SB-MBBR Technology: Treatment Strategies for Nitrification in Elevated Carbon Concentration Industrial Wastewater
Bibliographic record
Abstract
The dairy processing industry, in specific cheese processing, is a growing sector of the economy world wide.For example, China's demand for milk products projected an increase of 3.2-fold by 2050.While domestically in Canada, an annual increase of 5% has been reported since 2015 projected to continue to 2018.An increase in the demand for milk and dairy products translates to an increase in the produced wastewater.For every 2 L of milk processed 5 L of wastewater is produced, meanwhile, for every 1kg of cheese processed 9 kg of cheese whey is produced requiring the use of an estimated 10 kg of milk.Cheese whey is a by-product generated during the cheese manufacturing processes, with cheese processing wastewater defined as a combined stream of all wastewaters from a cheese processing facility.Cheese processing wastewater can be characterized as medium to high strength wastewater with COD concentrations of above 1000 mg•L -1 .In specific, cheese effluent can display COD concentrations in the range of 2500-5000 mg•L -1 and ammonia concentration in the ranges from 100-140 mg•L -1 -N, and total phosphorus is in the range of 50-60 mg•L -1 -P.Due to the high concentrations of carbon and nutrients in cheese processing wastewater, current stringent provincial regulations applied to municipal wastewater treatment plants restrict the discharge to sewers without prior treatment; leading to an increased demand for decentralized treatment units.Decentralized treatment units like sequencing batch reactors (SBR) gained popularity in cheese wastewater treatment due to their simple construct, reduced building expense and smaller footprint compared to conventional systems.The SBR however, has the potential of discharging floating or settled sludge during the draw or decant stages.The aerobic granular SBRs (AG-SBRs) promoted the formation of granules decreasing the potential of discharge.However, AG-SBRs were found to have problems regarding granular formation.The larger granules in an AG-SBR tended to of you have been a constant source of knowledge and inspiration.I am extremely grateful for all your support and encouragement.Throughout this degree, I found that I gained new knowledge and skills made possible through your patronage.I would also like to thank Neda Arabgol, Kellie Boyle, Maha Dabbas, and Patrick Daoust, for making me feel welcome at the University of Ottawa.Your constant support and friendship made it possible for me to finish this thesis.I would also like to extend a thank you for Alexandra Tsitouras for the usage of her data, and Rochelle Mathews for helping conduct some of
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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) | 0.000 | 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 | 0.001 |
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".