A computational intelligence scheme for prediction equilibrium water dew point of natural gas in TEG dehydration systems

Raw natural gases are frequently saturated with water during production operations. It is crucial to remove water from natural gas using dehydration process in order to eliminate safety concerns as well as for economic reasons. Triethylene glycol (TEG) dehydration units are the most common type of natural gas dehydration. Making an assessment of a TEG system takes in first ascertaining the minimum TEG concentration needed to fulfill the water content and dew point specifications of the pipeline system. A flexible and reliable method in modeling such a process is of the essence from gas engineering view point and the current contribution is an attempt in this respect. Artificial neural networks (ANNs) trained with particle swarm optimization (PSO) and back-propagation algorithm (BP) were employed to estimate the equilibrium water dew point of a natural gas stream with a TEG solution at different TEG concentrations and temperatures. PSO and BP were used to optimize the weights and biases of networks. The models were made based upon literature database covering VLE data for TEG–water system for contactor temperatures between 10 °C and 80 °C and TEG concentrations ranging from 90.00 to 99.999 wt%. Results showed PSO-ANN accomplishes more reliable outputs compared with BP-ANN in terms of statistical criteria.