Energy analysis of underground coal gasification with CO <sub>2</sub> capture and auxiliary power production
Bibliographic record
Abstract
Underground coal gasification is a method of converting in situ coal into synthesis gas through the same chemical reactions that occur in surface gasifiers. If underground coal gasification systems are combined with carbon capture and storage methods, CO 2 emissions can be greatly reduced. Carbon capture and storage methods require considerable amount of energy to separate CO 2 and prepare it for transport and storage, which is typically considered a parasitic loss. This study investigates utilizing waste heat rejected during processing of syngas from underground coal gasification using an auxiliary power plant, to supply the required energy associated with amine-based CO 2 capture and compression processes necessary for transport and sequestration. The system investigated is the Newman Spinney underground coal gasification test plant coupled with a basic Rankine cycle. It is shown that the energy requirement for CO 2 processing can be met through the use of an auxiliary plant. The basic model illustrates that the power produced by the auxiliary plant is 2.1 times greater than that required by CO 2 compression, while supplying the required heat for amine-based CO 2 capture. Parametric analyses are performed to investigate the impact of air injection rate, syngas cooling and CO 2 capture and compression requirements on system performance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".