Removal of Some Heavy Metals from Incinerator Bottom Ash Using Coconut (Cocos Nucifera) Husk
Bibliographic record
Abstract
There has been a great limitation to the use of incinerator bottom ash (IBA) residue in the construction and manufacturing industries due to the high contents of heavy metals, which poses great threat to the environment and human health. This study was carried out to evaluate the efficiency of heavy metals (Fe, Zn, Cd and Pb) removal from domestic IBA filtrate using a biological material coconut (Cocos nucifera) husk as adsorbent and to remove the metals from the ash residue using the toxicity characteristic leaching procedure (TCLP). The mean percentage removal of the heavy metals for the incinerator bottom ash (IBA) filtrate varied from 75.84 - 99.42% for the two ash types. Similarly, the percentage removal of metals from the ash residues was between 92.34 and 99.97%. The data from this study showed that after the treatment, the heavy metals in the ash filtrate and residue were significantly reduced. Thus, it would be feasible to use the IBA residue as a partial replacement of cement in the construction and manufacturing industries for making monolithic blocks, interlocking bricks, concretes and road construction materials. This would help improve and provide good and effective waste management practice that would reduce the huge volume of ash residue from incineration combustion processes.
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".