LLNL ARRA Report on CCSI Uncertainty Quantification Capabilities with Applications on MEA Process Models

Ng, Brenda; Tong, Charles; Ou, Jiong; Leek, J

doi:10.2172/1092509

Cited by 1 publication

(1 citation statement)

References 6 publications

(5 reference statements)

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“…Reliability estimations come from the mechanical risk modeling approach. Insights from the design process modeling efforts were augmented with literature searches on the performance of each CC technology, which helped to provide some uncertainty ranges . Additional epistemic measures were included to account for maturation level (TRL) for the system components and some of the qualitative performance issues.…”

Section: Applicationmentioning

confidence: 99%

Development of a risk-based comparison methodology of carbon capture technologies

Engel

Dalton

Dale

et al. 2014

Greenhouse Gas Sci Technol

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Given the varying degrees of maturity among existing carbon capture (CC) technology alternatives, an understanding of the inherent technical and fi nancial risk and uncertainty associated with these competing technologies is requisite to the success of carbon capture as a viable solution to the greenhouse gas emission challenge. The availability of tools and capabilities to conduct rigorous, risk-based technology comparisons is thus highly desirable for directing valuable resources toward the technology option(s) with a high return on investment, superior carbon capture performance, and minimum risk. To address this research need, we introduce a novel risk-based technology comparison method supported by an integrated multi-domain risk model set to estimate risks related to technological maturity, technical performance, and profi tability. Through a comparison between solid sorbent and liquid solvent systems, we illustrate the feasibility of estimating risk and quantifying uncertainty in a single domain (modular analytical capability) as well as across multiple risk dimensions (coupled analytical capability) for comparison. This method brings technological maturity and performance to bear on profi tability projections, and carries risk and uncertainty modeling across domains via inter-model sharing of parameters, distributions, and input/output. The integration of the models facilitates multidimensional technology comparisons within a common probabilistic risk analysis framework. This approach and model set can equip potential technology adopters with the necessary computational capabilities to make risk-informed decisions about CC technology investment. The method and modeling effort can also be extended to other industries where robust tools and analytical capabilities are currently lacking for evaluating nascent technologies.

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Section: Applicationmentioning

confidence: 99%