| Abstrakt: |
In this study, triethylene glycol (TEG) regeneration process, which is a critical step in natural gas (NG) dehydration, was investigated. Machine learning (ML) approach was used to develop robust models that could assess the impacts of operative variables on TEG regeneration. A supervised multilayer feed-forward neural network was employed to develop the models, and the k-fold cross-validation technique was used during the training phase. The impacts of TEG flowrate, pressure of distillation column, and temperature of reboiler on energy consumption and TEG purity were investigated. The optimal conditions for TEG regeneration was found using a genetic algorithm (GA) based on the developed models. The R2 values of test dataset were 0.9998 and 0.9989 for TEG purity and reboiler duty, respectively, demonstrating the reliability of optimally tuned models. Overall, this study sheds light on the factors that affect TEG regeneration and provides a useful framework for optimizing the NG dehydration process. [ABSTRACT FROM AUTHOR] |
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