Bibliographic record
Abstract
Management of wastewater treatment plants has become a major environmental and economic concern in North America because of the unprecedented deterioration of these facilities. This situation is aggravated by the lack of adequate funds for upgrading and maintenance. In 2008, Statistics Canada estimated that wastewater treatment assets have exceeded 63% of their useful life, the highest level among public infrastructure facilities. Similar studies in the United States found current wastewater treatment facilities with a near-failure average grade of D-. These facts show the urgent need for rehabilitation decision tools to keep these facilities running effectively. This dissertation aims to respond to such a pressing need by developing a performance assessment model (PAM) for the maintenance and rehabilitation of wastewater treatment plants (WWTPs) that depends on various treatment and infrastructure aspects. \nThe developed PAM is based on the evaluation of both treatment and infrastructure performance for the main treatment phases of a WWTP. The treatment performance of each phase is based on efficiency of treatment and robustness of its parameters as set by design standards. The infrastructure performance of each treatment phase is determined using infrastructure condition rating models developed by integrating the multi-attribute utility theory (MAUT) and the analytic hierarchy process (AHP).The required data for these models were collected via questionnaires from, site visits to, and interviews with experts in Canada and the United States. The results reveal that physical factors have the highest impact on deterioration of WWTP infrastructure and that pumps are the most vulnerable infrastructure unit. Deterioration curves for different infrastructure units in a WWTP are generated using sensitivity analysis, which shows the effect of age over their condition rating indexes. The treatment and infrastructure performance indexes are merged and presented in a combined condition rating index. Integer programming approach is used to optimize the rehabilitation interventions within available budget constraints with a minimum desired condition rating for each infrastructure unit. \nThe developed PAM is validated using data of three WWTPs from Canada and the United States. Managers of these WWTPs acknowledged the efficacy of the developed model outputs and deemed it systematic, straightforward, and valuable for clearly pinpointing the main problems in these WWTPs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".