Efficient mineralization of sugar industry wastewater by catalytic wet air oxidation as an eco‐friendly method and its kinetic modelling
Bibliographic record
Abstract
Abstract In this study, catalytic wet air oxidation using lanthanum cobalt oxide (LaCoO 3 ) as catalyst was employed for the efficient treatment of synthetic sugar industry wastewater in a single process. A parametric study was performed to determine the optimum conditions. The results showed that reaction temperature and theoretical air percentage were the most effective parameters. Sugar derivatives were almost completely destroyed at the optimum conditions and total organic carbon (TOC) and chemical oxygen demand (COD) removals were determined as 91% and 87%, respectively, indicating a high mineralization degree, which was the main goal of advanced oxidation. The reaction kinetics were investigated by pseudo‐homogeneous and heterogeneous models based on two different parameters: sucrose and TOC concentration. The degree of fit showed that the reaction order was determined as two for the pseudo‐homogeneous approach. According to the surface concentration calculations for heterogeneous models, the presence of mass transfer limitations was only observed for oxygen as gas reactant. All heterogeneous models also fitted the reaction rate data accurately, but the Mars–van Krevelen was the selected model for sucrose and TOC oxidation with the best fit.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".