139
Views
3
CrossRef citations to date
0
Altmetric
Research Article

Application of Metaheuristic Algorithms for Pressure Analysis of Crude Oil Pipeline

, ORCID Icon, &
Pages 5124-5142 | Received 22 Mar 2019, Accepted 11 Jul 2019, Published online: 09 Sep 2019

References

  • Abubakar, A., A. R. Al-Hashmi, T. Al-Wahaibi, Y. Al-Wahaibi, A. Al-Ajmi, and M. Eshrati. 2014. Parameters of drag reducing polymers and drag reduction performance in single-phase water flow. Advances in Mechanical Engineering 6:202073. doi:10.1155/2014/202073.
  • Abubakar, A., T. Al-Wahaibi, A. R. Al-Hashmi, Y. Al-Wahaibi, A. Al-Ajmi, and M. Eshrati. 2015. Influence of drag-reducing polymer on flow patterns, drag reduction and slip velocity ratio of oil–water flow in horizontal pipe. International Journal of Multiphase Flow 73:1–10. doi:10.1016/j.ijmultiphaseflow.2015.02.016.
  • Anderson, J. A. 1995. An introduction to neural networks. Cambridge, MA: MIT press.
  • Asidin, M. A., E. Suali, T. Jusnukin, and F. A. Lahin. 2019. Review on the applications and developments of drag reducing polymer in turbulent pipe flow. Chinese Journal of Chemical Engineering.
  • Ba Geri, M., A. Imqam, and R. Flori. (2019, April). A critical review of using high viscosity friction reducers as fracturing fluids for hydraulic fracturing applications. In SPE Oklahoma city oil and gas symposium, Society of Petroleum Engineers.
  • Brostow, W. 2008. Drag reduction in flow: review of applications, mechanism and prediction. Journal of Industrial and Engineering Chemistry 14 (4):409–16. doi:10.1016/j.jiec.2008.07.001.
  • Clerc, M., and J. Kennedy. 2002. The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6 (1):58–73. doi:10.1109/4235.985692.
  • Cruz, D. O. A., and F. T. Pinho. 2003. Turbulent pipe flow predictions with a low Reynolds number k–ε model for drag reducing fluids. Journal of non-Newtonian Fluid Mechanics 114 (2):109–48. doi:10.1016/S0377-0257(03)00119-8.
  • Cybenko, G. 1989. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems 2 (4):303–14. doi:10.1007/BF02551274.
  • Dumas, L., V. Herbert, and F. Muyl. 2005. Comparison of global optimization methods for drag reduction in the automotive industry. In Computational science and its applications – ICCSA 2005, ed. O. Gervasi, M. L. Gavrilova, V. Kumar, A. Laganá, H. P. Lee, Y. Mun, and C. J. K. Tan, 948–57. Berlin, Heidelberg: Springer Berlin Heidelberg.
  • Galeano, H., and P.-C. Narváez. 2003. Genetic algorithms for the optimization of pipeline systems for liquid transportation (1). CT&F - Ciencia, Tecnología y Futuro 2:55–64. Retrieved from http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0122-53832003000100006&nrm=iso.
  • Gallego, F., and S. N. Shah. 2009. Friction pressure correlations for turbulent flow of drag reducing polymer solutions in straight and coiled tubing. Journal of Petroleum Science and Engineering 65 (3):147–61. doi:10.1016/j.petrol.2008.12.013.
  • Halali, M. A., V. Azari, M. Arabloo, A. H. Mohammadi, and A. Bahadori. 2016. Application of a radial basis function neural network to estimate pressure gradient in water–oil pipelines. Journal of the Taiwan Institute of Chemical Engineers 58:189–202. doi:10.1016/j.jtice.2015.06.042.
  • Hecht-Nielsen, R (Eds.). 1992. Theory of the backpropagation neural network. In Neural networks for perception, 65–93. Elsevier.
  • Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural Networks 2 (5):359–66. doi:10.1016/0893-6080(89)90020-8.
  • Hydrology, A. T. C. on A. of A. N. N. in H. 2000. Artificial neural networks in hydrology. II: Hydrologic applications. Journal of Hydrologic Engineering 5 (2):124–37. doi:10.1061/(ASCE)1084-0699(2000)5:2(124).
  • Joseph, D. D., A. Narain, and O. Riccius. 2006. Shear-wave speeds and elastic moduli for different liquids. Part 1. Theory. Journal of Fluid Mechanics 171:289–308. doi:10.1017/S0022112086001453.
  • Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes university, engineering faculty, computer engineering department.
  • Karami, H. R., M. Keyhani, and D. Mowla. 2016. Experimental analysis of drag reduction in the pipelines with response surface methodology. Journal of Petroleum Science and Engineering 138:104–12. doi:10.1016/j.petrol.2015.11.041.
  • Karami, H. R., and D. Mowla. 2012. Investigation of the effects of various parameters on pressure drop reduction in crude oil pipelines by drag reducing agents. Journal of non-Newtonian Fluid Mechanics 177:37–45. doi:10.1016/j.jnnfm.2012.04.001.
  • Karami, H. R., and D. Mowla. 2013. A general model for predicting drag reduction in crude oil pipelines. Journal of Petroleum Science and Engineering 111:78–86. doi:10.1016/j.petrol.2013.08.041.
  • Kennedy, J., and R. Eberhart (1995, November). Particle swarm optimization (PSO). In Proc. IEEE International Conference on Neural Networks, Perth, Australia (pp. 1942–1948.
  • Kennedy, J. 2010. Particle swarm optimization. Encyclopedia of Machine Learning, Springer, 10,760–66.
  • Lee, C., J. Kim, D. Babcock, and R. Goodman. 1997. Application of neural networks to turbulence control for drag reduction. Physics of Fluids 9 (6):1740–47. doi:10.1063/1.869290.
  • Lorang, L. V., B. Podvin, and P. Le Quéré. 2008. Application of compact neural network for drag reduction in a turbulent channel flow at low Reynolds numbers. Physics of Fluids 20 (4):45104. doi:10.1063/1.2904993.
  • Mahmood, W. K., W. A. Khadum, E. Eman, and H. A. Abdulbari. 2019. Biopolymer–surfactant complexes as flow enhancers: Characterization and performance evaluation. Applied Rheology 29 (1):12–20. doi:10.1515/arh-2019-0002.
  • Masoudian, M., K. Kim, F. T. Pinho, and R. Sureshkumar. 2013. A viscoelastic k-ε-v2¯-f turbulent flow model valid up to the maximum drag reduction limit. Journal of non-Newtonian Fluid Mechanics 202:99–111. doi:10.1016/j.jnnfm.2013.09.007.
  • McCulloch, W. S., and W. Pitts. 1943. A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics 5 (4):115–33. doi:10.1007/BF02478259.
  • Moayedi, H., M. Mehrabi, M. Mosallanezhad, A. S. A. Rashid, and B. Pradhan. 2018. Modification of landslide susceptibility mapping using optimized PSO-ANN technique. Engineering with Computers 35(3):967–984.
  • Moré, J. J. 1978. The Levenberg-Marquardt algorithm: implementation and theory. In Watson G.A. (eds) Numerical analysis Lecture Notes in Mathematics, 630. Springer, Berlin, Heidelberg.
  • Mosallanezhad, M., and H. Moayedi. 2017. Developing hybrid artificial neural network model for predicting uplift resistance of screw piles. Arabian Journal of Geosciences 10 (22):479. doi:10.1007/s12517-017-3285-5.
  • Pereira, A. S., G. Mompean, L. Thais, and R. L. Thompson. 2017. Statistics and tensor analysis of polymer coil–stretch mechanism in turbulent drag reducing channel flow. Journal of Fluid Mechanics 824:135–73. doi:10.1017/jfm.2017.332.
  • Poli, R., J. Kennedy, and T. Blackwell. 2007. Particle swarm optimization. Swarm Intelligence 1 (1):33–57. doi:10.1007/s11721-007-0002-0.
  • Pouquet, A. 2019. Review of the monograph by pierre sagaut and claude cambon entitled homogeneous turbulence dynamics. Journal of Turbulence, 20:3, 240–244, doi: 10.1080/14685248.2019.1579993.
  • Rasti, E., F. Talebi, and K. Mazaheri. 2019. Improvement of drag reduction prediction in viscoelastic pipe flows using proper low-Reynolds k-ε turbulence models. Physica A: Statistical Mechanics and Its Applications 516:412–22. doi:10.1016/j.physa.2018.10.009.
  • Roy, A., D. Dutta, and K. Choudhury. 2013. Training artificial neural network using particle swarm optimization algorithm. International Journal of Advanced Research in Computer Science and Software Engineering 3:3.
  • Sarangi, P. P., A. Sahu, and M. Panda. 2014. Training a feed-forward neural network using artificial bee colony with back-propagation algorithm. In Mohapatra D., Patnaik S. (eds) Intelligent computing, networking, and informatics, Advances in Intelligent Systems and Computing 243:511–19. New Delhi: Springer.
  • Seyedashraf, O., M. Mehrabi, and A. A. Akhtari. 2018. Novel approach for dam break flow modeling using computational intelligence. Journal of Hydrology 559:1028–38. doi:10.1016/j.jhydrol.2018.03.001.
  • Shams, R., and S. Shad. 2019. Experimental study of two-phase oil–polymer flow in horizontal flow path. Experimental Thermal and Fluid Science 100:62–75. doi:10.1016/j.expthermflusci.2018.08.028.
  • Wang, Y., C. Wu, G. Tan, and Y. Deng. 2017. Reduction in the aerodynamic drag around a generic vehicle by using a non-smooth surface. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 231 (1):130–44.
  • Yari, E., A. Ayoobi, and H. Ghassemi. 2014. Applying the artificial neural network to estimate the drag force for an autonomous underwater vehicle. Open Journal of Fluid Dynamics 04 (03):334–346. doi:10.4236/ojfd.2014.43025.
  • Yousif, Z. 2018. Drag reduction study of xathan gum with polydiallyldimethylammonium chloride (PDDAC) solutions in turbulent flow. Engineering and Technology Journal 36 (8Part (A) Engineering):891–99.
  • ZHAO, D., Y. WANG, P. ZHOU, and Q. LI. 2015. Optimization of drag-reduction by suction using multi-island genetic algorithm. Journal of Beijing University of Aeronautics and Astronautics 41 (5):941–946.
  • Zhong, K., C. Yan, S. Chen, T. Zhang, and S. Lou. 2019. Aerodisk effects on drag reduction for hypersonic blunt body with an ellipsoid nose. Aerospace Science and Technology 86:599–612. doi:10.1016/j.ast.2019.01.027.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.