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Optimizing TEG Dehydration Process under Metamodel Uncertainty

Author

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  • Rajib Mukherjee

    (Department of Chemical Engineering, The University of Texas Permian Basin, Odessa, TX 79762, USA
    Vishwamitra Research Institute, Crystal Lake, IL 60012, USA)

  • Urmila M. Diwekar

    (Vishwamitra Research Institute, Crystal Lake, IL 60012, USA)

Abstract

Natural gas processing requires the removal of acidic gases and dehydration using absorption, mainly conducted in tri-ethylene glycol (TEG). The dehydration process is accompanied by the emission of volatile organic compounds, including BTEX. In our previous work, multi-objective optimization was undertaken to determine the optimal operating conditions in terms of the process parameters that can mitigate BTEX emission using data-driven metamodeling and metaheuristic optimization. Data obtained from a process simulation conducted using the ProMax ® process simulator were used to develop a metamodel with machine learning techniques to reduce the computational time of the iterations in a robust process simulation. The metamodels were created using limited samples and some underlying phenomena must therefore be excluded. This introduces the so-called metamodeling uncertainty. Thus, the performance of the resulting optimized process variables may be compromised by the lack of adequately accounting for the uncertainty introduced by the metamodel. In the present work, the bias of the metamodel uncertainty was addressed for parameter optimization. An algorithmic framework was developed for parameter optimization, given these uncertainties. In this framework, metamodel uncertainties are quantified using real model data to generate distribution functions. We then use the novel Better Optimization of Nonlinear Uncertain Systems (BONUS) algorithm to solve the problem. BTEX mitigation is used as the objective of the optimization. Our algorithm allows the determination of the optimal process condition for BTEX emission mitigation from the TEG dehydration process under metamodel uncertainty. The BONUS algorithm determines optimal process conditions compared to those from the metaheuristic method, resulting in BTEX emission mitigation up to 405.25 ton/yr.

Suggested Citation

  • Rajib Mukherjee & Urmila M. Diwekar, 2021. "Optimizing TEG Dehydration Process under Metamodel Uncertainty," Energies, MDPI, vol. 14(19), pages 1-20, September.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6177-:d:644907
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    References listed on IDEAS

    as
    1. Mohamed Ibrahim & Saad Al-Sobhi & Rajib Mukherjee & Ahmed AlNouss, 2019. "Impact of Sampling Technique on the Performance of Surrogate Models Generated with Artificial Neural Network (ANN): A Case Study for a Natural Gas Stabilization Unit," Energies, MDPI, vol. 12(10), pages 1-12, May.
    2. Urmila Diwekar, 2008. "Introduction to Applied Optimization," Springer Optimization and Its Applications, Springer, number 978-0-387-76635-5, June.
    3. Kemal Sahin & Urmila Diwekar, 2004. "Better Optimization of Nonlinear Uncertain Systems (BONUS): A New Algorithm for Stochastic Programming Using Reweighting through Kernel Density Estimation," Annals of Operations Research, Springer, vol. 132(1), pages 47-68, November.
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