To appropriate design and satisfactory performance of utilities in the gas processing and transmission plants, a crucial factor that should be taken in consideration is the natural gas water content. The present research aimed to develop a precise strategy for estimating sour gas/sweet gas water content ratio. This developed predictive tool is based on adaptive neuro-fuzzy inference system (ANFIS). In this regard, a comprehensive data bank that contains 1,126 data points was collected. This model predicts ratio of sour gas to sweet gas as function of pressure, temperature, and equilibrium H 2 S mole fraction. The ranges of pressure and temperature were 200-70000 KPa and 10-150°C, respectively. In addition, the equilibrium H2S mole fraction ranges between 0.076 and 0.492. Results obtained from the ANFIS model confirmed acceptable and reasonable predictive capability of this model. This tool is simple to use and can be help petroleum engineers to predict water content of natural gas at broad ranges of conditions.
The aim of this contribution was to develop a simple tool based on fuzzy logic concepts to predict true vapor pressure of volatile petroleum products. In this regard, the adaptive neuro fuzzy inference system was evolved to estimate the true vapor pressure of volatile petroleum products as function of temperature and Reid vapor pressure. In addition, to determine optimal membership function parameters, the particle swarm optimization as an amazing evolutionary algorithm was applied. This predictive tool is suggested as a precise technique to measure the true vapor pressures of typical liquefied petroleum gases, natural gasoline, and motor fuel components at broad ranges of temperatures. This technique was trained and tested by 156 set of data points collected from the reference. The temperature range is 253-373 K and the range of Reid vapor pressure is 35-250 KPa. Results obtained from the present tool found to be in acceptable agreement with the actual reported data in the literature. The values of root mean square error and regression coefficient obtained 5.34 and 0.9975, respectively.
2016) Prediction of water removal rate in a natural gas dehydration system using radial basis function neural network, Petroleum Science and Technology, 34:10, 951-960To link to this article: http://dx.
ABSTRACTNatural gas commonly contains water as a contaminant that can condense to water or form gas hydrates, which causes a range of problems during gas production, transportation, and processing. Therefore, the removal of gas moisture is of great importance. A common and popular method for removing water contamination from natural gas is using solid dehydrators. Calcium chloride is a nonregenerative desiccant to dehydrate natural gas. With continual water adsorption, CaCl 2 changes to consecutively higher states of hydration, finally producing a CaCl 2 brine solution. This method does not require heating or moving parts. In addition, it does not react with H 2 S or CO 2 . These features make this method a popular one for drying natural gas. Nevertheless, precise and simple methods are needed to predict the water content of natural gas dried by calcium chloride dehydrator units. In this study, an intelligent method, called the radial basis function neural network, was incorporated to predict the gas moisture dehydrated by calcium chloride in dehydration units. Modeling was performed under different conditions of a fresh recharge and before recharging. The overall correlation factor of 0.9999 for both the fresh charge and before charging conditions showed that the outputs of the proposed models were in agreement with the experimental data. In addition, the developed models were compared with the previously proposed intelligent models and classic correlations. The comparison showed that the developed model is superior to the previously proposed models and correlations regarding the accuracy of prediction.
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