Adsorption of a Multicomponent Pharmaceutical Wastewater on Charcoal-Based Activated Carbon: Equilibrium and Kinetics
Bibliographic record
Abstract
The treatment of pharmaceutical wastewater is a critical environmental challenge, necessitating efficient removal methods. This study investigates the adsorption of a synthetic multicomponent pharmaceutical wastewater (SPWW) containing methanol, benzene, methylene chloride, 4-aminophenol, aniline, and sulfanilic acid onto charcoal-based activated carbon (AC). Batch experiments were conducted to study the effects of pH, contact time, and initial concentrations of the adsorbates. The results show that longer contact time and higher initial concentrations increase the adsorption capacity, whereas pH shows no significant effect on the adsorption capacity at a value of less than 10, eliminating the need for pH adjustment and reducing process costs. The pseudo-second order (PSO) kinetic model best describes the adsorption process, with intraparticle diffusion playing a key role, as confirmed by the Weber and Morris (W-M) model. Six models describing the adsorption at equilibrium are applied to experimental data, and their parameters are estimated with a nonlinear regression model. Among isotherm models, the Langmuir-Freundlich model provides the best fit, suggesting multilayer adsorption on a heterogeneous granular activated carbon (GAC) surface. The maximum adsorption capacity is estimated to be 522.3 mgC/gAC. Experimental results confirm that GAC could effectively treat highly concentrated pharmaceutical wastewater, achieving up to 52% removal efficiency.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".