Estimation of water content of natural gases using particle swarm optimization method
作者： Mohammad-Ali Ahmadi , Zainal Ahmad , Le Thi Kim Phung , Tomoaki Kashiwao , Alireza Bahadori
DOI: 10.1080/10916466.2016.1153655
关键词: Particle swarm optimization 、 Natural gas 、 Petroleum engineering 、 Sour gas 、 Water content 、 Chemistry
摘要: ABSTRACTA precise estimation of natural gas water content is a significant constraint in appropriate planning production, processing services and transmission. The main contribution this research to develop machine learning approach for predicting sweet sour gases. In regard, joining particle swarm optimization an artificial neural network was utilized. suggested model presents good predictions the with following circumstances, including CO2 contents 0–40 mol%, H2S 0–50 pressures range from atmospheric 70,000 KPa 100,000 gas, temperatures 10–200°C gases 10–150°C
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