Thermal Optimization of a Solar Thermal Cooling Cogeneration Plant at Low Temperature Heat Recovery
Bibliographic record
Abstract
A simple cooling cogeneration has been developed by coupling a Kalina cycle system (KCS) with a vapor absorption refrigeration (VAR) system. The working fluid used in this theoretical thermodynamic evaluation is ammonia water mixture. A low temperature heat recovery (150 °C–200 °C) from engine exhaust gas, solar collectors, or similar can be used to operate the plant. A controlling facility is provided to set the required amount of power or cooling to meet the variable demand. In this proposed plant, the liquid refrigerant absorbs more amount of heat from evaporator surroundings with a flow control located in between power and cooling cycles. The extra included components are condenser, heat exchanger and throttling device over KCS plant. Due to possibility of more cooling, it offers high energy utilization factor (EUF). The coupled plant characteristics are studied with changes in mass split ratio, separator vapor fraction, separator temperature, and turbine concentration to develop efficient working conditions. The power mass split ratio is varied from 80% to 100% to run the coupled plant at nearly full load conditions. The separator vapor fraction and temperature are optimized at 45% and 150 °C, respectively. It is recommended to maintain the turbine concentration above 0.85 for optimum power and cooling. The maximum cycle EUF and plant EUF are 0.15 and 0.06, respectively, at 80% power mass split ratio. The specific power and specific cooling at these conditions are 62 kW/kg and 72 kW/kg, respectively.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".