Mathematical Modeling and Analysis of Distributed Energy Systems for a Refinery in Kuwait
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide In this study, a model is developed to optimally integrate various energy generation technologies within a refinery to help reduce economic costs as well as mitigate carbon emissions. The combined heat and power system was found to reduce 80 Mton of CO 2 emissions while saving $2.61 billion dollars over 30 years as opposed to utilizing boilers and grid-connected electricity. Maximum carbon emissions can be prevented by installing wind turbines to reduce further 49 Mton of carbon emissions, saving at an added cost of $53.4 million. Purchasing electricity completely from the grid was found to be the most expensive option, resulting in a monthly average of $25 million. Changes in various factors such as the land available for installation of technology, electricity tariffs, and efficiency of modules and their impacts on the total project costs and emissions were studied. It was found that solar photovoltaic (PV) modules can be a more economical and environmentally friendly option than wind technology if they were equally efficient. Moreover, grid-connected electricity would only be the most economical option if it were purchased at $0.03/kWh or lower. However, it is currently sold at close to $0.10/kWh, making CHP the most economic option for refineries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".