Development of Waste Heat Fired Activated Carbon Ammonia Adsorption Chiller
Bibliographic record
Abstract
Adsorption systems are promising alternative to the existing refrigeration systems in the wake of alarming energy crisis and potential danger due to the use of ozone depleting refrigerants. Sorption systems use thermal energy as its power source and solid adsorbent beds to adsorb and desorb a refrigerant to obtain the desired cooling effect. Solar energy, engine exhaust and low grade waste heat could be used to drive the sorption compressors. The use of non-ozone depleting refrigerant makes these systems environmentally benign. Adsorption refrigeration systems can meet the cooling requirement across a wide range of temperatures. These systems have minimal moving parts and hence they are free of noise, vibration and related problems. This paper will present the description, operation and simulated system characteristics for a 1000W adsorption chiller. The adsorption system performance factors such as coefficient of performance (COP), specific cooling power (SCP), and cycle time were predicted. Parameters such as the generation and adsorption temperature, condenser and evaporator temperature were varied to analyze the influence of the varied operating conditions. A two bed 1000 W capacity adsorption water chiller to chill water from 12 to 7 C was considered for the simulation. COP of the simulated system ranged between 0.3 to 0.4 and SCP from 90 to 180 W/kg AC respectively. The maximum value of cycle time obtained was 25 minutes when the generation outlet temperature was 180 oC.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".