Freeze-Thaw Treatment of Membrane Concentrates Derived from Kraft Pulp Mill Operations
Bibliographic record
Abstract
Freeze thaw was studied as a waste treatment method for concentration and volume reduction of contaminated waste concentrates that are derived from the use of membrane technology in the treatment of high strength Kraft pulp mill effluents. Unidirectional freezing experiments were conducted to simulate seminatural freezing, in which the independent variables—freezing rate, time frozen, storage temperature, concentration, liquid depth, thawing rate and method of thawing—were examined for their relative importance. Method of thawing followed by freezing rate, rate of thawing, storage temperature, and time frozen were identified as the most important independent variables that contribute significantly to treatment performance. Under ideal conditions, freeze thaw was shown to effectively concentrate and separate the constituent matter of alkaline, extraction-stage membrane concentrate to achieve color removals as high as 73% in the top 70% liquid fraction. The results suggest a new field of use for freeze thaw as a waste treatment process for the management of high strength liquid wastes amenable to mechanical coagulation by freezing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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 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".