chem. Fund., 15, 59-64. Poling, B.E., Prausnitz, J.P., O’Connell, J.P. (2004). The Properties of Gases and Liquids. Fifth ed., McGRAWHILL, New York.
  • Rackett, H.G. (1970). Equation of State for Saturated Liquids. J. Chem. Eng. Data, 15, 514–517. Sloan, E.D., Koh, C. (2008). Clathrate Hydrates of Natural Gases. Third ed., CRC Press.
  • Tohidi, B., Burgass, R.W., Danesh, A., Todd, A.C. (1993). Hydrate Inhibition Effect of Produced Water, Part 1. Ethane and Propane Simple Gas Hydrates. SPE, 255- 264.
  • Valeh-e-Sheyda, P., Yaripour, F., Moradi, G., Saber, M. (2010). Application of Artificial Neural Networks for Estimation of the Reaction Rate in Methanol Dehydration. Ind. Eng. Chem. Res., 49, 4620–4626.
  • Van der Waals, J.H., Platteeuw, J.C. (1959). Clathrate Solutions. Adv. Chem. Phys., 11, 1–57.
Year 2012, Volume: 15 Issue: 2, 91 - 101, 17.05.2012

Abstract

References

  • Avlonitis, D. (1992). Thermodynamics of Gas Hydrate Equilibria. Ph.D. Thesis, Department of Petroleum Engineering, Heriot-Watt University, Edinburgh.
  • Avlonitis, D., Danesh, A., Todd, A.C. (1994). Prediction of VL and VLL Equilibria of Mixtures Containing Petroleum Reservoir Fluids and Methanol with a Cubic EoS. Fluid Phase Equil., 94, 181-216.
  • Beale, R., Jackson, T. (1990). Neural Computing: An Introduction. First ed., IOP Publishing Ltd., UK.
  • Chapoy, A., Coquelet, C., Richon, D. (2003). Solubility Measurement and Modeling of Methane/Water Binary System at Temperatures from 283.15 to 318.15 K and Pressures up to 35 MPa. Fluid Phase Equil., 214, 101– 117.
  • Chapoy, A., Haghighi, H., Burgass, R., Tohidi, B. (2010). Gas Hydrates in Low Water Content Gases: Experimental Measurements and Modelling Using the CPA Equation of State. J. Chem. Therm., 40, 1030– 1037.
  • Chapoy, A., Haghighi, H., Tohidi, B. (2008). Development of a Henry’s Constant Correlation and Solubility Measurements of N-Pentane, I-Pentane, Cyclopentane, N-Hexane, and Toluene in Water. J. Chem. Therm., 40, 1030-1037.
  • Chapoy, A., Mohammadi, A.H., Richon, D. (2007). Predicting the Hydrate Stability Zones of Natural Gases Using Artificial Neural Networks. Oil Gas Sci. Tech., 62, 701–706.
  • Danesh, A. (1998). PVT and Phase Behaviour of Petroleum Reservoir Fluids. Elsevier, Amsterdam. Das, A., Maiti, J., Banerjee, R. N. (2010). Process Control Strategies for a Steel Making Furnace Using ANN with Bayesian Regularization and ANFIS. Expert Syst. Appl., 37, 1075–1085.
  • Demuth, H., Beale, M. (2002). Neural Network Toolbox User’s Guide. The Math Works Inc.
  • Dharmawardhana, P.B., Parrish, W.R., Sloan, E.D. (1980). Experimental Thermodynamic Parameters for the Prediction of Natural Gas Hydrate Dissociation Conditions. Ind. Eng. Chem. Fund., 19, 410–414.
  • Elgibaly, A.A., Elkamel, A.M. (1998). A New Correlation for Predicting Hydrate Formation Conditions for Various Gas Mixtures and Inhibitors. Fluid Phase Equil., 152, 23–42.
  • Erdogmus, M. (2000). Development of a Modified PatelTeja Equation of State. PhD. Dissertation, Pennsylvania State University, University Park, PA.
  • Esen, H., Inalli, M. (2010). ANN and ANFIS Models for Performance Evaluation of a Vertical Ground Source Heat Pump System. Expert Syst. Appl., 37, 8134–8147.
  • Ferrando, N., Lugo, R., Mougin, P. (2006). Coupling Activity Coefficient Models, Henry Constant Equations, and Equations of State to Calculate Vapor–Liquid and Solid–Liquid Equilibrium Data. Chem. Eng. Process, 45, 773-782.
  • Habiballah, W.A., Startzman, R.A., Barrufet, M.A. (1996). Use of Neural Networks for Prediction of Vapor Liquid Equilibrium K-Values for Light-Hydrocarbon Mixtures. SPE Reservoir Eng., 11, 121–126.
  • Jang, J.S.R. (1993). ANFIS: Adaptive-Network-Based Fuzzy Inference System, IEEE Trans. Syst. Man. Cybern., 23, 665–685.
  • Jang, J.S.R., Gulley, N. (2002). Fuzzy Logic Toolbox User’s Guide. The Math Works Inc.
  • John, V.T., Papadopoulos, K.D., Holder, G.D. (1985). A Generalized Model for Predicting Equilibrium Conditions for Gas Hydrates. AIChE J., 31, 252–259.
  • Kwak, T.Y., Mansoori, G.A. (1986). Van Der Waals Mixing Rules for Cubic Equations of State, Applications for Supercritical Fluid Extraction Modeling. Chem. Eng. Sci., 41, 1303-1309.
  • Marion, G.M., Catling, D.C., Kargel, J.S. (2006). Modeling Gas Hydrate Equilibria in Electrolyte Solutions. Calphad, 30, 248–259.
  • Ma, Q.L., Chen, G.J., Guo, T.M. (2003). Modeling the Gas Hydrate Formation of Inhibitor Containing Systems. Fluid Phase Equil., 205, 291–302.
  • Mohammadi, A. H., Belandria, V., Richon, D. (2010). Use of an Artificial Neural Network Algorithm to Predict Hydrate Dissociation Conditions for Hydrogen + Water and Hydrogen + Tetra-N-Butyl Ammonium Bromide + Water Systems. Chem. Eng. Sci., 65, 4302–4305.
  • Mohammadi, A. H., Richon, D. (2010). Hydrate Phase Equilibria for Hydrogen + Water and Hydrogen + Tetra Hydro Furan + Water Systems: Predictions of Dissociation Conditions Using an Artificial Neural Network Algorithm. Chem. Eng. Sci., 65, 3352–3355.
  • Munck, J., Skjold-Jorgensen, S., Rasmussen, P. (1988). Computations of the Formation of Gas Hydrates. Chem. Eng. Sci., 43, 2661-2672.
  • Paranjpe, S.G., Patil, S.L., Kamath, V.A., Godbole, S.P. (1989). Hydrate Equilibria for Binary and Ternary Mixtures of Methane, Propane, Isobutane, and NButane: Effect of Salinity. SPE Reservoir Eng., 4, 446– 454.
  • Parrish, W.R., Prausnitz, J.M. (1972). Dissociation Pressures of Gas Hydrates Formed by Gas Mixtures. Ind. Eng. Chem. Process Des. Dev., 11, 26–35.
  • Patel, N.C., Teja, A.S. (1982). A New Cubic Equation of State for Fluids and Fluid Mixtures. Chem. Eng. Sci., 37, 463-473.
  • Peng, D.Y., Robinson, D.B. (1976). A New Two Constant Equation of State. Ind. Eng. Chem. Fund., 15, 59-64. Poling, B.E., Prausnitz, J.P., O’Connell, J.P. (2004). The Properties of Gases and Liquids. Fifth ed., McGRAWHILL, New York.
  • Rackett, H.G. (1970). Equation of State for Saturated Liquids. J. Chem. Eng. Data, 15, 514–517. Sloan, E.D., Koh, C. (2008). Clathrate Hydrates of Natural Gases. Third ed., CRC Press.
  • Tohidi, B., Burgass, R.W., Danesh, A., Todd, A.C. (1993). Hydrate Inhibition Effect of Produced Water, Part 1. Ethane and Propane Simple Gas Hydrates. SPE, 255- 264.
  • Valeh-e-Sheyda, P., Yaripour, F., Moradi, G., Saber, M. (2010). Application of Artificial Neural Networks for Estimation of the Reaction Rate in Methanol Dehydration. Ind. Eng. Chem. Res., 49, 4620–4626.
  • Van der Waals, J.H., Platteeuw, J.C. (1959). Clathrate Solutions. Adv. Chem. Phys., 11, 1–57.
There are 32 citations in total.

Details

Primary Language English
Journal Section Regular Original Research Article
Authors

Nassim Zeinali This is me

Azadeh Ameri This is me

Mohammad Saber

Publication Date May 17, 2012
Published in Issue Year 2012 Volume: 15 Issue: 2

Cite

APA Zeinali, N., Ameri, A., & Saber, M. (2012). Comparative analysis of hydrate formation pressure applying cubic Equations of State (EoS), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). International Journal of Thermodynamics, 15(2), 91-101.
AMA Zeinali N, Ameri A, Saber M. Comparative analysis of hydrate formation pressure applying cubic Equations of State (EoS), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). International Journal of Thermodynamics. May 2012;15(2):91-101.
Chicago Zeinali, Nassim, Azadeh Ameri, and Mohammad Saber. “Comparative Analysis of Hydrate Formation Pressure Applying Cubic Equations of State (EoS), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)”. International Journal of Thermodynamics 15, no. 2 (May 2012): 91-101.
EndNote Zeinali N, Ameri A, Saber M (May 1, 2012) Comparative analysis of hydrate formation pressure applying cubic Equations of State (EoS), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). International Journal of Thermodynamics 15 2 91–101.
IEEE N. Zeinali, A. Ameri, and M. Saber, “Comparative analysis of hydrate formation pressure applying cubic Equations of State (EoS), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)”, International Journal of Thermodynamics, vol. 15, no. 2, pp. 91–101, 2012.
ISNAD Zeinali, Nassim et al. “Comparative Analysis of Hydrate Formation Pressure Applying Cubic Equations of State (EoS), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)”. International Journal of Thermodynamics 15/2 (May 2012), 91-101.
JAMA Zeinali N, Ameri A, Saber M. Comparative analysis of hydrate formation pressure applying cubic Equations of State (EoS), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). International Journal of Thermodynamics. 2012;15:91–101.
MLA Zeinali, Nassim et al. “Comparative Analysis of Hydrate Formation Pressure Applying Cubic Equations of State (EoS), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)”. International Journal of Thermodynamics, vol. 15, no. 2, 2012, pp. 91-101.
Vancouver Zeinali N, Ameri A, Saber M. Comparative analysis of hydrate formation pressure applying cubic Equations of State (EoS), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). International Journal of Thermodynamics. 2012;15(2):91-101.