A Thermodynamic Investigation of Commercial Kitchen Operations and the Implementation of a Waste Heat Recovery System
Bibliographic record
Abstract
A modeling tool was developed capable of evaluating the thermal performance of a commercial building, for the purpose of objectively quantifying the impacts of both operational changes and technological retrofits. The modeling tool was created using a steady state energy balance approach, discretized into half hour time steps to capture the time varying characteristics of the rate of heat transfer through the building envelope, the ventilation systems, appliance heat gains, heat generated by electricity consumption, solar energy transfer and space heating through exhaust gas energy recovery with the TEG POWER system. Several experimental facilities were used to validate the modeling tool, and to provide inputs to the case studies presented. Data from two separate commercial baking operations was collected, and was shown to be in agreement with the model predictions with a 7% error. Several energy conservation measures were simulated, including switching to idealized methods of exhaust ventilation, sealing and insulating appliances, shutting down appliances during unoccupied hours, and the inclusion of exhaust gas energy harvesting. Implementing all four conservation measures at a single restaurant had the effect of reducing electricity consumption by 14% or approximately 17,700 kWh (64 GJ), and reducing natural gas consumption by 60% or approximately 18,200 m3 (608 GJ) annually. In contrast, proceeding directly to the energy harvesting solution, and bypassing other conservation measures, only allowed for 20% of the total potential energy savings to be realized. If the concepts identified are implemented across 2000 comparable restaurants in Ontario, there is a potential to reduced electricity consumption by 44.4 million kWh and natural gas consumption by 33.7 million cubic meters annually. The measures would effectively eliminate 65,500 metric tonnes of CO2 emissions every year.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".