Energy targeting of abandoned mines to supply greenhouse energy demand in cold climates
Bibliographic record
Abstract
The combination of a Solar Assisted Geothermal Heat Pump system (SAGHP) with a multi-zone greenhouse is investigated to take advantage of water flooding in abandoned open pit mines in Canada. The envisioned system includes an Air Handling Unit (AHU), Heat Recovery Ventilation (HRV), daily Thermal Energy Storage (TES), and daily Domestic Hot Water (DHW). The main objective is to satisfy the greenhouse heating, cooling, and dehumidification loads, for the considered application, while minimizing energy consumption. This analysis is conducted using data extracted from a case study of a commercial, multi-zone greenhouse, considering different daily weather conditions throughout a year. To reduce the computation time, a clustering approach based on the K-Means method is applied to obtain a small number of typical weather days. Elbow, Dendrogram, and Silhouette approaches confirmed that it is possible to represent a year as six different Typical Days (TD), which can be further categorized as Heating only (TD1 and TD2), Heating/Cooling (TD3 and TD4), and Cooling only (TD5 and TD6). Dynamic Pinch Approach (DPA) showed a great ability to target the minimum energy consumption and maximize the potential heat recovery for each typical day. The study focuses on energy targeting, with discussion of preliminary design considerations, such as the solar hot water (SHW) system, Thermal Energy Storage (TES), and heat pumping. Results revealed that mine water can significantly improve the energy system efficiency, specifically where heating/cooling or only cooling is dominant (TD3, TD4, TD5, and TD6). For instance, by integrating an AHU with the greenhouse for the TDs where heating/cooling is dominant, 22.5% energy saving is achievable. The incorporation of heat pumping, waste heat recovery, and solar thermal collectors through mixed direct/indirect heat recovery (i.e. via TES) can reduce hot utility usage in the considered application by as much as 40%.
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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 | 0.001 |
| 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".