Bibliographic record
Abstract
Owens Corning and other glass manufacturers have used oxy-fuel combustion technology successfully in furnaces to reduce emissions, increase throughput, reduce fuel consumption and, depending on the costs of oxygen and fuel, reduce energy costs. The front end of a fiberglass furnace is the refractory channel system that delivers glass from the melter to the forming process. After the melter, it is the second largest user of energy in a fiberglass plant. A consortium of glass companies and suppliers, led by Owens Corning, was formed to develop and demonstrate oxy/fuel combustion technology for the front end of a fiberglass melter, to demonstrate the viability of this energy saving technology to the U.S. glass industry, as a D.O.E. sponsored project. The project goals were to reduce natural gas consumption and CO2 green house gas emissions by 65 to 70% and create net cost savings after the purchase of oxygen to achieve a project payback of less than 2 years. Project results in Jackson, TN included achieving a 56% reduction in gas consumption and CO2 emissions. A subsequent installation in Guelph ON, not impacted by unrelated operational changes in Jackson, achieved a 64% reduction. Using the more accurate 64% reduction in the payback calculation yielded a 2.2 year payback in Jackson. The installation of the demonstration combustion system saves 77,000 DT/yr of natural gas or 77 trillion Btu/yr and eliminates 4500 tons/yr of CO2 emissions. This combustion system is one of several energy and green house gas reduction technologies being adopted by Owens Corning to achieve aggressive goals relating to the company’s global facility environmental footprint.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".