IWAYS - Recycling of Heat, Water and Material across Multiple Sectors: Ceramic, Chemical and Steel Industry
Bibliographic record
Abstract
In the framework of the iWAYS project, a synergy between energy and water reclamation and exploitation is addressed by means of the development and the installation of a wide array of technologies in three different industrial sectors: ceramic tile manufacturing, aluminium fluoride production and steel tubes manufacturer. The aim of the project is the creation of customized and integrated systems to achieve a substantial reduction in the thermal waste and in the freshwater consumption; this is the principal challenge the iWAYS project is solving by developing a set of technologies capable of recovering water and energy from challenging exhaust streams for productive use in the industrial processes. iWAYS systems will then treat steam condensate to meet the water quality requirements of each industrial process, while the recovered heat will be used to reduce primary energy consumption. iWAYS will recover additional materials from flue gas such as valuable acids or particulates, improving the production’s raw material efficiency and reducing detrimental emissions to the environment. The iWAYS technology will provide a reduction in the freshwater consumption greater that the 30% in each industrial case; with regards to the energy recovery, iWAYS will recover 6 GWh/y in the ceramic sector, more than 5 GWh/y in the chemical scenario and approximately 1 GWh/y in the steel sector. The iWAYS solution will have a payback lower than 5 years.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".