(436c) Optimization of TEG Dehydration Process in Natural Gas Processing Under Metamodel Uncertainty | AIChE

(436c) Optimization of TEG Dehydration Process in Natural Gas Processing Under Metamodel Uncertainty


Diwekar, U. - Presenter, Vishwamitra Research Institute /stochastic Rese
Optimization of TEG Dehydration Process in Natural Gas Processing under Metamodel Uncertainty

Rajib Mukherjee1,2, Urmila M Diwekar2*

1Department of Chemical Engineering, The University of Texas Permian Basin, Odessa, TX 79762, USA

2Vishwamitra Research Institute, Crystal Lake, Illinois, 60012, USA


Natural gas processing involves removal of acidic gases followed by dehydration that is mainly carried out through absorption in tri-ethylene glycol (TEG). The dehydration process is accompanied by the emission of volatile organic compounds, including BTEX. There are several process parameters associated with major equipment that may impact BTEX emission. In our previous work, a multi-objective optimization is performed for optimal operating conditions identification related to the process parameters to mitigate BTEX emission using data driven metamodeling using machine learning followed by optimization with metaheuristic technique [1]. The metamodel is generated with data obtained from process simulation carried out in ProMax® process simulator. The metamodels are created using limited samples which introduces metamodeling uncertainty [2]. Previous metamodel-based robust designs treated a metamodel as the real model and ignore the influence of metamodeling uncertainty. Thus, the performance of the resulting optimized process parameters may have shortcomings due to the lack of adequately accounting for the uncertainty introduced by the metamodel. In the present work, the bias of metamodel uncertainty has been addressed for parameter optimization. An algorithmic framework is developed to address the challenge of parameter optimization under metamodel uncertainties. We used the better optimization of nonlinear uncertain systems (BONUS) algorithm to solve the problem. BTEX mitigation is used as the objective of the optimization [3]. Our algorithm allows the determination of the optimal process parameters for BTEX emission mitigation from TEG dehydration process under metamodel uncertainty.

Keywords: Natural gas processing, TEG dehydration, BTEX mitigation, metamodeling uncertainty, support vector regression (SVR), BONUS algorithm

[1] Mukherjee, R., & Diwekar, U. M. (2021). Multi-objective Optimization of the TEG Dehydration Process for BTEX Emission Mitigation Using Machine-Learning and Metaheuristic Algorithms. ACS Sustainable Chemistry & Engineering, 9(3), 1213-1228.

[2] Zhang, S., Zhu, P., Chen, W., & Arendt, P. (2013). Concurrent treatment of parametric uncertainty and metamodeling uncertainty in robust design. Structural and multidisciplinary optimization, 47(1), 63-76.

[3] Sahin KH, Diwekar U, M. (2004) Better Optimization of Nonlinear Uncertain Systems (BONUS): a new algorithm for stochastic programming using reweighting through Kernel density estimation. Ann Oper Res 132:47–68