Fermentation Intensification with Vacuum-Assisted Technology: An Evaluation of Full-Scale EBPR Alternatives Using a Calibrated Process Model Analysis
Bibliographic record
Abstract
Fermentation Intensification with Vacuum-Assisted Technology: An Evaluation of Full-Scale EBPR Alternatives Using a Calibrated Process Model AnalysisAbstractIntensiCarb® is a vacuum-based intensification technology that provides process benefits in fermentation or anaerobic digestion. Applying this technology to fermentation allows for half the process volume to achieve improved yield of volatile fatty acids (VFA) for beneficial use. Generating VFA from fermentation provides a benefit to facilities that need supplemental carbon for nutrient removal targets in the liquid treatment process. VFA can be used by facilities to improve enhanced biological phosphorus removal (EBPR) performance, lowering effluent total phosphorus (TP) concentrations. An analysis was completed to evaluate the process performance and life-cycle costs of IntensiCarb® relative to chemical addition and conventional fermentation alternatives for TP removal. Experimental results on vacuum-assisted process intensification were used in the process performance analysis and GHG emissions were estimated to compare alternatives. The evaluation suggests IntensiCarb® is competitive with other alternatives to lower effluent TP with EBPR. FeCl3 chemical addition had the lowest life-cycle cost overallIntensiCarb® is a vacuum-based intensification technology that provides process benefits in fermentation or anaerobic digestion. An analysis was completed to evaluate the performance and life-cycle costs of IntensiCarb® relative to chemical addition and conventional fermentation alternatives for the purposes of phosphorus removal. This paper describes bench-scale experimental results, presents model performance predictions, and compares life-cycle costs and greenhouse gas emission estimates.SpeakerArmenta, MaxwellPresentation time16:10:0016:30:00Session time15:30:0017:00:00SessionExtracting Carbon for Nutrient RemovalSession locationRoom S403a - Level 4TopicFacility Operations and Maintenance, Intermediate Level, Municipal Wastewater Treatment Design, NutrientsTopicFacility Operations and Maintenance, Intermediate Level, Municipal Wastewater Treatment Design, NutrientsAuthor(s)Armenta, MaxwellAuthor(s)M. Armenta 1; F. Kakar 2 ; A. Al-Omari 3; C. Muller 4; K. Bell 5; G. Nakhla 6; D. Santoro 7; J. Boone 7; M. Armenta 1; R. Coleman 8;Author affiliation(s)Brown and Caldwell 1; Brown and Caldwell 2 ; Brown and Caldwell 3; Brown and Caldwell 4; Brown and Caldwell 5; University of Western Ontario, London, ON 6; USP Technologies 7; PRAB, Kalamazoo, MI 7; Brown and Caldwell 1; Environmental Operating Solutions, Inc. 8;SourceProceedings of the Water Environment FederationDocument typeConference PaperPublisherWater Environment FederationPrint publication date Oct 2023DOI10.2175/193864718825159027Volume / Issue Content sourceWEFTECCopyright2023Word count18
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".