A Novel Efficient Soft Computing Model for Natural Gas Compressibility Factor based on GMDH neural network

Traditional gas compressibility factor estimation methods such as AGA8-92DC and SGERG-88 usually use overly complex theoretical derivation and corresponding estimation model. This will cost most of the operating memory of the low-power gas flowmeter. Therefore, the previous models are not suitable for application on the flowmeter using the low-power embedded chips. To solve this problem, this paper proposed a novel efficient soft computing model for natural gas compressibility factor based on Group Method of Data Handling(GMDH) neural network. First, the signal of working conditions such as temperature, pressure and gas mole fraction of components are used to calculate pseudo-critical pressure and pseudo-critical temperature. Second, the soft computing model based on GMDH neural network with Corrected Akaike's Information Criterion (AICc) is utilized by using pseudo-critical pressure and pseudo-critical temperature as training sets. For the four common natural gas types, the estimated results show that the mean absolute percentage error is only 0.0168% and the computing time is effectively reduced. It also proved that the GMDH neural network can significantly reduce the computing time and improve the accuracy of the compressibility factor. Feasibility and effectiveness of this model was verified. Our work provides a very useful way and also make it possible to real-timely estimate the natural gas compressibility factor in low-power flowmeter under the premise of satisfying the accuracy.