Underground plant factory in a mine tunnel – Part 1: Conceptual design and thermal simulation
Bibliographic record
Abstract
• No research addresses underground plant factory design and thermal simulation • This study developed a technology for energy-efficient underground plant factories • Underground mine tunnels provide suitable growth conditions, except natural light • Heat loss and gain through rock envelope reduce cooling and heating loads Producing leafy vegetables in plant factories (indoor vertical farming) in controlled environment provides a solution for food security to alleviate the impact of global warming and increasing extreme weather events on food production yet their high energy consumption limits the expansion of this industry. The objective of this study is to decrease energy consumption of plant factories by utilizing unused mining tunnels to house the plant factories i.e., underground plant factories (UGPF). The novelty of this research lies in the potential to reduce heating, ventilation and air conditioning (HVAC) energy consumption in controlled-environment plant production facilities by leveraging the stable thermal conditions of unused underground mine tunnels. An UGPF is conceptually designed for a northern metal mine drift and energy loads are predicted using SketchUp Plugin (geometry development software interface) and OpenStudio (building energy simulation) software packages. Due to high rock thermal conductivity (RTC) and low virgin rock temperature (VRT), high heat loss occurs through the surrounding rock envelope in the first 6 months although it gradually reduces but still significant, which reduces cooling load in light period of the UGPF. The initial (on first day) HVAC load is 28.3% lower than the surface plant factory, but with increasing operation time it increases because of reduced heat loss through rock envelope. After 3 years of operation, for the base case of 2.75 W/m.K RTC and 11°C VRT, the HVAC electricity consumption per unit cultivation area is 251 kWh e /m 2 .year, achieving 8.9% reduction as compared to the conventional plant factory. Sensitivity analysis shows that the light emitting diodes efficacy and photosynthetic photon flux density are dominant factors on energy load while the number of tiers is less influential considering the energy load per unit cultivation area.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".