Combining LC‐OCD analysis with design‐of‐experiments methods to optimize an advanced oxidation process for the treatment of industrial wastewater
Bibliographic record
Abstract
Abstract Advanced oxidation (AO) is widely used as a pre‐treatment and/or polishing step for the treatment of wastewater from industrial processes and the destruction of particular contaminants in water sources. It has a high treatment efficacy for many different compounds and thus is ideally suited as a treatment technology for specialized facilities that receive shipments of wastewater from networks of industrial, manufacturing, and commercial facilities. The primary challenge is how to optimize the process because bulk measurements of organic content (e.g. TOC) give no information about the specific composition and specialized advanced analytical techniques (e.g. LC‐MS) are unsuitable due to the complex composition. In this study, a novel combination of design‐of‐experiments (DOE) methods and LC‐OCD analysis was used with actual wastewater samples in order to optimize the AO treatment conditions in terms of chemical reagent concentrations, develop statistical models of the process, and identify potential mechanisms of COD removal. A significant variation in organic content removal was obtained over the range of conditions tested in the DOE method. For example, the percent removal of organic contaminants in the one wastewater sample varied from a low of 36 % to a high of 82 %. Most importantly, it was found that the treatment performance differed quite significantly for wastewater samples of different composition. The results presented in our study prove the need to dynamically optimize the AO treatment conditions for wastewater sources of different origins. Furthermore, by the application of the LC‐OCD analysis a step‐by‐step mechanism of COD removal was postulated.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".