Retrofit Design Method for CO<sub>2</sub> Emission Reduction
Bibliographic record
Abstract
Utilization of fossil fuel, as one of the major contributors in CO 2 emission production, provides driving force behind global warming. Increasing energy efficiency through energy retrofit of process plants can reduce fuel requirement and hence reduce CO2 emission production. The current practice for reducing CO2 emissions often selects the retrofit options with no consideration of carbon tax or opportunity for carbon emission credit. This work presents a design methodology to select and combine from the heat exchanger network (HEN) retrofit options of different process plants considering the available investment cost for process integration measures and constraining carbon emission reduction target. The presented methodology investigates the capability of the introduced scenarios from combined retrofit options for carbon emission credit opportunity. In this method the decision for degree of heat recovery in retrofit of HEN can be made either based on achieving maximum opportunity for carbon emission credit considering fixed available investment cost or based on investing minimum cost to maintain the emission reduction target with no extra CO2 emission reduction. The final decision making is then based on total payback of the combined retrofit scenario. The method is developed through a case study. Results show that the presented method is capable to include issues of carbon emission trading scheme in making decision for energy conservation in process industries with the objective of reducing CO2 Emission. The results also illustrate that the payback of the combined retrofit options which are selected to meet the emission target are lower and hence better options relative to those that provide CO2 emission credit.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".