Evaluating Pressure in Deepwater Gas Pipeline for the Prediction of Natural Gas Hydrate Formation

Authors

  • A. Abbasi Department of Mechanical Engineering, Universiti Teknologi PETRONAS, Malaysia
  • F. Mohd Hashim Department of Mechanical Engineering, Universiti Teknologi PETRONAS, Malaysia
Volume: 9 | Issue: 6 | Pages: 5033-5036 | December 2019 | https://doi.org/10.48084/etasr.3174

Abstract

This paper proposes the prediction of hydrate formation pressure in deepwater pipeline with an approach of intelligent optimization. The proposed novel correlation of hydrate formation is using the function of ordinary differential equation. The developed optimization prediction model was founded on the constant coefficients which were examined by a multiple set of experimental data of methane (CH4), ethane (C2H6), propane (C3H8), iso-butane (iC4), nitrogen (N), Carbon Dioxide (CO2) and hydrogen sulfide (H2S) hydrates. The consequences of this research are highly optimistic for the natural gas production industry.

Keywords:

intelligent optimization techniques, hydrate formation conditions, natural gases, ordinary differential equation

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References

E. G. Hammerschmidt, “Formation of gas hydrates in natural gas transmission lines”, Industrial & Engineering Chemistry, Vol. 26, No. 8, pp. 851-855, 1934 DOI: https://doi.org/10.1021/ie50296a010

J. Carroll, Natural gas hydrate: A guide for engineers, Gulf Professional Publishing, 2003

A. Abbasi, F. M. Hashim, “Hydrate formation prediction model for binary gases of methane+ethane and methane+propane by using optimization algorithm”, Petroleum Science and Technology, pp. 1-7, 2019 DOI: https://doi.org/10.1080/10916466.2019.1655447

R. G. Kobayashi, K. Y. Song, E. D. Sloan, “Phase behavior of water/hydrocarbon systems”, in: Petroleum Engineering Handbook, pp. 25-28, Society of Petroleum Engineers, 1987

A. Abbasi, F. M. Hashim, “A prediction model for the natural gas hydrate formation pressure into transmission line”, Petroleum Science and Technology, Vol. 34, No. 9, pp. 824-831, 2016 DOI: https://doi.org/10.1080/10916466.2016.1170842

A. Abbasi, F. M. Hashim, “Development of a hydrate formation prediction model for sub-sea pipeline”, Petroleum Science and Technology, Vol. 35, No. 5, pp. 443-450, 2017 DOI: https://doi.org/10.1080/10916466.2016.1263210

D. Mahlke, A. Martin, S. Moritz, “A simulated annealing algorithm for transient optimization in gas networks”, Mathematical Methods of Operations Research, Vol. 66, No. 1, pp. 99-115, 2007 DOI: https://doi.org/10.1007/s00186-006-0142-9

E. D. Fatnes, Numerical simulations of the flow and plugging behaviour of hydrate particles, University of Bergen, 2010

W. Duch, J. Korczak, Optimization and global minimization methods suitable for neural networks, Neural Computing Surveys, 1998

Z. Cui, X. Gu, “An improved discrete artificial bee colony algorithm to minimize the makespan on hybrid flow shop problems”, Neurocomputing, Vol. 148, pp. 248-259, 2015 DOI: https://doi.org/10.1016/j.neucom.2013.07.056

R. Z. R. Mercado, C. B. Sanchez, “Optimization problems in natural gas transportation systems: A state-of-the-art review”, Applied Energy, Vol. 147, pp. 536-555, 2015 DOI: https://doi.org/10.1016/j.apenergy.2015.03.017

C. B. Sanchez, Optimization methods for pipeline transportation of natural gas, University of Bergen, 2010

H. Kang, F. Chen, Y. Li, J. Deng, Z. Yang, “Knot calculation for spline fitting via sparse optimization”, Computer-Aided Design, Vol. 58, pp. 179-188, 2015 DOI: https://doi.org/10.1016/j.cad.2014.08.022

D. N. Truong, V. T. Bui, “Hybrid PSO-optimized ANFIS-based model to improve dynamic voltage stability”, Engineering, Technology & Applied Science Research, Vol. 9, No. 4, pp. 4384-4388, 2019 DOI: https://doi.org/10.48084/etasr.2833

K. Soleimani, J. Mazloum, “Designing a GA-based robust controller for load frequency control (LFC)”, Engineering, Technology & Applied Science Research, Vol. 8, No. 2, pp. 2633-2639, 2018 DOI: https://doi.org/10.48084/etasr.1592

A. Bahadori, H. B. Vuthaluru, “A novel correlation for estimation of hydrate forming condition of natural gases”, Journal of Natural Gas Chemistry, Vol. 18, No. 4, pp. 453-457, 2009 DOI: https://doi.org/10.1016/S1003-9953(08)60143-7

M. M. Ghiasi, A. Bahadori, S. Zendehboudi, “Estimation of triethylene glycol (TEG) purity in natural gas dehydration units using fuzzy neural network”, Journal of Natural Gas Science and Engineering, Vol. 17, pp. 26-32, 2014 DOI: https://doi.org/10.1016/j.jngse.2013.12.008

S. Rashid, A. Fayazi, B. Harimi, E. Hamidpour, S. Younesi, “Evolving a robust approach for accurate prediction of methane hydrate formation temperature in the presence of salt inhibitor”, Journal of Natural Gas Science and Engineering, Vol. 18, pp. 194-204, 2014 DOI: https://doi.org/10.1016/j.jngse.2014.02.005

M. Ghavipour, M. Ghavipour, M. Chitsazan, S. H. Najibi, S. S. Ghidary, “Experimental study of natural gas hydrates and a novel use of neural network to predict hydrate formation conditions”, Chemical Engineering Research and Design, Vol. 91, No. 2, pp. 264-273, 2013 DOI: https://doi.org/10.1016/j.cherd.2012.08.010

M. Farzad, H. Tahersima, H. Khaloozadeh, “Predicting the mackey glass chaotic time series using genetic algorithm”, SICE-ICASE International Joint Conference, Busan, South Korea, October 18-21, 2006 DOI: https://doi.org/10.1109/SICE.2006.315603

A. Goshtasby, W. D. Oneill, “Curve fitting by a sum of gaussians”, CVGIP: Graphical Models and Image Processing, Vol. 56, No. 4, pp. 281-288, 1994 DOI: https://doi.org/10.1006/cgip.1994.1025

D. C. Montgomery, Design and analysis of experiments, John Wiley & Sons, 2008

M. M. Ghiasi, A. Bahadori, S. Zendehboudi, A. Jamili, S. R. Gomari, “Novel methods predict equilibrium vapor methanol content during gas hydrate inhibition”, Journal of Natural Gas Science and Engineering, Vol. 15, pp. 69-75, 2013 DOI: https://doi.org/10.1016/j.jngse.2013.09.006

J. Foroozesh, A. Khosravani, A. Mohsenzadeh, A. H. Mesbahi, “Application of artificial intelligence (AI) in kinetic modeling of methane gas hydrate formation”, Journal of the Taiwan Institute of Chemical Engineers, Vol. 45, No. 5, pp. 2258-2264, 2014 DOI: https://doi.org/10.1016/j.jtice.2014.08.001

E. Khamehchi, E. Shamohammadi, H. Yousefi, “Predicting the hydrate formation temperature by a new correlation and neural network”, Gas Processing Journal, Vol. 1, No. 1, pp. 41-50, 2013

M. M. Ghiasi, A. Bahadori, S. Zendehboudi, “Estimation of the water content of natural gas dried by solid calcium chloride dehydrator units”, Fuel, Vol. 117, Part A, pp. 33-42, 2014 DOI: https://doi.org/10.1016/j.fuel.2013.09.086

E. D. Sloan, C. A. Koh, Clathrate Hydrates of Natural Gases, CRC Press, 2007 DOI: https://doi.org/10.1201/9781420008494

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How to Cite

[1]
Abbasi, A. and Mohd Hashim, F. 2019. Evaluating Pressure in Deepwater Gas Pipeline for the Prediction of Natural Gas Hydrate Formation. Engineering, Technology & Applied Science Research. 9, 6 (Dec. 2019), 5033–5036. DOI:https://doi.org/10.48084/etasr.3174.

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