MétaCan
Menu
Back to cohort
Record W2890719566

Benchmarking the Sustainability of Sludge Handling Systems in Small Wastewater Treatment Plants in Ontario

2018· dissertation· en· W2890719566 on OpenAlexfundaboutno aff
Greggory Archer

Bibliographic record

VenueUWSpace (University of Waterloo) · 2018
Typedissertation
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsnot available
FundersMinistry of EnvironmentOntario Water Consortium
KeywordsBenchmarkingSustainabilitySewage treatmentWaste managementEnvironmental scienceWastewaterEnvironmental engineeringEnvironmental planningBusinessEngineeringEcologyBiology
DOInot available

Abstract

fetched live from OpenAlex

This project quantitatively benchmarked all aspects of sludge handling in a cross-section of small wastewater treatment plants across Ontario. Using plant operational data and on-site measurements, a variety of sustainability metrics were evaluated: energy consumption, chemical use, biosolids disposition, biosolids quality, and greenhouse gas emissions. In addition, a desktop analysis was conducted to determine the sustainability impact of incorporating innovative technologies into facilities with conventional processes. Parameters from select new technologies within the study sample were applied to plants within the sample that employed conventional processes, and the impact on greenhouse gas (GHG) emissions was calculated. Overall electricity consumption for sludge handling ranged from 0.9 – 3.9 kWh per dry kg of raw sludge. The thermo-alkali hydrolysis and auto-thermal thermophilic aerobic digestion (ATAD) processes consumed the least (0.3 kWh/dry kg) and most (3.8 kWh/dry kg) amount of electricity for stabilization, respectively. Mechanical dewatering processes consumed minor amounts of electricity (2 – 5% of total sludge handling draw), however, associated polymer dosages were found to be higher than literature values in some cases. The disposition fuel requirements for plants with dewatering were up to 85% lower than facilities without dewatering. Biosolids contaminant (pathogen/metals) contents were observed to be substantially below Non-Agricultural Source Material (NASM) requirements. The copper content of the hauled biosolids exhibited the highest concentration relative to the NASM limit among all plants studied, ranging from 14 – 37% among facilities practicing land application of biosolids. Four plants generated a product that met Class A requirements for 𝐸. 𝑐𝑜𝑙𝑖 content, including one facility that generated it through a long-term storage approach (GeoTube™). Carbon emissions ranged from -119 to 299 kg CO2 equivalents per dry tonne of raw sludge. Six of the eight facilities that practiced land application of biosolids exhibited net-negative GHG emissions, as the carbon credits gained from fertilizer production avoidance outweighed the emissions associated with sludge processing and transportation operations. Of these six plants, five employed sludge treatment configurations that are common in Ontario. Given that land application is the most common disposal practice among small treatment plants in Ontario, the findings indicate that current conventional practices can be sustainable with respect to GHG emissions. The innovative technology assessment revealed that existing trucking requirements and polymer dosage are the primary factors that determine whether new technology implementation would improve environmental sustainability. The benchmarking approach developed and information gathered is of value to plant owners and operators who seek to better understand how their utility is performing relative to peers, identify areas of need and further investigation, and improve the long-term sustainability of their operations.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.830

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.189
Teacher spread0.177 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueUWSpace (University of Waterloo)Same topicWastewater Treatment and ReuseFrench-language works237,207