Thermodynamic modeling and uncertainty quantification of CO2-loaded aqueous MEA solutions

Morgan, Josh; Chinen, Anderson Soares; Omell, Benjamin; Bhattacharyya, Debangsu; Tong, Charles; Miller, David C.

doi:10.1016/j.ces.2017.04.049

Cited by 38 publications

(59 citation statements)

References 36 publications

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“…Corresponding rate expressions are given by Equations 2.35 and 2.36. As discussed in the work of Morgan et. al.…”

Section: Reaction Kineticsmentioning

confidence: 94%

“…Uncertainty quantification of density, surface tension and viscosity models using a Bayesian approach has been reported by Morgan et al (2015). More recently, Morgan et al have presented a Bayesian inference procedure for UQ of the thermodynamic model of a MEA-H2O-CO2 system where the VLE, enthalpy and chemistry models were considered together (Morgan et al, 2017). One significant difference in the UQ of the mass transfer and hydraulic models in comparison to the UQ of the thermodynamic and transport models is the consideration of the tower model.…”

Section: Uncertainty Quantificationmentioning

confidence: 99%

“…Reaction kinetics of the MEA-H2O-CO2 system has been studied by several authors (Austgen et al 1989;Versteeg et al 1996;Hilliard, 2008;Plaza, 2011). In this work a reduced set of reactions represented by Equations 2.33 and 2.34 is considered (Plaza ,2011;Morgan et. al., 2017).…”

Section: Reaction Kineticsmentioning

confidence: 99%

“…The regenerated MEA is then recycled back to the absorber. Development of the properties models for the benchmark model, uncertainty quantification of the properties models and validation of the steady-state model with the data from National Carbon Capture Center (NCCC), Wilsonville, AL have been described in details in the PhD dissertation of Morgan (2017).…”

Section: Chapter 1 Introductionmentioning

confidence: 99%

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Development of Hydraulic Models, Mass Transfer Models, and Dynamic Models of Solvent-Based Carbon Capture Processes with Uncertainty Quantification and Validation with Pilot Plant Data

Chinen¹

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To accelerate the development and commercial deployment of CO2 capture technologies, computational tools and models are being developed under the auspices of the U.S. Department of Energy's Carbon Capture Simulation Initiative (CCSI). The CCSI process modeling team was tasked with developing a "gold-standard" model that will serve as a definitive reference for benchmarking the performance of solvent-based CO2 capture systems under steady-state and dynamic conditions over a large operating-range. The main three areas that this work focused on are: development of the hydrodynamic and mass transfer submodels for a monoethanolamine (MEA) solvent system, uncertainty quantification of these submodels, development of a dynamic model for this system, and development of a dynamic design of experiment methodology for model validation and parameter estimation of this system. For the gold-standard model, it was desired that the pressure drop and holdup models must be applicable over a wide range of operating conditions. In this work, a large range of liquid and gas flowrates, and wide range of viscosity and density for the liquid phase are considered and an optimal model is developed. The pressure drop and holdup models are also evaluated with data from numerous process scales. Seeking to accomplish a Ph.D. in chemical engineering at West Virginia University has showed me much more than the scientific contributions in this document. I had an unexpected growth in perspective and perception through all the experiences I had since I joined the program in 2013, and God's grace has been generously put in my life in the form of the many I am thankful for. I would like to thank my advisor, Dr. Debangsu Bhattacharyya, for being an example of a great mentor, showing how an engineering researcher should look at (and question) the world. He pushed me to be able to achieve goals that I misjudged as unfeasible, and I am grateful for all the confidence deposited in me. I also would like to thank my Advisory and Examining Committee members (Dr.

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“…Corresponding rate expressions are given by Equations 2.35 and 2.36. As discussed in the work of Morgan et. al.…”

Section: Reaction Kineticsmentioning

confidence: 94%

Section: Uncertainty Quantificationmentioning

confidence: 99%

Section: Reaction Kineticsmentioning

confidence: 99%

Section: Chapter 1 Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development of Hydraulic Models, Mass Transfer Models, and Dynamic Models of Solvent-Based Carbon Capture Processes with Uncertainty Quantification and Validation with Pilot Plant Data

Chinen¹

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“…The contributions of this portion of the work are included in two manuscripts, for standalone property models such as viscosity, density, and molar volume of the MEA-CO 2 -H 2 O system (Morgan et al, 2015) and the thermodynamic framework of this system. (Morgan et al, 2017a). The contributions of these works may be summarized as follows:…”

Section: Scope Of Researchmentioning

confidence: 99%

Physical Property Modeling of Solvent-Based Carbon Capture Processes with Uncertainty Quantification and Validation with Pilot Plant Data

Morgan¹

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in Prof. Bhattacharyya's Advanced Process and Energy Systems Engineering Group, both past and current. I have enjoyed the opportunity to get to know many of you, and to have the opportunity to discuss research work and share ideas with many of you. I maintain fond memories of all of the adventures we have had outside of the work setting, particularly on travels for AIChE conferences. I would especially like to acknowledge the honor of working directly with Anderson Soares Chinen on the development of the MEA solvent model and various other projects; without his contributions, most of the work contained herein would not be possible. Similarly, I would like to thank Ben Omell for his very valuable guidance throughout the development of these process models, and for providing the results for the validation of our model with TCM data. There are many collaborators that I have been privileged to work with, and who have contributed much to the work described in this document. I would like to thank the National Carbon Capture Center (NCCC) and all of the involved staff for providing data for the 2014 and 2017 test campaigns for the MEA solvent system. I would like to thank Charles Tong from Lawrence Livermore National Laboratory for providing much assistance and advice for the uncertainty quantification phase of this work, particularly with respect to use of the PSUADE software, and for his patience while I learned to use these tools. I have also enjoyed the opportunity to collaborate with Sham Bhat and Christine Anderson-Cook from Los Alamos National vi Laboratory on the design of experiments for the 2017 NCCC test campaign, and acknowledge their contributions to the success of this project. I would like to thank Bill Buschle from the University of Edinburgh for his work on the measurement uncertainty in the solvent at NCCC, which has yielded results that have greatly improved the comparison between our MEA model and the pilot plant data. I would like to thank Prof. Gary Rochelle from the University of Texas at Austin for sharing the "Phoenix Model", which was used as a precursor to the model developed in this work, and members of his research team, specifically Brent Sherman, for being very helpful as I was starting out in the solvent modeling work. I would like to thank all involved in the high viscosity solvent model project for the opportunity to be involved with this collaborative effort. I am grateful for the CCSI and CCSI 2 research programs, and for the leadership of David Miller and Michael Matuszewski, that have made these collaborations possible. While pursuing a Ph.D. at West Virginia University, I have been privileged to be able to supplement my research with excellent coursework from within the Department of Chemical and Biomedical Engineering (CBE) and elsewhere. From CBE, I would like to acknowledge the following professors for offering excellent courses that have contributed to my research and professional development in some way: Prof. Charter Stinespring (thermodynamics and statistical and molecular t...

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A generalized cutting‐set approach for nonlinear robust optimization in process systems engineering

et al. 2021

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We propose a novel computational framework for the robust optimization of highly nonlinear, non‐convex models that possess uncertainty in their parameter data. The proposed method is a generalization of the robust cutting‐set algorithm that can handle models containing irremovable equality constraints, as is often the case with models in the process systems engineering domain. Additionally, we accommodate general forms of decision rules to facilitate recourse in second‐stage (control) variables. In particular, we compare and contrast the use of various types of decision rules, including quadratic ones, which we show in certain examples to be able to decrease the overall price of robustness. Our proposed approach is demonstrated on three process flow sheet models, including a relatively complex model for amine‐based CO2 capture. We thus verify that the generalization of the robust cutting‐set algorithm allows for the facile identification of robust feasible designs for process systems of practical relevance.

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Thermodynamic modeling and uncertainty quantification of CO2-loaded aqueous MEA solutions

Cited by 38 publications

References 36 publications

Development of Hydraulic Models, Mass Transfer Models, and Dynamic Models of Solvent-Based Carbon Capture Processes with Uncertainty Quantification and Validation with Pilot Plant Data

Development of Hydraulic Models, Mass Transfer Models, and Dynamic Models of Solvent-Based Carbon Capture Processes with Uncertainty Quantification and Validation with Pilot Plant Data

Physical Property Modeling of Solvent-Based Carbon Capture Processes with Uncertainty Quantification and Validation with Pilot Plant Data

A generalized cutting‐set approach for nonlinear robust optimization in process systems engineering

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