Annual Report: Carbon Capture Simulation Initiative (CCSI) (30 September 2013)

Miller, David C.; Syamlal, Madhava; Cottrell, Roger; Kress, Joel D.; Sundaresan, Sankaran; Sun, Xin; Storlie, Curtis B.; Bhattacharyya, D.; Tong, Charles; Zitney, Stephen E.; Dale, Crystal Buchanan; Engel, Dave; Agarwal, D.; Calafiura, P.; Shinn, John H.

doi:10.2172/1122936

Cited by 3 publications

(4 citation statements)

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“…These compression energies match well with that reported by the Carbon Capture Simulation Initiative Toolset implemented in Aspen Plus for the conditioning of ambient pressure CO 2 to 15.2 MPa (370–441 kJ per kg-CO 2 ). 38 We find our approach for estimating recompression energy to be reasonable as we were able to derive similar compression energies of 392 to 420 kJ per kg-CO 2 assuming 4 and 3 stage compression, respectively.…”

Section: Methodsmentioning

confidence: 58%

Emerging concepts in intermediate carbon dioxide emplacement to support carbon dioxide removal

Breunig

Rosner

Lim

et al. 2023

Energy Environ. Sci.

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Section: Methodsmentioning

confidence: 58%

Emerging concepts in intermediate carbon dioxide emplacement to support carbon dioxide removal

Breunig

Rosner

Lim

et al. 2023

Energy Environ. Sci.

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“…Gas-solid heat transfer is important in many emerging technologies such as carbon-neutral energy generation using biomass [2], chemical looping combustion [3], and CO 2 capture [4][5][6]. An improved understanding of gas-solid heat transfer is crucial for process and component design in the development of these technologies.…”

Section: Introductionmentioning

confidence: 99%

Modeling average gas–solid heat transfer using particle-resolved direct numerical simulation

Sun

Tenneti

Subramaniam

2015

International Journal of Heat and Mass Transfer

103

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Particle-resolved direct numerical simulation Average fluid temperature equation Two-fluid model Fixed assembly of particles Nusselt number correlation for gas-solid flow a b s t r a c tThe purpose of this work is to develop gas-solid heat transfer models using Particle-resolved Direct Numerical Simulations (PR-DNS). Gas-solid heat transfer in steady flow through a homogeneous fixed assembly of monodisperse spherical particles is simulated using the Particle-resolved Uncontaminated-fluid Reconcilable Immersed Boundary Method (PUReIBM). PR-DNS results are obtained over a range of mean slip Reynolds number (1-100) and solid volume fraction (0.1-0.5). Fluid heating is important in gas-solid heat transfer, especially in dense low-speed flows, and the PUReIBM formulation accounts for this through a heat ratio which appears in the thermal self-similarity boundary condition that ensures thermally fully-developed flow. The average volumetric interphase heat transfer rate (average gas-solid heat transfer) that appears in the average fluid temperature evolution equation is quantified and modeled using PR-DNS results. The Nusselt number corresponding to average gas-solid heat transfer is obtained from PR-DNS data, and compared with Gunn's Nusselt number correlation (Gunn, 1978). A new Nusselt number correlation is proposed that fits the PR-DNS data more closely and also captures the Reynolds number dependence more accurately. It is shown that the use of Nusselt number correlations based on the average bulk fluid temperature in the standard two-fluid model for gas-solid heat transfer is inconsistent, and results in up to 35% error in prediction of the average gas-solid heat transfer. Using PR-DNS data, a consistent two-fluid model is proposed that improves the predicted average gas-solid heat transfer.

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“…As noted earlier, simultaneous parameter estimation of such an integrated model is not currently feasible in Aspen Plus mainly because of the segregation of the diffusivity submodel, reactions submodel, and the selected mass transfer coefficients and interfacial area submodels. To circumvent this issue, FOQUS, developed as part of U.S. DOE’s CCSI, is used. The objective function is presented in eq .…”

Section: Deterministic Model Developmentmentioning

confidence: 99%

“…This large-scale optimization problem is computationally expensive and can be difficult to solve in commercial software. The Framework for Optimization, Quantification of Uncertainty and Surrogates (FOQUS), that can read from and write to Aspen Plus models, is utilized in this work for developing the integrated model. More details on the simultaneous regression approach is given in section .…”

Section: Introductionmentioning

confidence: 99%

Development of a Rigorous Modeling Framework for Solvent-Based CO₂ Capture. 1. Hydraulic and Mass Transfer Models and Their Uncertainty Quantification

Chinen

Morgan

Omell

et al. 2018

Ind. Eng. Chem. Res.

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Rigorous process models are critical for reducing the risk and uncertainty of scaling up a new technology. It is essential to quantify uncertainty in key submodels so that uncertainty in the overall model can be appropriately characterized. In solvent-based postcombustion CO2 capture technologies, mass transfer and column hydraulics are key factors affecting the performance of the absorber. Developing submodels for mass transfer, column hydraulics, and reactions is a challenging multiscale problem since the phenomena are tightly coupled and it is difficult to design experiments to isolate each properly. In particular, simultaneous mass transfer coupled with fast reaction kinetics makes it difficult to measure the mass transfer rate and reactions rate individually. The typical approach to solving this issue is to use proxy systems to conduct experiments under mass-transfer-limited or reaction-limited conditions. This approach can lead to inaccurate mass transfer submodels. In this paper, a novel simultaneous regression approach is proposed where submodels for mass transfer, diffusivity, interfacial area, and reaction kinetics are optimally identified using experimental data from multiple scales and operating conditions. Since all models have some level of uncertainty, a rigorous uncertainty quantification (UQ) technique is implemented for the hydraulic and mass transfer submodels based on Bayesian inference. Posterior distributions of submodel parameters are propagated through the column model to obtain the uncertainty bounds on critical performance measures.

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Annual Report: Carbon Capture Simulation Initiative (CCSI) (30 September 2013)

Cited by 3 publications

References 0 publications

Emerging concepts in intermediate carbon dioxide emplacement to support carbon dioxide removal

Emerging concepts in intermediate carbon dioxide emplacement to support carbon dioxide removal

Modeling average gas–solid heat transfer using particle-resolved direct numerical simulation

Development of a Rigorous Modeling Framework for Solvent-Based CO₂ Capture. 1. Hydraulic and Mass Transfer Models and Their Uncertainty Quantification

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Annual Report: Carbon Capture Simulation Initiative (CCSI) (30 September 2013)

Cited by 3 publications

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Emerging concepts in intermediate carbon dioxide emplacement to support carbon dioxide removal

Emerging concepts in intermediate carbon dioxide emplacement to support carbon dioxide removal

Modeling average gas–solid heat transfer using particle-resolved direct numerical simulation

Development of a Rigorous Modeling Framework for Solvent-Based CO2 Capture. 1. Hydraulic and Mass Transfer Models and Their Uncertainty Quantification

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Development of a Rigorous Modeling Framework for Solvent-Based CO₂ Capture. 1. Hydraulic and Mass Transfer Models and Their Uncertainty Quantification