Need for Optimization Under Uncertainty: Designing Flow Exchanges in Eco-Industrial Parks
Bibliographic record
Abstract
Eco-Industrial Parks (EIPs) and Industrial Symbioses (IS) have provided several cost-effective and environmental friendly solutions for the economic growth of countries. The need for excessive materials, water and energy can be reduced by exchanging wastes, by-products and energy among different clusters of industries, which is the fundamental goal of establishing synergies among industries. Symbioses design looks for the best set of connections among industries to satisfy defined objectives. However, there are not enough data to support the design of a new EIP for some industries. The existing research contains multiple objective optimization methods, but lacks details in the real industrial world to consider comprehensive criteria in design of flow exchanges due to the large cost and long establishment time for those synergies. This paper presents a multi-objective model to decide the best network of industries for several exchanges among them. The model helps minimizing costs for multiple product exchanges while considering environmental impacts to be reduced. Moreover, this paper investigates uncertainties affecting synergies within EIPs by incorporating in a modeling process. The presented models are validated through optimizing symbioses in an EIP. The efficiency of single and multiple objective models is analyzed for effects of the selected uncertainties. Future research directions are also discussed.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".