Evaluation of the Infuence of Chemical Treatment of Olive Pomace Waste on the Thermophysical Properties of Aerated Concrete
Bibliographic record
Abstract
In this study, we have decided to develop a composite material which is included in the list of building materials that would meet the requirements of thermal insulation while at the same time helping to protect the environment.To do this, we used waste from the olive oil extraction industry as a replacement for sand in non-autoclaved aerated concrete, we developed two types of mix, the first one by using olive pomace sand (OPU) with proportions of (0%, 10%, 20% ,30% and 40% by mass) and the other using the same proportions of olive pomace treated (OPT) with NaOH treatment, to study the effect of chemical treatment on the physical and thermal properties of this waste.The results obtained show that chemical treatment gives better physical properties and that this treatment improves thermal conductivity with gains of 0.64% and 2.93% for 30% and 40% respectively, and it is also found that it reduces the rate of water absorption and porosity for the 10% replacement percentage and shows a reduction rate of 31.2% and 23.5% respectively for untreated specimens and 34.4% and 24.5% for treated specimens.
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 | 0.000 | 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) | 0.001 | 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".