Innovative ground air heat exchanger system for climate regulation in cold climate greenhouses
Bibliographic record
Abstract
Greenhouse adoption is increasing globally to enhance food security, particularly in cold climates. However, their operation often faces substantial heating demands and significant heat loss due to traditional dehumidification methods. This study presents a novel, energy-efficient dehumidification approach designed to reduce heat and CO 2 losses in greenhouses. Specifically, it investigates the use of a Ground Air Heat Exchanger (GAHE) system, which utilizes the stable ground temperature to cool humid greenhouse air below its dew point, effectively condensing excess humidity. Additionally, the GAHE system provides cooling during warmer periods when indoor temperatures exceed desired setpoints, offering dual-function climate control. An analytical–numerical model of the GAHE system was developed and coupled with a greenhouse climate simulation platform to evaluate its dehumidification performance. The system was optimized using a Genetic Algorithm to maximize moisture condensation per unit length of GAHE pipe. The optimized GAHE configuration was assessed across six Canadian cities, with varying installation areas. Performance results were compared against conventional dehumidification methods, including Natural Ventilation (NV) and Mechanical Cooling and Dehumidification (MCD). Results indicate that GAHE system reduces annual average deviations of air temperature and relative humidity from their setpoints, reflecting improved dehumidification and cooling performance, by up to 35% and 18.4% compared to NV and MCD systems, respectively, and by up to 79% and 75% for cooling performance relative to the same systems. Moreover, the GAHE system reduces the greenhouse heating load and CO 2 supply requirements by 30–44% and 36.82–58.83%, respectively, relative to NV, without significantly affecting crop productivity.
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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".