FBR Technology: Its Potential Application on Reuse of Industrial Wastewater
Bibliographic record
Abstract
The treatment performance of the fluidized-bed Fenton process in terms of COD and color removal efficiency on a textile wastewater from a manufacturing facility in Southern Taiwan was evaluated as a case study for the potential application of fluidized-bed reactor (FBR) technology on reuse of industrial wastewater. Results showed that the effluent COD and color of the textile wastewater met the regulatory requirements of Taiwan when the following conditions were used in the treatment: concentration ratio COD:Fe 2+ :H 2 O 2 = 1:0.95:7.94, carrier = 74.07 g/l, initial pH = 3. The COD and color removal efficiency of the fluidized-bed Fenton process for synthetic commercial dyeing wastewater and actual textile wastewater were compared. At optimum pH =3, the fluidized-bed Fenton process can remove COD more easily from the commercial dye than from actual textile wastewater. In the case of color removal, the fluidized-bed Fenton had high removal efficiency. This study has shown that the fluidized-bed Fenton process can not only treat textile waste water to meet Taiwan’s regulatory limits for COD and color but also has the potential to be a technology on reuse of industrial wastewater
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".