Insulated Concrete Form (ICF) Foundation as Solar Thermal Energy Storage Integrated with Reverse Osmosis/Thermal Water Desalination Plant for Cold Climate
Bibliographic record
Abstract
Reverse osmosis (RO) and thermal water desalination are considered as the most effective water treatment techniques. In reverse osmosis water treatment, a preheating step can help to improve water production by reducing the compressor pumping power. In thermal water desalination, thermal energy is needed in different parts to increase the water temperature for the evaporator step. This paper presents an innovative approach to using insulated concrete form (ICF) foundations as a solar thermal energy storage system in conjunction with reverse osmosis or thermal water desalination plant in cold climates. The proposed system combines renewable energy and desalination technology to provide a sustainable solution for water scarcity in remote areas. The ICF foundation serves as a heat storage, which stores excess heat generated by solar thermal collectors during the day. It releases it during the night to meet the heating needs of the water desalination plant. Two different ICF-based systems are modeled and simulated using TRNSYS software and compared with a system without thermal energy storage to investigate the advantages of ICF-based systems. It is shown that the ICF-based system has 33% and 43% higher solar fraction and collector efficiency, respectively, than a base case system with a solar thermal collector. It is shown that the intermittency of sunlight is compensated by means of thermal storage, and the system is a viable solution for the water desalination preheating process.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".