Фізико-механічні властивості керамічної цегли з використанням рисового лушпиння і золи теплоелектростанцій
Bibliographic record
Abstract
The paper presents the results of the influence on shrinkage deformations of the adopted composition during the drying and firing of ceramic bricks made using rice husk and ash of the combined heat and power plant of the city of Kyzylorda of the Republic of Kazakhstan. The optimal values of the husk additives content and ash from thermal power plants in the studied compositions were determined. Ash dumps from thermal power plants (TPP) create environmental tension and pose a great threat to both the environment and human health. It was found that the hydro-removal ash from the thermal power plant mainly consists of oxides of silica (45.45…46.37 %) and alumina (16.62…17.70 %), there are oxides of calcium (1.66…2.20 %), magnesium (0.86…1.12 %), iron (2.98…3.41 %) and alkali metals (0.80…1.04 %). The composition of ceramic bricks based on loess-like loam, rice husks, and ash from thermal power plants was studied. The charge composition of the raw components of the “clay, TPP ash, and rice husk” brick: clay is 71…75 %, TPP ash is 18…22 %, and rice husk is 2…6 % of the total mass of the components of the raw mixture of ceramic bricks. The compressive strength of fired ceramic bricks was 11…12 MPa. According to the results of experimental studies, it was found that the increased concentration of rice husks in natural mixtures is characterized by a stable increase in ceramic mass drying cracks. The increase in time until the appearance of drying cracks is 100 up to 160 sec. The resulting ceramic brick in accordance with the developed composition has a low weight, good thermal properties and meets the standard requirements for ceramic bricks according to GOST 530-2012
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".