View More View Less
  • 1 University of Miskolc, Pf. 2., H-3515 Miskolc-Egyetemváros, Hungary
  • 2 University of Miskolc, Pf. 2., H-3515 Miskolc-Egyetemváros, Hungary
Restricted access

Purchase article

USD  $25.00

Purchase this article

USD  $387.00

During the production of gas one of the major problems is the formation of hydrate crystals in the pipeline. The growing hydrate crystals can form hydrate plugs in the pipeline. The hydrate plug effect lengthens production outages and results in the loss of money of the maintainer, because the removal of the plug is a time consuming procedure. One of the solutions used to prevent hydrate formation is the addition of modern compositions to the gas flow. The modern compositions help to dehydrate the gas, thus, the size of hydrate crystal does not increase. The substances, used in low concentrations, have to be locally injected at the gas well sites. Thus, an injector unit is required for this purpose. The production-related aspect that the consumers expect much more flexibility from gas provider cannot be neglected because of the habits of the users and the appearance of energy-saving technologies are different. The first part of the article a newly developed injection system is introduced. To achieve optimal dosage, not only the hardware of injection system is important, but also the software. In addition to the traditional control, a preventive inhibitor dosing system can be developed, based on model driven system. The nature of the model highly influences the quality of control system. In the second part of the article a machine learning based predictive detection system is introduced

  • [1]

    Bölkény I. Measurement and analysis of hydrate formation, Proc. of 16th International Carpathian Control Conference (ICCC), Szilvásvárad, Hungary, 27–30 May, 2015, pp. 5457.

    • Search Google Scholar
    • Export Citation
  • [2]

    Gao S. Q. Hydrate risk management at high water-cuts with anti-agglomerates hydrate inhibitors, Energy Fuels, Vol. 23, No. 4, 2009, pp. 21182121.

    • Search Google Scholar
    • Export Citation
  • [3]

    Berecz E. ; Balla-Achs M. Gas hydrates, (in Hungarian) Akadémiai Kiadó, Budapest, 1980.

  • [4]

    Kelland M. A. History of the development of low dosage hydrate inhibitors, Energy Fuels, Vol. 20, No. 3, 2006, pp. 825847.

  • [5]

    Lederhos J. P. , Long J. P., Sum A., Christiansen R. L., Sloan E. D. Jr. Effective kinetic inhibitors for natural gas hydrates, Chemical Engineering Science, Vol. 51, No. 8, 1996, pp. 12211229.

    • Search Google Scholar
    • Export Citation
  • [6]

    Boxall J. , May E. Formation of gas hydrate blockages in under-inhibited conditions, Proceeding of the 7th International Conference on Gas Hydrates (ICGH 2011), Vol. 3, Edinburgh, UK, 17-21 July 2011, pp. 22432248.

    • Search Google Scholar
    • Export Citation
  • [7]

    Tornyi L. Füvesi V. , Vörös Cs., Jónap K., Vágó Á. Analyses and field applications of gas hydrate inhibitors, Journal Nafta Plin, 2015, pp. 111118.

    • Search Google Scholar
    • Export Citation
  • [8]

    Hammerschmidt E. G. Formation of gas hydrates in natural gas transmission lines, Ind. Eng. Chem. Vol. 26, No. 8, 1934, pp. 851585.

  • [9]

    Bölkény I. , Konyha J., Jónap K., Vörös Cs. Hydration inhibiting technologies, results and future opportunities based on the measurements and projects of the last 15 years (in Hungarian), Műszaki Földtudományi Közlemények, Vol. 85, No. 1, 2015, pp. 3040.

    • Search Google Scholar
    • Export Citation
  • [10]

    Vörös Cs. , Füvesi V., Pintér Á.: Design of a new chemical injection pump system, Proc. of Factory Automation Conference, University of Pannon, Veszprém, Hungary, 21-22 May 2013, pp. 124127.

    • Search Google Scholar
    • Export Citation
  • [11]

    Norgaard M. , Ravn O, Hansen L.K., Poulsen N.K. The NNSYSID toolbox -A MATLAB toolbox for system identification with neural network, Proceedings of the 1996 IEEE International Symposium on Computer-Aided Control System Design, Dearborn, MI, 15-18 September 1996, pp. 374379

    • Search Google Scholar
    • Export Citation
  • [12]

    Neelekantan P. , Reddy A. R. M. Decentralized load balancing in distributed systems, Pollack Periodica, Vol. 9, No. 2, 2014, pp. 1528.

    • Search Google Scholar
    • Export Citation
  • [13]

    Bakó L. , Brassai S. T. Embedded neural controllers based on spiking neuron models, Pollack Periodica, Vol. 4, No. 3, 2009, pp. 143154

    • Search Google Scholar
    • Export Citation
  • [14]

    Norgaard M. , Ravn O., Poulsen N. K., Hansen L. K. Neural networks for modeling and control of dynamic systems, Springer-Verlag, London, UK, 2000.

    • Search Google Scholar
    • Export Citation
  • [15]

    Füvesi V. , Kovács E. Separation of faults of eletromechanical drive chain using artificial intelligence methods, 18th BSMBI days Int. Conf, Debrecen, Hungary, 11-12 October 2012, pp. 1927.

    • Search Google Scholar
    • Export Citation
  • [16]

    Canny J. A computational approach to edge detection, IEEE Trans. Pattern Anal. Mach. Intell, Vol. 8, No. 6, 1986, pp. 679698.

  • [17]

    Bölkény I. , Füvesi V. Modeling and detection of gas hydrate appearance, Proceedings of the 17th International Carpathian Control Conference, Tatranska, Lomnica, Szlovákia, 29 May -1 June 2016, pp. 8690.

    • Search Google Scholar
    • Export Citation
  • [18]

    Konyha J. , Bányai T. Approach to accelerate algorithms to solve logistic problems with GPGPU, Advanced Logistic Systems, Vol. 10, No. 2, 2016, pp. 510.

    • Search Google Scholar
    • Export Citation
  • [19]

    Konyha J. , Bányai T. Sensor networks for smart manufacturing processes, Solid State Phenomena, Vol. 261, 2017, pp. 456462.

Monthly Content Usage

Abstract Views Full Text Views PDF Downloads
Sep 2020 31 0 0
Oct 2020 5 0 0
Nov 2020 4 3 4
Dec 2020 2 0 0
Jan 2021 7 0 0
Feb 2021 3 0 0
Mar 2021 0 0 0