Predictive models for density correction factor of natural gas and comparison with standard methods

Abstract

Two intelligent-based models which do not require complete gas compositions are presented to estimate natural gas density correction factor using comprehensive datasets (nearly 60 000 instances) originating from the AGA8-DCM (Detail Characterization Method) standard: (1) NGDC-ANN model (Natural Gas Density Calculator based on Artificial Neural Network) and (2) AGA8-GCMD model (Gross Characterization Method Developed by applying genetic algorithm technique). In the suggested models, only five input variables (specific gravity at base condition, operating temperature and pressure and molar composition of CO2 and N2) are employed. The experimental datasets obtained from this work (68 instances) and literature (505 instances) are applied to validate the developed model showing a very good agreement between experimental and estimated data. Simplicity, improving accuracy and satisfactory results of the suggested models over a wide range of operational conditions show that these models would be excellent alternatives for the traditional standard methods, so that, the NGDC-ANN model prediction besides of its simplicity to use show the highest accuracy over a wide of operational range in comparison to similar models.