Organic Rankine Cycle Power Generation for Energy Recovery From Air Compressors
Bibliographic record
Abstract
The organic Rankine cycle (ORC) is a power generating technology that can enable the utilization of waste heat to generate electric power from different sources, including geothermal hot springs, other power generating technologies, and industrial applications. There is a significant technical challenge in producing financially viable ORC plants, due to the high costs associated with custom heat exchange components and turbo-machinery. In the last few years, however, packaged systems have been developed from commercially available refrigeration systems, which significantly reduce cost. The goal of this project was to evaluate the feasibility of generating electricity using the waste heat from industrial air compression equipment. A simulation program was written to model the thermodynamics of the ORC. Several potential working fluids were surveyed and ranked based on their applicability to the industrial operating conditions. In particular, refrigerant R-236fa has been determined to be the most appropriate working fluid. The decision was made by weighing its thermal properties, as well as environmental and health considerations. Two types of heat rejection technology were considered, namely water-cooling and air-cooling, with emphasis on the effect of ambient conditions on ORC performance. It is concluded that water-cooling can be used for a plant located in Ontario, Canada, with the possibility of utilizing air-cooling during the cooler seasons of the year. An installation cost of U.S. $1,300/kW is feasible, yielding a simple payback period of 6.35 years. The results of this research project encourage further work to be done on this application of waste heat recovery.
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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) | 0.004 | 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".