Optimization of Network Carbon Capture and Storage System (CCS) Using Mathematical Approach
Bibliographic record
Abstract
Carbon capture and storage (CCS) is one of the technologies to reduce greenhouse gas emissions (GHG) tocapture of CO2 from the flue gas of a power plant that typically use coal as a Source of energy and then store it ina suitable geological storage (in specific locations). In practice, these sites may not be readily available forstorage at the same time that the Sources (GHG producing) are operating which gives rise to multi – periodplanning problems. This study presents a mathematical approach by considering constraints limit flowratereceived by Sink, various time availability of Sink and Source and calculation with the purpose to determine theminimum cost network which is getting the maximum load that is exchanged from Source to Sink. Illustrativecase studies are given to demonstrate the application of mathematical models to obtained with the exact result ofthe exchange network from Source to Sink. Derived from network obtained from the calculation of theMaximum Load Source to Sink and results may vary in accordance with the limitations that exist in themathematical model. The case study has been prepared with 2 cases, first 6 Source and 3 Sink with value ofSource Load is greater than the amount available on the Sink. Also, second case is 2 Source and 5 Sinkwithvalue of Source Load is smaller than the amount available on the Sink. In addition, Case Studies tominimize the cost of pipeline construction and distribution of CO2 by plant and storage location determination inJava. Flowrate restriction factor that goes into Sink, Source and Sink establishment time and cost are taken intoaccount can affect the networks that can be exchanged from the Source to the Sink.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".