An Inexact Dynamic Optimization Model for CO<sub>2</sub>Emission Reduction in Subei Region, Northeast China
Bibliographic record
Abstract
In this study, an inexact mixed-integer fuzzy robust linear programming model for coupled management of coal and power with consideration of CO2 emissions mitigation system planning (IMIFLP-CCPM) was developed under uncertainty. This model could reach into the closed relationship and interactive characteristics of China’s coal production, electric power generation, and CO2 emissions in coupled coal and power management system and thus explore the applicability of the decarburization facilities and mechanism incorporated in the system through scenario analysis. Based on the integration of interval linear programming, fuzzy robust linear programming, and mixed-integer linear programming, the IMIFLP-CCPM could effectively incorporate and handle uncertainties presented in terms of interval values and fuzzy sets. Also, dynamic analysis of capacity expansion, facility improvement, and inventory planning within a multi-period and multi-option context could be facilitated in this model. The developed IMIFLP-CCPM was applied to a long-term coupled coal and power management with CO2 reduction systems in Subei region, Northeast China. One base scenario and four CO2 reduction scenarios were presented and analyzed to examine the optimal coal-flow allocation patterns and carbon mitigation schemes for the studied system when forced to comply with a given CO2 emission limit. The results indicated that the IMIFLP-CCPM model could provide in-depth analysis of tradeoffs between system costs, energy security, and CO2 emission reduction, thus helping investigate interactive relationships among multiple economical, environmental, and energy structural targets within the study system. Moreover, the attempt of planning coupled coal and power management with CO2 mitigation under uncertainty would provide an effective reference to cope with the dilemma of energy development and CO2 mitigation under the climate change situation in China.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".