The Use of Risk Analysis Techniques to Determine the\nProbability of Producing Non-Compliant Drinking\nWater: Focusing on Dual Media Rapid Gravity\nFiltration
Bibliographic record
Abstract
The main goal of a drinking water treatment plant is to provide safe drinking water for its consumers. Historically, this was accomplished through monitoring the influent and effluent water quality to ensure that the water quality met a set of guidelines and regulations. However, as the limitations of relying on compliance monitoring become more evident, water utilities and drinking water treatment plants are beginning to utilize risk management frameworks to help provide safe drinking water and to mitigate potential risks. Applying a risk management framework requires an evaluation of potential risks. This systematic evaluation can be performed through using risk analysis methods. <br /><br /> \n The overall goal of this research is to analyze and evaluate risk analysis methodologies that are used in a variety of engineering fields, select two risk analysis methods, and use them to evaluate the probability of producing non-compliant drinking water from a rapid gravity filtration unit with respect to turbidity. <br /><br /> \n The risk analysis methodologies that were used in this research were the consequence frequency assessment and computer modelling combined with probabilistic risk analysis. Both of the risk analysis methodologies were able to determine the probability of producing non-compliant water from a rapid gravity filtration unit with respect to turbidity. However, these methodologies were found to provide different numerical results with respect to each other. The consequence frequency assessment methodology was found to be easier to implement; however, the consequence frequency assessment was only able to be performed on one parameter at a time. Computer modelling and probabilistic risk analysis enabled the inclusion of multiple parameters which provided a more comprehensive understanding of the filtration unit. <br /><br /> \n The primary conclusion from this research is that the risk analysis methods, as they are described in this thesis, are not sufficient to use directly on a rapid gravity filtration unit without further modification. Furthermore, although the risk analysis methods provided some guidance, these methods should only be used as a part of a complete risk management process.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".