Performance Analysis of Clay Bricks Baked With Sustainable and Eco‐Friendly Refuse‐Derived Fuel
Bibliographic record
Abstract
In this study, the potential production of refuse‐derived fuel (RDF) to supplement fossil fuel coal in the firing process of clay bricks is explored as a possible solution to the baked brick industries. RDF‐incorporated brick making may be regarded as a low‐carbon‐footprint and cost‐effective production system in view of the current looming fossil fuel depletion and anthropogenic CO 2 emissions. Results indicate that RDF‐fired bricks perform better than conventional bricks regarding the major parameters. The compressive strength of RDF‐fired bricks, 13.7 N/mm 2 , was higher than the minimum required value of 10.0 N/mm 2 . The water absorption was within the controlled limit of 14.2% (maximum 15%), confirming durability. No efflorescence was observed, indicating that the surface contained a low proportion of water‐soluble salts. Particulate matter (PM) as air emission was 39.4 mg/Nm 3 . NO 2 emissions were well below the 400 mg/Nm 3 limit at 259.2 mg/Nm 3 , while SO 2 and CO concentrations, 115.8 mg/Nm 3 and 82 mg/Nm 3 , respectively, were also within permissible limits. These results suggest that RDF is an environmentally friendly and cost‐effective alternative to conventional brick firing methods dependent on coal, aligning with the principles of the circular economy.
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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 | 0.000 |
| 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) | 0.002 | 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".