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.
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