Energy and reliability analysis of wastewater treatment plants in small communities in Ontario
Bibliographic record
Abstract
/d. Five different treatment technologies were investigated, namely, rotating biological contactor (RBC), sequencing batch reactor (SBR), membrane bioreactor (MBR), lagoon, and extended aeration activated sludge process (EAAS). Energy benchmarking based on key performance indicators (KPIs) was used to quantify the specific consumption of energy in WWTPs per unit of the pollutant removed. The overall annual electrical energy consumption was correlated to the volume of treated wastewater, the population equivalent, and the amounts of TSS and BOD removed. The RBC plants showed a distinctive advantage for all energy KPIs assessed, while SBR plants yielded the highest values of energy KPIs. Analyses of the expected percentage of compliance with discharge standards and the coefficient of reliability (COR) based on the WWTPs' performance records showed that few WWTPs were able to achieve reliability levels over 95%, considering the mandated discharge standards under the current operating and maintenance conditions. Within each technology, the treatment train, operating conditions, maintenance level, and age of infrastructure were important elements that contributed to the large variability observed.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".