Analysis of natural gas consumption and energy saving measures for powder coating and food processing companies in the Greater Toronto Area (GTA)
Bibliographic record
Abstract
Small and medium industries (SMEs) savings analysis and meaningful performance indicators can help Enbridge Gas Distribution Inc., and individual SMEs make effective decisions to improve facility performance. For this study, information on 11 SMEs’ energy consumption has been provided. This entails: preliminary benchmarking, separation of process and seasonal energy consumption, heating degree days, individual facilities owned reference temperature, normalized annual energy consumption, normalized process and seasonal energy consumption, oven energy consumption, energy balance of oven, energy intensity of oven, and non-productive energy consumption. The most appropriate performance indicator is energy intensity of oven-in bake ovens, cure ovens, and dry-off ovens. The results observed energy intensity in terms of natural gas consumption of bake ovens are from 24m3/ft3 to 30m3/ft3, where the intensity of ovens with finishing process companies are from 8m3/ft3 to 36m3/ft3. Potential natural gas savings from the facilities processing powder coating and baking are 19% to 53% of total oven energy consumption by reducing exhaust energy loss. In the same study observed in analyzing production scheduling, that 8% to 69% of energy consumption can be saved by proper shut-down operation and scheduling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".