Development of novel method for prediction of gas density in operational conditions

The performance of gas industries is extensively function of gas properties such as gas density. Due to this importance in the present work, a novel grid partitioning based fuzzy inference system method applied to predict gas density base on pressure, temperature and molecular weight of gas. To this end, the required experimental data are collected from reliable sources. Different comparison scenarios are used to evaluate the ability of model. The coefficients of determination (R²) for training and testing phases are calculated as 0.9985 and 0.9980 respectively. The determined indexes and graphical evaluations show that predicting model can estimate gas density in high degree of accuracy. According to the obtained results, the predicting model can be used as a simple and powerful software in gas industries to predict different processes.