Electrical Energy Consumption of Multiscale UV-AOP Reactors for Micropollutant Removal in Drinking Water: Facilitated Prediction by Reaction Rate Constants Measured on a Mini-Fluidic Photoreaction System
Bibliographic record
Abstract
Electrical energy consumption per order ( E EO ) is an important figure-of-merit for the selection and optimization of ultraviolet (UV)-based advanced oxidation processes (UV-AOPs). However, E EO applications are limited by the lack of an accurate and facilitative evaluation method because E EO presents reactor property dependence. In this study, we developed an E EO prediction method for multiscale UV-AOP reactors for micropollutant removal in water. The method utilized the reaction rate constants determined in a reference reactor (e.g., mini-fluidic photoreaction system), complemented by a scale-up method that clarified the dependence of E EO on reactor properties. The predicted results of various UV-AOPs were verified experimentally in four bench/pilot-scale reactors in laboratory and a full-scale flow-through reactor (FFR) in field using sulfamethazine as a model micropollutant. For example, E EO values of 0.105 and 0.058 kWh m –3 order –1 were predicted in the FFR at H 2 O 2 doses of 5 and 10 mg L –1, respectively, which generally agreed with verification results. Additionally, the developed method could assist the identification of appropriate reactors in the laboratory for E EO measurements, providing a valuable supplement for the E EO prediction in practice. The developed method presents acceptable accuracy, convenience, and low cost, which would significantly facilitate E EO evaluations for practical UV-AOP applications.
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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 | 0.001 | 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 | 0.001 |
| 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".