Analysis of Packed Bed Adsorption Column with Low Cost Adsorbent for Cadmium Removal
Bibliographic record
Abstract
In the current research, removal of cadmium from the effluent by using rice husk adsorbent is studied in packed bed with respect to various parameters like initial concentration, adsorbent dose, bed height and pH. Also the solute uptake data was analyzed for first and second order kinetic equations by varying these parameters. The effect of these parameters on kinetics of solute uptake is reported. It was observed that with increase in initial concentration from 10 mg/l to 50 mg/l, the exhaustion time decreased from 830 mg/l to 570 mg/l and break point time decreased from 330 minutes to 120 minutes. Also with increase in flow rate, exhaust time and break through time decreased significantly. The alkaline conditions favoured the adsorption with optimum pH of 6.The exhaustion and break through delayed with increasing bed height. The kinetics of the cadmium removal was affected by initial concentration and flow rate. At low initial concentration, it indicated second order kinetics. There was increasing shift from second to first order with increase in initial concentration. With increase in bed height from 30 to 40 cm there was shift from second to first order but with further increase in bed height, again shift towards second order kinetics was observed. The optimum bed height, initial concentration, flow rate and pH values were 50 cm,30 mg/l, 60 ml/min and 6 respectively. The adsorption efficiency was estimated to be 69 percent for optimum values of pH, initial concentration, bed height and flow rate.
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) | 0.001 | 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".