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.
Industrial natural gas treating plants commonly employ amine-based treatments for hydrogen sulfide elimination from crude oil and gas. Some deficiencies boost the motivation to find an appropriate alternative. Due to their advantageous properties, liquid electrolytes are considered as possible substitutes for classical alkanolamine solvents in such processes. The solubility of gases in ionic solutions at different temperatures and pressures is a crucial factor in the examination of ionic liquids as a potential alternative. Two intelligent methods, namely, simple multilayer perceptron (MLP) and radial basis function neural networks, are proposed to accurately predict the solubility of H 2 S in various ionic liquids. The predicted values agree well with the experimental data. A comparison to other intelligent models, which were recently suggested, reveals the superiority of the proposed simple MLP model.
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