Study on CCS source-sink matching and its cluster deployment in multi-type geological bodies in Anhui Province of China
Bibliographic record
Abstract
Scientific and reasonable source-sink matching is an important basis for the site selection of CCUS (i.e., Carbon capture, utilization and storage) cluster deployment project. In this study, deep saltwater layer, depleted oil and gas reservoir and unrecoverable coal field in Anhui province are the research objects. Firstly, the evaluation methods of CO 2 storage potential of multi-type geological bodies are discussed, respectively. Secondly, the methods of source-sink matching of CCS and its pipe network optimization are discussed. Then the application schemes of CCS source-sink matching and its cluster deployment are established. The results show that the cumulative CO 2 emissions of 27 active coal-fired power plants in Anhui province are nearly one trillion tons during the 30-year planning period. The geological storage potential of CO 2 is 899.08 × 10 8 t, and the deep saltwater layer has absolute storage advantage. After the optimization, the total amount of CO 2 stored in saltwater layer is 9198.72 million tons, and the cumulative planning pipeline is 684.37 km, which requires a cumulative capital of 5.20*10 11 $, and pipeline planning and accumulated capital can save 67.19% of pipeline length and 26.01% of total capital, respectively. The CCS cluster deployment in Anhui province can focus on Huaibei coalfield, Huainan coalfield, Hefei metropolitan area, and Yangtze River Economic Belt, and should give full play to the advantages of saltwater layer layout. Demonstration projects should be built in clusters to simplify the overall layout of CCS pipe network. Connecting C23 and C25, C27 and C10, and C18 and C13 can connect the four CCS cluster deployment areas in Anhui province into a whole. This study can provide theoretical support for the cluster deployment and demonstration project implementation of CCS in Anhui province.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".