Estimation of triethylene glycol (TEG) purity in natural gas dehydration units using fuzzy neural network

• An artificial neural network is used to estimate glycol purity in natural gas dehydration units.
• Evaluation of pressure reduction in reboiler on the performance of gas dehydration plant.
• The model has been developed and tested using 200 series of the data.
• Statistical and graphical error analyses are presented to show accuracy of the model.

Natural gas usually contains a large amount of water and is fully saturated during production operations. In natural gas dehydration units' water vapor is removed from natural gas streams to meet sales specifications or other downstream gas processing requirements. Many methods and principles have been developed in the natural gas dehydration process for gaining high level of triethylene glycol (TEG) purity. Among them, reducing the pressure in the reboiler at a constant temperature results in higher glycol purity. The main objective of this communication is the development of an intelligent model based on the well-proven standard feed-forward back-propagation neural network for accurate prediction of TEG purity based on operating conditions of reboiler. Capability of the presented neural-based model in estimating the TEG purity is evaluated by employing several statistical parameters. It was found that the proposed smart technique reproduces the reported data in the literature with average absolute deviation percent being around 0.30%.