Advancing the Treatment of Industrial Wastewater via Integration of PeCOD® And LC-OCD Analytical Tools
Bibliographic record
Abstract
In 2012, mandatory effluent quality standards were established in Canada as part of the Wastewater Systems Effluent Regulations (WSER) with compliance deadlines starting in 2020. Maintaining the treatment process efficacy to meet these new stringent discharge regulations is extremely challenging at treatment facilities that treat wastewater from multiple industries due to the high variation in the composition of the incoming feed to the process. In this work, application of two new analytical tools, PeCOD® and Liquid Chromatography-Organic Carbon Detection (LC-OCD), for measurement and characterization of industrial wastewater organic pollution respectively, has been investigated. Organic pollution is commonly measured as Chemical Oxygen Demand via the dichromate method (CODCr) which requires 2-3 hours to complete. Thus this method is not suitable for applications that require rapid and frequent pollution monitoring. The Photoelectrochemical Oxygen Demand (peCOD) is an alternative parameter of organic pollution that can be measured in approximately 15 minutes via a method that utilizes the high oxidation potential of UV-irradiated TiO2 nano-particulates. Herein peCOD suitability to replace CODCr for analysis of industrial wastewater was investigated. The results indicated that for both untreated (i.e. incoming) and treated (i.e. effluent) industrial wastewater samples, peCOD results are lower than CODCr results. However, for the effluent samples, the two methods’ results are strongly correlated. Containing hard to oxidize materials (i.e. macromolecules) and high concentrations of chloride and nitrogenous compounds were identified as potential causes of difference between the results of the two methods. When there is variation in the composition of the incoming wastewater to a treatment process, information about the wastewater composition is required for process optimization. Thus optimization cannot be based solely on bulk measurements of organic pollution (e.g. COD). In this study, a novel combination of LC-OCD analysis with Design-Of-Experiments (DOE) methods was used to optimize the Fenton Advanced Oxidation (AO) treatment conditions in terms of chemical reagent concentrations, to develop statistical models of the process, and to identify potential mechanisms of COD removal.
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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) | 0.008 | 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".