Integration of Advanced Oxidation Technologies and Biological Processes: Recent Developments, Trends, and Advances
Bibliographic record
Abstract
The greatest challenge of today's wastewater treatment technology is to optimize the use of biological and chemical wastewater treatment processes. The choice of the process and/or integration of the processes depend strongly on the wastewater characteristics, concentrations, and the desired efficiencies. It has been observed by many investigators that the coupling of a bioreactor and advanced oxidation processes (AOPs) could reduce the final concentrations of the effluent to the desired values. However, optimizing the total cost of the treatment is a challenge, as AOPs are much more expensive than biological processes alone. Therefore, an appropriate design should not only consider the ability of this coupling to reduce the concentration of organic pollutants, but also try to obtain the desired results in a cost effective process. To consider the total cost of the treatment, the residence time in biological and photochemical reactors, the kinetic rates, and the capital and operating costs of the reactors play significant roles. In this study, recent developments and trends (1996-2003) on the integration of photochemical and biological processes for the degradation of problematic pollutants in wastewater have been reviewed. The conditions to get the optimum results from this integration have also been considered. In most of the studies, it has been shown that the integrated processes were more efficient than individual processes. However, slight changes in the configuration of the reactors, temperature, pH, treatment time, concentration of the oxidants, and microorganism's colonies could lead to a great deviation in results. It has also been demonstrated that the treatment cost in both reactors is a function of time, which changes by the flow rate. The minimum cost in the coupling of the processes cannot be achieved unless considering the best treatment time in chemical and biological reactors individually.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".