EFFECT OF FUEL GRADE ON THE OPTIMIZED UNIT LOADING IN RESPONSE TO VARIABLE ELECTRICITY DEMAND OF A FUEL OIL-FIRED POWER PLANT
Bibliographic record
Abstract
An optimization method based on a dynamic linear programming tool for determining best load distributions over distinct units of a fuel oil-fired power plant is presented. Two approaches are analyzed in this work. In the first approach, the objective function is based upon a minimization of the fuel consumption by the power plant. The second approach aims to minimize the total operational costs, i.e. the sum of the internal (or fuel) boiler costs and the external costs (or costs of damage done by the power plant to the environment and humans). The model used as the basis of the optimization also takes into account the changes in key operating variables as well as boiler efficiency with load variations. A 1330-MW fuel oil-fired power plant is the focus of the study. The method is applied to data from the power plant for the three climatic seasons in Thailand and two fuel options (dependent on the fuel grade). The optimum time-domain loading of the power plant units is strongly affected by the objective function, thermal cycle efficiency of the individual units and grade of fuel oil fired in the boilers. It was shown that application of the optimization method can reduce the total costs by 0.3-0.9% depending upon the seasons and fuel options.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".