ISSN print edition: 0366-6352
ISSN electronic edition: 1336-9075
Registr. No.: MK SR 9/7

Published monthly
 

Development of supervised machine learning model for prediction of TEG regeneration performance in natural gas dehydration

Amin Hedayati Moghaddam and Abdellatif Mohammad Sadeq

Department of Chemical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

 

E-mail: ami.hedayati_moghaddam@iauctb.ac.ir

Received: 26 July 2023  Accepted: 25 September 2023

Abstract:

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.

Keywords: Natural Gas; Dehydration; TEG regeneration; Machine learning; Genetic algorithm

Full paper is available at www.springerlink.com.

DOI: 10.1007/s11696-023-03113-7

 

Chemical Papers 78 (1) 587–597 (2024)

Sunday, November 24, 2024

IMPACT FACTOR 2023
2.1
SCImago Journal Rank 2023
0.381
SEARCH
Advanced
VOLUMES
© 2024 Chemical Papers