A novel systematic heat integration and heat recovery approach for reactivating abandoned mines to meet energy demand of greenhouses-application of dynamic pinch analysis
Bibliographic record
Abstract
Designing an optimum and efficient energy system for a greenhouse in cold climate conditions, such as Canada, is a very challenging task, and is even more sophisticated when different sources of energies (solar, geothermal, etc.) should be integrated into the energy system. This study, for the first time, is proposing a systematic heat integration approach, based on Dynamic Pinch Analysis, to improve the efficiency of the energy system of a greenhouse through taking advantage of heat recovery from waste energies (grey water and air ventilation). Also, it proposed a novel methodology to integrate a solar assisted geothermal heat pump system into a greenhouse to eliminate fossil fuel consumption. Following the evaluation of the geothermal energy potential of an open pit lake of an abandoned mine (King Beaver Mine), a mathematical energy model was developed to calculate the energy demand of the case study greenhouse in Quebec, Canada. To reduce the calculation time, two unsupervised machine learning techniques (K-Means and K-medoids) were used to identify the typical days (TDs). For each typical day and each time slice (1 hr), composite curves (CCs) were plotted. These CCs enabled energy targeting by maximizing heat recovery and facilitating the design of an optimal heat exchanger network (HEN). A techno-economic analysis was then conducted to determine the optimal HEN configuration among the scenarios, ensuring efficient placement of heat exchangers to maximize energy efficiency and cost savings for the greenhouse climate control system. It is shown that by taking advantage of heat recovery from waste energy 38 percent energy saving is possible. Calculations indicate that using a properly sized thermal energy storage unit could reduce the condenser size of the heat pump by over 40 percent.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".