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Record W3036657788 · doi:10.2172/1544431

Dilute Source CO<sub>2</sub> Capture: Management of Atmospheric Coal-Produced Legacy Emissions (Final Technical Report)

2019· report· en· W3036657788 on OpenAlexaff
Jenny S. J. McCahill

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsCarbon Engineering (Canada)
Fundersnot available
KeywordsFlue gasPilot plantProcess engineeringEngineeringProcess (computing)CoalWork (physics)Environmental scienceWaste managementComputer scienceMechanical engineeringOperating system

Abstract

fetched live from OpenAlex

This report presents the scientific and technical findings by Carbon Engineering Ltd (CE) resulting from the DOE sponsored research and development project on Direct Air Capture (DAC) process. The work was performed by CE under the Award DE-FE0026861 from the US Department of Energy’s (DOE) National Energy Technology Laboratory (NETL). The work plan was centered on advancing the Recipient’s DAC technology, developing a better understanding of its performance and cost. The report outlines the results from two key activities that were performed under this award: Studying the performance characteristics of the DAC plant at both pilot and lab scale Techno-economic analysis (TEA) to study two implementations of CE’s proprietary DAC process: i) An early commercial DAC process that captures ~1 MT CO2/year of atmospheric CO2 (Baseline case), and ii) A modified early commercial DAC process that captures 1 MT CO2/year of flue gas CO2 downstream of a subcritical PC post-CCS power plant (Case 1 Option C). Within the first key activity of the project, significant operational improvements and learnings were realized, including for key equipment in the DAC process in addition to the overall system. Learnings included impact of equipment design on the capture, calcination and slaking processes, and operational parameter modifications for optimizing the pelletization process, along with the complex interactions and associated considerations of these on overall system performance. In parallel, benchtop scale equipment was successfully utilized to validate pilot scale data, such as, retention in the pellet reactor, and capture performance parameters of the air contactor. CE continues to conduct core research and development activities; the learnings from this DOE project will contribute to the design, testing and operation activities of CE’s fully integrated DAC validation plant, as well as the first commercial DAC plant. The second key activity incorporated the above learnings, as well as input from vendors and EPC experts, to produce the TEA report. More specifically, the TEA results were based on measured pilot plant performance data for all major unit operations, and on an energy and material balance for the process at commercial scale computed using an Aspen One® process simulation. The cost estimation methodology follows AACE International recommended practices; with an uncertainty range of -15%/+30%. Depending on financial assumptions, energy costs, and the specific choice of inputs and outputs, the levelized cost per ton CO2 captured from the atmosphere for an early commercial plant for the baseline case ranges from 227 to 304 $/t-CO2. And, the levelized cost to capture 98% of the flue gas CO2 emissions in a subcritical PC power plant (Case 1 Option C) through a combination of Cansolv carbon capture system and a modified DAC system was estimated to range from 109 to 160 $/MWh of net power generated. Process improvement opportunities identified during this work, in addition to cost reduction opportunities identified during the Value Engineering exercise associated with the Baseline case indicate that significant reductions in capital costs are available as CE scales up and proceeds through deployment of initial facilities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.251
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2019
Admission routes1
Has abstractyes

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