Energy Use, Energy Savings and Environmental Analysis of Industrial
Bibliographic record
Abstract
In this paper energy use , energy and bill savings, emission reduction and pay period using high efficient motor, variable speed drive by modulating speed of boiler and compressor has been estimated using energy audit data.It has been found that 68,923 MWh, 132,922 MWh, 78,769 MWh and 49,230 MWh of energy can be saved for 40%, 60%, 80% and 100% motor loadings, respectively for 20% speed reduction in the boiler system.Corresponding bill savings for the aforementioned energy savings have been found to be US$ 4,411,052, US$ 8,507,028, US$ 5,041,202, and US$ 43,150,751 for 40%, 60%, 80% and 100% motor loadings, respectively for 20% speed reduction in the boiler system.Energy savings and bill savings resulting from the use of high-efficiency motor in air compressor are 6703 MWh, 8251 MWh, and 10571 MWh, total energy, can be saved for 50%, 75%, and 100%, motor loadings, respectively.Similarly, bill savings for the preceding amounts of energy savings are US$ 428,984, US$ 528,042 and US$ 676,538, respectively.It is also clear that the use of high efficiency motor and VSDs leads to substantial energy savings and an enormous reduction in emissions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".