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Record W6987678884

Thermodynamic analysis of a direct air carbon capture plant with directions for energy efficiency improvements

2021· dissertation· en· W6987678884 on OpenAlexaboutno aff

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2021
Typedissertation
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsExergyWork (physics)Efficient energy useCarbon dioxideCarbon capture and storage (timeline)Energy consumptionCarbon fibersExergy efficiencyProcess (computing)Power station
DOInot available

Abstract

fetched live from OpenAlex

According to the Intergovernmental Panel on Climate Change, Carbon Dioxide Removal (CDR) technologies play a significant role in deep mitigation pathways to limit global temperature rise to 1.5°C. As a result, interest in them is becoming increasingly prevalent, the most widely discussed being Direct Air Capture (DAC), or active removal of carbon dioxide from atmospheric air. While DAC processes have indeed been successfully tested, one of the most prominent being that developed by Canadian company Carbon Engineering, their widespread deployment faces significant headwinds due to prohibitively high energy consumption and its associated costs. Before DAC can be considered to exist in a state of technological readiness, reductions to the installations' energy demand must be realized. This thesis analyzes the thermodynamic behavior of Carbon Engineering's proposed 1 Mt-CO2/year natural gas fuelled DAC plant, which they describe as “a low-risk starting point rather than a fully optimized least-cost design” [Keith et al., Joule 2, 1573], with the aim to illustrate key areas to which energy efficiency improvement measures must target. With an understanding built of the mechanisms by which energy is utilized and irreversibly lost within their plant, suggestions are put forth for directions to pursue for process improvements, with further analysis included on potential alternative plant configurations which would reduce overall heat and power consumption. A thermodynamic work loss analysis is performed on their plant design at a system level, which finds 92.2% of incoming exergy being lost to thermodynamic irreversibilities. A component-level analysis is then performed to detail the mechanisms by which these losses occur in the most energy-intensive plant segments, namely, the calciner and preheat cyclones, air separation unit, water knockout system, CO2 compression system, and power island. The dissipation of chemical exergy in the air contactor component, i.e., the release of stored chemical exergy as low-grade heat to the environment due to the exothermic reaction of CO2 and aqueous KOH, was determined as the largest unavoidable source of work loss. The most avoidable losses were found to be associated with use of natural gas as a feedstock for heat and power, namely, through its introduction of additional CO2 and water to be processed within the plant, and due to gas turbine power production's inherent Carnot efficiency limits. Additional analysis and discussion follows regarding possible loss reduction measures and modifications, the key concept presented being the use of renewable energy to provide plant power, combined with a calciner using electric resistance heating to meet its reduced thermal demand. Use of a readily-available high-temperature heat source for calciner heat is also considered, with thorough description included of its thermodynamic advantages. Finally, the all-electric plant concept is analyzed at a system level, and its advantages compared to the original natural gas fuelled case.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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