Application of carbon dioxide stripping for struvite crystallization — I: Development of a carbon dioxide stripper model to predict CO<sub>2</sub> removal and pH changes
Bibliographic record
Abstract
This research investigated the feasibility of stripping CO 2 from the digester supernatant to raise the pH, thereby reducing the caustic chemical usage. In this study, a cascade CO 2 stripper was first designed and tested, with three different synthetic solutions in a struvite recovery, crystal reactor: (1) tap water saturated with CO 2 , (2) NaHCO 3 solution saturated with CO 2 , and (3) NaHCO 3 + NH 4 Cl solution saturated with CO 2 . It was found that the removal efficiency of the CO 2 stripper was dependant on several parameters, such as the characteristics of the influent, including total alkalinity, temperature, and initial concentration of dissolved CO 2 gas, influent flow rate, effluent recycle rate, aeration rate, and baffle numbers in the stripper. Based on the performance of the stripper on the three synthetic solutions, a CO 2 stripping model was developed using these parameters. This model was subsequently tested in a pilot-scale facility, to predict the amount of CO 2 removal possible.
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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 | 0.000 | 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 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".