Innovation Portfolio Management for Retrofitting Advanced Biotechnologies for Anaerobic Digestion Process at Lulu Island Wastewater Treatment Plant
Bibliographic record
Abstract
Innovation Portfolio Management for Retrofitting Advanced Biotechnologies for Anaerobic Digestion Process at Lulu Island Wastewater Treatment PlantAbstractMetro Vancouver recently commissioned the Pilot Digestion Optimization Facility (PDOF) at its Lulu Island Wastewater Treatment Plant (LI WWTP) to facilitate the evaluation of different digestion optimization technologies without risk to full-scale plant operation. This study summarizes the integrated approach adopted at the pilot-scale preliminary design stage for two of these technologies. The first technology aims to increase biogas production rates by testing the Direct Interspecies Electron Transfer (DIET) process using electroconductive materials. The second technology utilizes a biological hydrogen methanation (BHM) process to convert the carbon dioxide portion of biogas into renewable natural gas (RNG) using hydrogen. Cost-effective pilot-scale conceptual designs are developed for testing the in-situ and ex-situ configurations of these technologies in order to reach a go/no-go decision quickly, while still producing replicable results for further scale-up. To guide the decision-making process for selecting these technologies for pilot testing, GHG emission reduction calculations and an energy balance model are used to evaluate the environmental impacts of implementing these technologies at full-scale. A Sumo model is used to evaluate the effects of implementing these technologies on process intensification.Metro Vancouver, in collaboration with its partners, is developing innovative technologies that reduce greenhouse gas emissions and intensify existing digestion processes. Metro Vancouver will first assess the effectiveness and scalability of these technologies at the Lulu Island Pilot Digestion Optimization Facility. Metro Vancouver intends to incorporate these technologies into one of its treatment facilities if the technologies prove successful at pilot-scale.SpeakerChegounian, ParisaPresentation time15:30:0015:50:00Session time15:30:0017:00:00SessionImproving Biosolids Treatment EfficiencySession locationRoom S403b - Level 4TopicEnergy Production, Conservation, and Management, Intermediate Level, Research and InnovationTopicEnergy Production, Conservation, and Management, Intermediate Level, Research and InnovationAuthor(s)Kakar, Farokh LaqaAuthor(s)F. Kakar 1; C. Muller 2 ; T. Barber 3; F. Kakar 1; P. Chegounian 4; L. Zaremba 4; T. Gregonia 4; P. Kadota 4; S. Sorensen 4; A. Al-Omari 5;Author affiliation(s)Brown and Caldwell 1; Brown and Caldwell 2 ; Brown and Caldwell 3; Brown and Caldwell 1; Liquid Waste Services Department, Metro Vancouver, Burnaby, BC, Canada 4; Liquid Waste Services Department, Metro Vancouver, Burnaby, BC, Canada 4; Liquid Waste Services Department, Metro Vancouver, Burnaby, BC, Canada 4; Liquid Waste Services Department, Metro Vancouver, Burnaby, BC, Canada 4; Liquid Waste Services Department 4; Brown and Caldwell 5;SourceProceedings of the Water Environment FederationDocument typeConference PaperPublisherWater Environment FederationPrint publication date Oct 2023DOI10.2175/193864718825159034Volume / 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".