Parametric Analysis and Multi-Objective Optimization for Energy-Efficient and High-Performance HVAC Air Filter Design and Selection
Bibliographic record
Abstract
Abstract Heating, ventilation, and air conditioning (HVAC) systems are notorious for their high energy consumption in buildings, particularly in regions with extreme cooling or heating demands. Air filters play a vital role in these systems, affecting both energy efficiency and indoor air quality. However, high-efficiency filters, due to their significant increase in airflow resistance, require excessive energy compared to low-efficiency filters. This poses a challenge in finding the optimal compromise between reducing energy consumption and enhancing indoor air quality. To address this challenge, a meticulous selection process is crucial in achieving a middle ground that satisfies both objectives. Proper sizing and design of air filters are therefore essential for successful HVAC projects. This paper introduces the utilization of optimization techniques as decision-support tools to determine the optimal design parameters of commonly used HVAC air filters under various scenarios. The developed model incorporates multiple objectives and design criteria, including life-cycle cost (LCC), filter size, and efficiency. By leveraging the differential evolution optimization technique, an algorithm is developed to forecast a range of optimal solutions (Pareto front) based on predefined system criteria and boundary conditions. The model is extensively tested and demonstrates exceptional performance in returning optimal solutions, in addition to the capability of narrowing down and converging to a single value. This methodology holds significant potential in assisting investment decisions concerning HVAC air filters, providing valuable insights for optimizing energy efficiency while ensuring satisfactory indoor air quality.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".