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Record W7046863117

Design and verification of a Hydraulic Air Compressor as a CO2 capture and sequestration system.

2023· dissertation· en· W7046863117 on OpenAlexfundno aff

Bibliographic record

VenueLu Zone Ul (Laurentian University) · 2023
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersGovernment of Ontario
KeywordsCarbonationCabin pressurizationCarbon capture and storage (timeline)Carbon sequestrationInjectorMass transferCarbon dioxide removal
DOInot available

Abstract

fetched live from OpenAlex

As a result of human activities, the level of atmospheric carbon dioxide (CO2) has increased. Carbon capture and sequestration (CCS) technologies offer an effective approach for mitigating CO2 from various industrial process to reduce the global climate change effect. These technologies can be integrated into existing infrastructure with minimal disruption. This study reviews on post- combustion CO2 capture and sequestration techniques, with a specific focus on mineral carbonation process routes and their potential feedstocks. Mineral carbonation is an approach that mimics the natural weathering of rock, in which metal oxide-bearing materials, for instance natural silicate minerals (serpentinite, olivine) react with gaseous CO2 to form solid carbonates. This process takes place on geological time scales, but it can be accelerated by increasing the concentration of CO2 in a reactor through pressurization of the system. The purpose of this study was investigation of technical feasibility of a CCS process by means of a hydraulic air compressor (HAC). A series of experiments were conducted on the HAC pilot plant to investigate the potential of the system as a post-combustion CO2 capture system. Those experiences experimentally verified a one-dimensional steady-state model for two-phase bubbly flow. The verified bubbly flow model was used to compare the hydrodynamics and mass transfer characteristics of HAC downcomers and upward co-current flow bubble column reactors and to predict gas liquid mass transfer coefficients. The experimental results showed that the HAC is an effective technology for intensification of CCS processes due to its improved mass transfer performance compared to other mass transfer devices. While the high capital cost of HAC construction, following a general Millar (2014) design paradigm, limits applicability, the horizontal injector loop (HIL), developed in this thesis offers a new apparatus with similar gas compressor performance and reduced height, which would make the concept more accessible for capital restricted projects. A mathematical dynamic kinetic model is developed to simulate the kinetics of CO2 absorption into an alkaline solution in the HIL. This model makes a significant contribution in predicting the absorption rate under operating conditions beyond those achievable in experimental tests undertaken. In addition, this model can be used to guide the design of a new reactor and future experiments, making it a valuable tool for CO2 capture and sequestration activity. Carbon dioxide capture and sequestration by means of a HIL as a pressurized, continuous chemical reactor was also investigated experimentally. The experimental results demonstrated that the HIL has a credible potential for this purpose. These experiments were also simulated using the dynamic kinetic model, which showed good agreement with the experimental findings. However, some potential improvements to the dynamic kinetic model have been identified to enhance its compatibility with experimental conditions.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.012
GPT teacher head0.224
Teacher spread0.213 · 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 designBench or experimental
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
Published2023
Admission routes1
Has abstractyes

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