A Statistical Approach to Optimize the Solar Adsorption Refrigeration System
Bibliographic record
Abstract
Solar-powered refrigeration based on adsorption cycles is simple, quiet in operation and adaptable to small medium or large systems. Application potentials include storage of vaccines for immunization against killer diseases in remote areas, preservation of foodstuff for future use and manufacture of ice. Already Solar Adsorption Refrigeration (SAR) is a technical success, but it is not commercially competitive with either the conventional vapor compression or PV refrigerators. Further developmental research is, therefore, required for improvements in existing designs either to increase system overall performances significantly or to reduce system unit cost or both. In this study a statistical approach was used to optimize of solar adsorption air conditioning or refrigeration unit using ANOVA analysis. It was found that the coefficient of performance (COP) of a SAR system does not depend sharply on the evaporator temperature without any relation of the system conditions. Instead COP depends significantly on both condenser temperature and type of couple used in the refrigeration system. In addition some factors that concern about design could have an effect on the COP. From the optimization model the maximum value of COP was found under low condenser temperature and high generator temperature. Zeolite/water couple has the maximum COP value whereas the activated carbon has the minimum value. Key words: Solar adsorption; Refrigeration; ANOVA; SAR
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".