Influence of the Mixing Energy Consumption Affecting Coagulation and Floc Aggregation
Bibliographic record
Abstract
Abstract The operational significance of energy-intensive rapid mixing processes remains unaddressed in coagulation and flocculation of insoluble precipitates (flocs), which play an important role in the removal of impurities from drinking water supplies. In this study, the influence of rapid mixing and associated mixing energy on floc aggregation was examined for a surface water source characterized by a high fraction of aquatic humic matter. Infrared spectral analyses showed that the colloidal complexes resulting from ligand exchange between iron and dissolved natural organic matter (DOM) were not substantially influenced by the mixing energy input. This signified that DOM removal by coagulation can be achieved at lower mixing intensity, thereby reducing energy consumption. In contrast, macroscopic investigations showed the coagulation mixing energy affected floc size distributions during the slow mixing stage in flocculation and, to some extent, their settling characteristics. The results from analysis of floc properties clearly showed that more mixing energy was expended than necessary in coagulation, which is typically designed at a high mixing intensity range of 600–1000 s–1 in treatment plants. The key findings from this study have practical implications to water utilities to strategically meet water quality goals while reducing energy demands.
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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 | 0.000 | 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".