A Method for the Compressibility Factor Calculation of Natural Gas-Hydrogen Blends, Using a Regression Equation and an Artificial Neural Network Algorithm
DOI:
https://doi.org/10.31649/1997-9266-2024-174-3-6-13Keywords:
hydrogen-natural gas blend, natural gas, compressibility factor, regression equation, artificial neural networkAbstract
To calculate the operating modes of gas pipelines and for the commercial metering of consumed or transported gas, it is necessary to take into account its compressibility factor, which is determined using equations of state or correlation dependencies. Equations of states are characterized by high calculation accuracy but require a significant amount of various data for calculations, so they are used for commercial gas metering. Correlation equations are usually used to calculate network parameters, which are less accurate but much easier to calculate. Gas transmission network operators use correlation equations for determining the compressibility factor based on carbon dioxide content or relative density, as given in SOU 60.3-0019801-100:2012.
When these equations are applied to calculate the compressibility factor of natural gas-hydrogen blends with volumetric hydrogen content up to 20%, an increase in error is observed with increasing hydrogen concentration. The equation based on the relative density is more accurate when calculating the natural gas-hydrogen mixture’s compressibility factor since the hydrogen content directly affects the density of the blend. The carbon dioxide equation is generally insensitive to changes in hydrogen concentration within the blend. Thus, it is worth adding a hydrogen variable to the equation to reduce calculation errors.
In this article, the selection of the hydrogen variable coefficient is carried out by the classical regression method, in which the equations are modified by supplementing the existing equations for calculating the compressibility factor with the addition of the product of the hydrogen content in the mixture by the calculated coefficient, and the equation's bias adjustment.
An alternative way to calculate the compressibility factor is to use artificial neural networks (ANNs). In the course of this work, a two-layer artificial neural network with back-propagation of error was developed. This ANN receives as input a set of values of carbon dioxide and hydrogen content, temperature, and pressure, and outputs the value of the compressibility factor.
References
M. Farzaneh-Gord, et al., “Accurate determination of natural gas compressibility factor by measuring temperature, pressure and Joule-Thomson coefficient: Artificial neural network approach,” Journal of Petroleum Science and Engineering, vol. 202, 2021.
K. E. Starling, and J. L. Savidge, “Compressibility Factors of Natural Gas and Other Related Hydrocarbon Gases,” American Gas Association, Transmission Measurement Committee Report, no. 8, and American Petroleum Institute, MPMS Chapter 14.2, 2nd ed., 1994.
International Organization for Standardization, “Natural Gas – Calculation of Compression Factor, Part 2: Calculation using molar-composition analysis,” ISO 12213-2, 2006.
International Organization for Standardization, “Natural Gas – Calculation of Compression Factor, Part 3: Calculation using physical properties,” ISO 12213-3, 2006.
R. H. Zimmerman, “Manual for the Determination of Supercompressibility Factors for Natural Gas,” PAR Project NX-19, Ohio State University, 1962.
G. Muller-Syring, et al., “Management summary. Entwicklung von modularen Konzepten zur Erzeugung, Speicherung und Einspeisung von Wasserstoff und Methan ins Erdgasnetz,” 2013.
M. Dell’Isola, et al., “Impact of hydrogen injection on thermophysical properties and measurement reliability in natural gas networks,” E3S Web Conf., vol. 312, 2021.
M. Łach, “Dokładność wyznaczania współczynnika ściśliwości gazu z podwyższoną zawartością wodoru – porównanie metod obliczeniowych,” NG, vol. 72, no. 5, pp. 329-338, 2016.
Н.-А. Сорока і М. Карпаш, «Дослідження придатності кореляційних методів визначення коефіцієнта стиснення газоводневих сумішей,» Вимірювальна та обчислювальна техніка в технологічних процесах, вип. 4, с. 111-120, 2023.
СОУ 60.3-30019801-100:2012, «Газ природний горючий. Визначення обсягів витрат природного газу на виробничо-технологічні потреби під час його транспортування газотранспортною системою та експлуатації підземних сховищ газу. Порядок розрахунку,» 2012.
Паспорт ФХП природного газу [Електронний ресурс]. Режим доступу: https://gaz.kherson.ua/?cat=30 .
Оперативні дані оператора ГТС (ГДП, ВБГ) про чисельні значення ФХП природного газу в точках його надходження до ГРМ [Електронний ресурс]. Режим доступу: https://www.chergas.ck.ua/spozhivacham/yakist-gazu-vid .
АТ «Укртрансгаз», «Якість газу у грудні 2019 року по регіонах України,» 2020. [Електронний ресурс]. Режим доступу: http://utg.ua/utg/media/news/2020/01/gq-2019-12.html .
Downloads
-
PDF (Українська)
Downloads: 92
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
