A study on ozonation and ozone based advanced oxidation processes for the removal of naphthenic acids from water: kinetic studies modelling and optimal control
Bibliographic record
Abstract
Industrial wastewater is one of the largest environmental challenges of this century. Most of these wastewaters contain non-biodegradable pollutants which need special treatment methods. Advanced oxidation processes (AOP’s), such as, ozonation, catalytic ozonation and ozone/ hydrogen peroxide have proved their effectiveness on the degradation of bio-recalcitrant pollutants. The main drawback in these processes is the high operating cost. The objective of this study was to develop innovative continuous ozonation and ozone based processes that can effectively degrade industrial non-biodegradable pollutants. Naphthenic acids (NAs) was used as the model pollutant in this study due to its importance as a major pollutant in oil and oil sands industries. The target was to convert bio-recalcitrant NAs into biodegradable substances with minimum consumption of ozone gas (operating cost). These processes can be followed by the biodegradation process to fully remove the rest of the pollutants. This research passed through several stages including screening of operating parameters, kinetic studies, and modeling, followed by optimal control of these processes. It was found that ozone concentration had the most significant effect on the NAs degradation compared to other parameters. The kinetics of direct and indirect (radical) ozonation of NAs were investigated and rate constants and activation energies of these reactions were determined. Catalytic ozonation of NAs was explored using alumina supported metal oxides and unsupported catalysts. Activated carbon was found to be the most effective catalyst. The addition of hydrogen peroxide into the ozonation systems significantly improved the removal of NAs compared with the ozonation only process. Models based on mass balance for the ozonation and ozone/ hydrogen peroxide processes were developed to predict the concentration profiles of reacting species. Optimal control policies of ozone/oxygen gas flow rate versus time were developed and validated to minimize NAs concentration in the liquid outlet stream from the continuous ozonation and ozone/ hydrogen peroxide processes. The experimental results demonstrated that the optimal control policies successfully minimized NAs concentration in the outlet stream. At the same time, ozone gas consumption was reduced to its minimum, i.e., just enough to minimize the concentration of NAs in the outlet stream.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".