Bibliographic record
Abstract
Anthropogenic CO2 emissions from the combustion of fossil fuels and various industrial activities are a primary driver of global warmingGlobal warming and its related climate impactsClimate. To address this challenge, carbon captureCarbon capture, utilization, and storage (CCUS)Carbon capture, utilization, and storage (CCUS) technologiesDecarbonization play a vital role in global decarbonization strategies. A range of commercially available carbon capture solutions exist, each adaptable to specific CO2 recovery applications. Once captured and purified, CO2 must be compressed for efficient transport, further processing, and long-term storage. This chapter provides a comprehensive overview of key CCUSCarbon capture, utilization, and storage (CCUS) considerations, includingDecarbonization the context of global decarbonization initiatives, CO2 product requirements, available capture technologies, and the unique thermodynamic challenges associated with CO2 compressionCO compression. It also explores the mechanical design aspects, common construction materials, compressor train configurations, shaft sealing techniques, capacity control methods, and modular design strategies for the two predominant types of CO2 compressors: centrifugal and integrally geared.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.059 | 0.030 |
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".