Reuse Feasibility of Electrocoagulated Metal Hydroxide Sludge of Textile Industry in the Manufacturing of Building Blocks
Bibliographic record
Abstract
During the last decade, the growing load of sludge from textile industries, the top foreign exchange earning sector of Bangladesh, is a common nuisance to environmental system and community health. The present study was aimed to minimize the environmental impact from the disposal of Electrocoagulated Metal Hydroxide Sludge (EMHS) by using it as a partial substitute of clay in the manufacturing of construction material like building blocks (BBs). Different batches of normal and pressurized building blocks (NBBs and PBBs, resp.) were prepared using up to 50% EMHS with clay and then fired at a particular temperature. EMHS proportion in the mixture and firing temperature were two key factors determining the quality of BB. BB did not show any deformation or uneven surfaces at any of the examined firing temperature. At higher firing temperature and EMHS proportion, more weight loss and shrinkage of BB were noticed. Higher compressive strength and lower water adsorption were found at lower EMHS content and higher firing temperature. It was explored that NBB and PBB with 20 and 30% EMHS in clay, respectively, and fired at 1050 °C would be usable for nonloading applications; namely, ornamental bricks, decoration purposes, and fence of garden.
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) | 0.000 | 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 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".