Prediction of Metal Precipitates in Tannery Sludge Leachate Based on Thermodynamic Calculations
Bibliographic record
Abstract
Thermodynamic calculations were performed in order to predict the formation of metal precipitates during the tannery sludge leaching. Gibbs free energy deltaG=RTln(Qc/Kc) of precipitation reaction was used to examine the formation of precipitates during the leaching process. The values of activity equilibrium constant (Ka) of various precipitation reactions were adopted from the literature. The Ka values were corrected to obtain the corresponding values of concentration product (Qc) and concentration equilibrium constant (Kc) using the activity coefficients (gamma) of the ions. The activity coefficients (gamma) of the ions was calculated using Davies equation Lngammai=-1.172Zi2((I0.5/(1+I0.5))-0.3I) (aqueous solution, 25 degrees C, 1 atm, I>0.3 mol x l(-1)). The values of ionic strength (I) at different sludge solids concentration and leaching pH were obtained by measuring the concentration of all ionic species in the leachate. The thermodynamic calculations indicated that the possible metal precipitates formed during the leaching process were (am)Cr(OH)3, (am)CrPO4, (am)Fe(OH)3, alpha-FeOOH(goethite), FePO4.2H2O, (am)AIPO4-2H2O, CaSO4.2H2O(gypsum). The solubility of these precipitates was found to decrease with the increase in the ionic strength (or sludge solids concentration). The results of computations were supported by the experimental observations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".