Innovative computational tools and models for the design, optimization and control of carbon capture processes

Miller, David C.; Agarwal, D.; Bhattacharyya, Debangsu; Boverhof, Joshua; Cheah, You-Wei; Chen, Yang; Eslick, John; Leek, J; Ma, Jinliang; Mahapatra, Priyadarshi; Ng, Brenda; Sahinidis, Nikolaos V.; Tong, Charles; Zitney, Stephen E.

doi:10.1016/b978-0-444-63428-3.50403-3

Cited by 8 publications

(6 citation statements)

References 8 publications

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“…Packing specific parameters ( C P , H L 1 , H L 2 ) for the hydraulic models were regressed using experimental data . Very accurate predictions of both pressure drop and holdup were obtained for the Mellapak 252Y packing as reported in Soares Chinen et al As discussed earlier, column hydraulics strongly affect the transient response; therefore, these models are important for developing a predictive dynamic model. Because it is practically impossible to isolate the mass transfer and kinetic reactions in chemical solvent systems, a novel simultaneous parameter regression approach was developed where the parameters for the reaction kinetics (eqs –), liquid-side and gas-side mass-transfer coefficients (eqs and ), interfacial area (eq ), and diffusivity (eq and ) models were estimated simultaneously using the multiscale data as discussed in Soares Chinen et al This regression was done by leveraging the Framework for Optimization, Quantification of Uncertainty, and Surrogates (FOQUS) developed by the Carbon Capture Simulation Initiative (CCSI). The key advantage of the simultaneous approach over the typical sequential approach is a model with improved prediction capability due to reduction in parametric uncertainty.…”

Section: Introductionmentioning

confidence: 81%

“…Because it is practically impossible to isolate the mass transfer and kinetic reactions in chemical solvent systems, a novel simultaneous parameter regression approach was developed where the parameters for the reaction kinetics (eqs 3−6), liquid-side and gas-side mass-transfer coefficients (eqs 10 and 11), interfacial area (eq 12), and diffusivity (eq 13 and 14) models were estimated simultaneously using the multiscale data as discussed in Soares Chinen et al 36 This regression was done by leveraging the Framework for Optimization, Quantification of Uncertainty, and Surrogates (FOQUS) 42 developed by the Carbon Capture Simulation Initiative (CCSI). The key advantage of the simultaneous approach over the typical sequential approach is a model with improved prediction capability due to reduction in parametric uncertainty.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Data Reconciliation and Validation of a Dynamic Model for Solvent-Based CO₂ Capture Using Pilot-Plant Data

Chinen

Morgan

Omell

et al. 2019

Ind. Eng. Chem. Res.

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This paper is focused on the development and validation of a dynamic model for a monoethanolamine-based CO2 capture unit. Starting with a rigorous steady-state process model, the dynamic model is developed in Aspen Plus Dynamics using a modified Murphree efficiency approach and is validated using dynamic data collected from the National Carbon Capture Center in Wilsonville, Alabama. The dynamic test runs were designed with due consideration of process excitation and process nonlinearities constrained by the available time to conduct the test runs and implement the experimental designs in the plant control system. The experimental data include solvent composition and loading. Because the experimental data were found to violate mass and energy balances, filtering algorithms coupled with dynamic data reconciliation techniques were used. Without adjusting any model parameters, the dynamic model satisfactorily predicted the dynamic response of CO2 capture due to step changes in the solvent flow rate, flue gas flow rate, and steam flow rates. Transient studies show that the process gain and time constants can considerably change depending on the direction of the disturbance or manipulated variables, indicating process nonlinearities. Because of a storage tank between the absorber and stripper, the time constant of the full plant can be considerably longer than the time constants of the absorber or stripper individually. Furthermore, impact of the control system on the process efficiency while following an optimal CO2 capture schedule was also studied.

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Section: Introductionmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Dynamic Data Reconciliation and Validation of a Dynamic Model for Solvent-Based CO₂ Capture Using Pilot-Plant Data

Chinen

Morgan

Omell

et al. 2019

Ind. Eng. Chem. Res.

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“…This Bayesian inference process results in an updated posterior distribution, π(θ̃| Z ), given as a set of sample points. This problem was solved using The Framework for Optimization, Quantification of Uncertainty, and Surrogates (FOQUS) toolset . The main steps of this UQ methodology are highlighted in Figure .…”

Section: Modeling Approach and Methodologymentioning

confidence: 99%

Modeling and Bayesian Uncertainty Quantification of a Membrane-Assisted Chilled Ammonia Process for CO₂ Capture

Hughes

Kotamreddy

Bhattacharyya

et al. 2022

Ind. Eng. Chem. Res.

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Rigorous rate-based models were developed for post-combustion CO 2 capture using chilled aqueous ammonia. Optimal estimation of mass-transfer and kinetic model parameters is undertaken by a simultaneous regression approach using wetted wall column and pilot plant data. In addition to the weighted leastsquares estimate, two robust estimation approaches using Hampel's redescending M-estimator and logistic estimator are used for parameter estimation. A fully Bayesian approach is used for quantifying the uncertainty of the selected parameters. A model of a novel membrane-assisted chilled ammonia process was developed for reducing the energy penalty in the NH 3 abatement section where a reverse osmosis membrane is used to aid in the separation of ammonia from the wash water. The study shows that the membrane can considerably lower the reboiler, cooler, and condenser duties in the NH 3 abatement section, but with higher water removal, there is a steeper rise in the required membrane area. Bayesian inference is observed to result in considerable reduction in prediction uncertainty of key performance variables.

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“…30 ). Still, most prior works limit the surrogate-based optimization to a CCU sub-system (either capture 21,22,31–33 or utilization 15,25 ).…”

Section: Introductionmentioning

confidence: 99%

Accelerating net zero from the perspective of optimizing a carbon capture and utilization system

Hao

Barecka

Lapkin

2022

Energy Environ. Sci.

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Innovative computational tools and models for the design, optimization and control of carbon capture processes

Cited by 8 publications

References 8 publications

Dynamic Data Reconciliation and Validation of a Dynamic Model for Solvent-Based CO₂ Capture Using Pilot-Plant Data

Dynamic Data Reconciliation and Validation of a Dynamic Model for Solvent-Based CO₂ Capture Using Pilot-Plant Data

Modeling and Bayesian Uncertainty Quantification of a Membrane-Assisted Chilled Ammonia Process for CO₂ Capture

Accelerating net zero from the perspective of optimizing a carbon capture and utilization system

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Innovative computational tools and models for the design, optimization and control of carbon capture processes

Cited by 8 publications

References 8 publications

Dynamic Data Reconciliation and Validation of a Dynamic Model for Solvent-Based CO2 Capture Using Pilot-Plant Data

Dynamic Data Reconciliation and Validation of a Dynamic Model for Solvent-Based CO2 Capture Using Pilot-Plant Data

Modeling and Bayesian Uncertainty Quantification of a Membrane-Assisted Chilled Ammonia Process for CO2 Capture

Accelerating net zero from the perspective of optimizing a carbon capture and utilization system

Contact Info

Product

Resources

About

Dynamic Data Reconciliation and Validation of a Dynamic Model for Solvent-Based CO₂ Capture Using Pilot-Plant Data

Dynamic Data Reconciliation and Validation of a Dynamic Model for Solvent-Based CO₂ Capture Using Pilot-Plant Data

Modeling and Bayesian Uncertainty Quantification of a Membrane-Assisted Chilled Ammonia Process for CO₂ Capture