Application of mass balance models in the process of ozone removal of naphthenic acids from water
Bibliographic record
Abstract
Abstract The oil sands process‐affected water (OSPW), produced in large amounts during the extraction of crude oil from oil sands, is toxic mainly due to the presence of naphthenic acids (NAs), among other constituents. Ozonation is an effective method for the removal of NAs. To provide information for better control of NAs ozonation, models based on mass balance were developed in this study to predict the concentration profiles of commercial NAs, dissolved ozone, and gaseous ozone during the process. It was found that the developed models successfully predicted the concentrations of commercial NAs and gaseous ozone. For the dissolved ozone concentration, the developed model can predict the equilibrium concentrations well. However, the actual ozone consumption was higher than that predicted by the model at the initial period of the ozonation process. This deviation possibly resulted from the inapplicability of the gas‐liquid equilibrium conditions at the initial high rate stage of the reaction. In addition, the Henry's law constant and the overall mass transfer coefficient for the given system were determined experimentally. Also investigated in this study was the effects of inlet gaseous ozone concentration into the reactor on the removal of NAs and on the concentration of dissolved ozone. As expected, the increase in inlet ozone concentration enhanced the removal of NAs and increased the concentration of dissolved ozone.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".