Thermodynamic performances of an absorption diffusion refrigeration system, equipped with an ejector-compressor, using the thermal energy of the exhaust gases of cement kilns
Bibliographic record
Abstract
The depletion and high cost of fossil energy sources are the main cause leading to the energy transition. Industrial processes are increasingly using renewable resources, and adapting the zero-waste policy, to improve their energy efficiency. This study investigates the thermodynamic performance of the diffusion absorption refrigeration (DAR) system, equipped with an ejector and a compressor. The system operates using the residual heat of the gases exiting the cement kiln chimneys, recovered from the high-performance heat exchangers. The system is modelled and optimized using the M2EP analysis method (mass, energy, exergy and performance) and the particle swarm optimization algorithm. The refrigeration machine generates cold by evaporating the strong solution of the H2O+NH3 mixture. Hydrogen circulates in the diffusion-absorption-compressor-ejector refrigerator circuit, facilitating the exchange between the evaporator and the absorber. A compressor and an ejector, placed between the evaporator and the absorber, increase the system pressure, therefore the coefficient of performance of the refrigeration machine. MATLAB and Excel software were used to solve the system of equations. The sensitivity of the model to variations in concentration, temperature and pressure was studied and analyzed. The results of this article serve as a decision-making tool for the installation of such a refrigeration system in a cement plant, to be able to valorize the gaseous waste generated.
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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 | 0.001 |
| 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) | 0.002 | 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".