Multi-objective Optimization of the TEG Dehydration Process for BTEX Emission Mitigation Using Machine-Learning and Metaheuristic Algorithms

Mukherjee, Rabindra Nath; Diwekar, Urmila M.

doi:10.1021/acssuschemeng.0c06951

Cited by 18 publications

(13 citation statements)

References 43 publications

(78 reference statements)

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“…Process conditions are from the ProMax ® reference example model Ex05-TEG Dehydration [32]. Details of the process condition can be found in Mukherjee and Diwekar, 2021 [1]. In the present work, sampling from a uniform distribution of the decision variables' range is performed, and the resulting impact on emission and drying is observed.…”

Section: Acid Gas Removal Unit Process Simulationmentioning

confidence: 99%

“…The other process variables, including stripping gas flow rate, reboiler temperature, absorber pressure, flash gas pressure, and lean solvent temperature, may also impact BTEX/VOC emission. In this work, as identified by Mukherjee and Diwekar (2021), the process variables that have a significant impact on BTEX emission are used [1].…”

Section: Data Generation and Process Optimizationmentioning

confidence: 99%

“…Thus, we can ascertain the process performance at corresponding process variable set points considering uncertainty. Details of the metamodel generation using SVR are described in Mukherjee and Diwekar, 2021 [1].…”

Section: Uncertainty Quantificationmentioning

confidence: 99%

“…The total BTEX and other VOC emissions from the dehydration process originate from the flash tank and regeneration unit. In our previous work, metamodels were developed from the simulated process data and metaheuristic optimization to optimize different process variables for BTEX emission reduction and maintain the dry gas specification [1]. The uncertainty involved in the optimization process is either under metamodeling uncertainties (MOOMU).…”

Section: Introductionmentioning

confidence: 99%

“…In cases in which many variables are present, important variable selection is required; variables can be selected using multivariate statistics or machine learning [21][22][23]. In the present problem, important variables selected through lasso, as found in Mukherjee and Diwekar (2021), were used [1]. Surrogate models are generated to quantify the effects of the important variables on the corresponding dependent variables of the process.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing TEG Dehydration Process under Metamodel Uncertainty

Mukherjee¹,

Diwekar²

2021

Energies

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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.

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Section: Acid Gas Removal Unit Process Simulationmentioning

confidence: 99%

Section: Data Generation and Process Optimizationmentioning

confidence: 99%

Section: Uncertainty Quantificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimizing TEG Dehydration Process under Metamodel Uncertainty

Mukherjee¹,

Diwekar²

2021

Energies

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Multi-objective Optimisation Using Fuzzy and Weighted Sum Approach for Natural Gas Dehydration with Consideration of Regional Climate

Kong

How

Mahmoud

et al. 2022

Process Integr Optim Sustain

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The majority of the existing simulation-based research works on natural gas dehydration via absorption using tri-ethylene glycol (TEG) have focused on solving single or bi-objective problems where most of the objectives are in conflict with one another. It was not until 2017 that multi-objective problems with conflicting nature have started gaining significant interest in this field, especially those involving 3 or more objectives. In this work, a multi-objective optimisation (MOO) framework was developed involving two different techniques, i.e. the fuzzy optimisation and the weighted sum approach, for handling different conflicting objectives in a natural gas dehydration process. The developed framework is straightforward, which can be applied by anyone effortlessly and can be easily extended to data from other literatures. Two different case studies, which involved bi- and tri-objectives, are given here to illustrate the efficacy of the developed framework for improving the sustainability and performance of the natural gas dehydration process. Relative to previous works without optimisation, the optimum results obtained here provide a compromised solution between different objectives. Using fuzzy optimisation in case 1, for example, increases the net profit by 0.2% and reduces the VOC emissions by 33% (i.e. better sustainability). Although the water dew point increases by 15%, it is still within the specification range and hydrate formation will not occur.

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