CO2 utilisation pathways to produce synthetic fuels and other value-added liquid products
Bibliographic record
Abstract
The conversion of CO2 into fuels and chemicals has emerged as a valuable alternative in the battle to combat climate change, particularly in geographical regions where the subsurface properties are not suitable for CO2 sequestration. This paper provides a review of various pathways for utilising captured CO2 to produce synthetic fuels and green chemicals. The pathways are generally agnostic to the source of the CO2, which mainly affects the purification requirements (such as desulfurisation or deoxygenation). The CO2 is typically converted into CO, which could be achieved by known chemical processes such as reverse water shift and CO2 reforming. The CO and H2 produced from these processes are the building blocks for synthetic liquid products such as methanol, ethanol, jet fuel, diesel, and naphtha. In another variation, the H2 could be generated from renewable energy sources and electrolysis to produce e-fuels from the captured CO2. A review of the current and developing technologies and their technology readiness levels is included to determine their suitability for industrial applications, as well as a high-level discussion on factors impacting their economic viability, such as the differences in compression and other energy requirements, process flexibility, and requirements for intermediate storage buffering. The relative cost differences between the various CO2 utilisation pathways are also presented along with a discussion on suitable cost reduction strategies for each option.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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.
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".