Improving HVAC system performance: towards the design of sustainable and immune buildings
Bibliographic record
Abstract
Superstructures, shopping centers and/or highrise buildings are becoming part of today's city landscapes.On the one hand, concerns about occupants' health, comfort, energy consumption and environment are becoming a main design consideration for these buildings' ventilation systems, and, on the other hand, threats to public safety and security such as the intentional and/or non-intentional release of chemical and other agents into indoor environments have become imminent in everyday life.Thus, designers now, need to integrate in their design not only consideration for the occupants' comfort and building energy consumption but also occupants' health and safety.Activated carbon filters have been used for purification of air and water in industrial applications.However these technologies have not been applied to the non-industrial built environment in general and there is no standard to quantify or to classify the performance of these systems for in-duct mechanical system application.The development of a standard testing procedure and design tool are a very timely effort, since it would create a benchmark for evaluating the contaminant reduction and energy savings of these systems.This paper first describes the experimental set-up for testing activated carbon filters for in-duct mechanical system application, presents the experimental results of twelve different activated carbon filters, then discusses the recommendations for the future works.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".