The study of optimal ozone dose for industrial ozone installation in textile wastewater reuse
Bibliographic record
Abstract
ABSTRACT Textile production is one of the most water-consuming industries. Closing the water loop by treatment and recycling is highly desirable in this regard. However, textile wastewater treatment is not standard on an industrial scale, and the Polish textile factory Bilinski is one of very few with a functional wastewater recycling system. This study investigates the operational conditions of the industrial ozone system at Bilinski Co., (Konstantynow Lodzki, Poland). An ozone reaction column from Thies GmbH (Germany) with a volume of 7 m 3 was used to determine the optimal ozone dose in a closed water loop for textile wastewater reuse. An ozone measurement system from BMT Messtechnik GmbH (Germany), a 965OG ozone concentration metre, and a DH7 dehumidifier were used to determine the ozone in the gas phase. The applied ozone dose and transferred ozone dose (TOD) were calculated based on these data. Three values of TOD, 62.9, 37.7, and 27.0 g/m 3 , were used for wastewater. A colour reduction of 97% was achieved after 8, 9, and 11 min of treatment. The test showed that the higher the TOD was, the shorter the treatment. Consequently, the average optimal ozone concentration was 32.4 ± 5.5 g/m 3 . However, this value was roughly estimated because of the industrial scale of the process. It can be assumed that after transferring this ozone concentration, 97% colour removal is possible. Finally, the faster the optimal ozone concentration was transferred, the shorter the treatment time. The experiment showed how operational conditions could be investigated in a high-volume industrial system.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".