Population-balance modeling of CO2 foam for CCUS using nanoparticles
Bibliographic record
Abstract
Foam implementation for carbon capture, utilization and storage (CCUS) can greatly improve CO2 mobility control, resulting in enhanced hydrocarbon production and carbon storage capacity. The use of nanoparticles (NP) to create robust foam structures has recently gained attention. Local foam generation and coalescence dynamics can be described by mathematical models. Here we address knowledge gaps for NP foam in porous media by tracking the bubble density (nf) of NP foam data spatially and temporally using an established surfactant (SF) population-balance model. We suggest a reduced shear-thinning effect, compared to SF, to accurately model NP CO2 foam flow in both the high- and low-quality regime. A NP foam rheological transition appeared at gas fraction fg = 0.85. The nf parameter increased linearly with distance from inlet for NP foam, with reduced CO2 mobility and improved displacement efficiency compared to co-injections of water and CO2.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".