Energy Symbioses in Eco-Industrial Parks: Models and Perspectives
Bibliographic record
Abstract
Design and implementation of Eco-Industrial Parks (EIPs) is a practical and scientific solution to achieve sustainable industries. Specifically, energy exchange networks can significantly contribute to the pollution reduction by recovering and sharing wasted heat generated in industrial processes. Despite this perceived fact, the existing research seems mainly looking for one to one relation and lacks in methods for modeling and optimizing multi-synergy symbioses that is essential for EIPs. This paper reviews main concepts in designing industrial symbioses and proposes an optimization model to exchange the residual energy between individual industries in an EIP. Using mathematical programming, the model decides the best set of connections between energy suppliers and users to minimize the total cost and maximize energy symbioses. The presented models analyze perspectives to potential symbioses for individual industries and EIP managers. A detailed discussion clarifies how these perspectives can affect the optimized symbioses. The model is validated using anonymized data of a real case. The result shows that various perspectives to the model provide different energy network topographies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".