2017_EJRNL_PP_AMIR_HOSSEIN_SAEEDI_DEHAGHANI_1.pdf
Terbatas  Suharsiyah
» Gedung UPT Perpustakaan
Terbatas  Suharsiyah
» Gedung UPT Perpustakaan
Density is an important property of natural gas required for the design of gas processing and
reservoir simulation. Due to expensive measurement of density, industry tends to predict gas
density through an EOS. However, all EOS are associated with uncertainties, especially at highpressure conditions. Also, using sophisticated EOS in commercial software renders simulation
highly time-consuming. This work aims to evaluate performance of adaptive neuro-fuzzy inference
system (ANFIS) as a widely-accepted intelligent model for prediction of P-r-T behavior of natural
gas. Using experimental data reported in the literature, our inference system was trained with 95
data of natural gas densities in the temperature range of (250e450)K and pressures up to 150 MPa.
Additionally, prediction by ANFIS was compared with those of AGA8 and GERG04 which both are
leading industrial EOS for calculation of natural gas density. It was observed that ANFIS predicts
natural gas density with AARD% of 1.704; and is able to estimate gas density as accurate as sophisticated EOS. The proposed model is applicable for predicting gas density in the range of (250
e450) K, (10e150) MPa and also for sweet gases, i.e., containing a low concentration of N2 and CO2.