Cogeneration and District Energy Systems: Modelling, Analysis and Optimization
Bibliographic record
Abstract
District energy (DE) systems use central heating and/or cooling facilities to provide heating and/or cooling services for communities and can be particularly beneficial when integrated with cogeneration plants for electricity and heat. This book provides information on district energy and cogeneration technologies, and the systems that combine them, with a focus on their modelling, analysis and optimization. Topics covered include a brief introduction to district heating and cogeneration; background material on thermodynamics and exergy analyses; models for cogeneration, heating and district heating, and chilling and district cooling; descriptions and analyses of configurations for integrating cogeneration and DE technologies; economics of cogeneration and DE; environmental impact of cogeneration systems, including wastes and carbon dioxide emissions and their allocations; modelling and optimization of cogeneration-based district energy systems accounting for economics and environmental impact; developments and advances in technologies and systems for cogeneration and DE; and future directions. Examples and case studies are included throughout the book to illustrate the material covered, and to demonstrate the importance, benefits and value of cogeneration and district energy technologies in achieving sustainable and efficient energy systems.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".