Waste Heat Recovery Technologies on Optimized CHP-BESS Plant: A Performance Comparison Between Organic Rankine Cycle and H2O-NH3 Absorption Plant
Bibliographic record
Abstract
Combined, heat, and power (CHP) plants, integrated with battery energy storage systems (BESS), represent a feasible solution to meet electric and thermal demand with a single fossil primary energy source.In this work, a comparative analysis of two waste heat recovery technologies for a hospital was performed.An ammonia-water absorption, power, and cooling (APC) system and an organic Rankine cycle (ORC) plant were combined within an optimized fossil primary energy saving (PES) oriented batteryintegrated cogeneration system, characterized by natural gas internal combustion engines, which waste heat is recovered inside the APC and ORC plants.A control strategy was implemented to optimize the efficiency of the system, prioritizing cooling or electric power production based on hourly Hospital's demand.The APC-based trigeneration configuration reaches a 20% of PES and a 24% reduction in CO2 emissions, while the ORC-based trigeneration system performs a 19% improvement in PES and a 23% reduction in CO2 emissions, compared to the hospital separate production of the same amount of energy.The simple payback (SPB) period for both configurations increases slightly, moving from 3.23 years for the optimized CHP-BESS plant to 3.3 years for the APC-based configuration and 3.4 years for the ORC-based plant.
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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) | 0.002 | 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".