Energy efficiency of using a novel fertilizer-based liquid desiccant system to dehumidify indoor plant environments: An experimental analysis
Bibliographic record
Abstract
Plant cultivation in controlled environments such as greenhouses and indoor farms has several advantages over field agriculture and can therefore be an important part of improving the sustainability of global food systems. Among the requirements for effective cultivation in indoor environments is the need to control humidity, but in general the energy cost associated with dehumidification is high. In this study, we evaluate the energy efficiency of a novel dehumidification concept that uses cold concentrated fertilizer as a liquid desiccant solution in a membrane-based contactor. This is the first ever experimental analysis of the process’ energy efficiency – which we define as the amount of energy required to cool desiccant relative to the amount of water vapor removed from the indoor environment. Specific energy use as low as 1.45 Wh per g of water vapor, is observed during laboratory testing when super concentrated calcium nitrate solution is maintained at 8°C. Assuming a coefficient of performance of 5, this translates to specific work of only 0.29 Wh/g. As the batch of fertilizer solution is recirculated and concentration drops, specific work is found to increase to 0.40 Wh/g. The need to adjust fertilizer temperature to minimize specific work in response to changing concentration is clearly shown. Testing is also conducted with several multi-ion fertilizer blends, and similar results are observed. These energy efficiency results compare very favorably with other dehumidification technologies and standards, suggesting a promising future for fertilizer-based dehumidification.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".