Selecting Water Disinfection Processes using Fuzzy Synthetic Evaluation Technique
Bibliographic record
Abstract
Abstract Drinking water treatment strategies generally involve treatment processes such as screening, coagulation/flocculation, sedimentation, and filtration/adsorption followed by disinfection. Disinfection approaches include chlorine/chlorine (chloramine), granular activated carbon with post chlorine (chloramine), ozone/chlorine (chloramine), chlorine dioxide/ chlorine (chloramine), chloramine/chloramine, and ultraviolet radiation/chlorine (chloramine). However, comparative evaluation of these disinfection methodologies and their application to a particular source of water is rare. In this study, a framework for multicriteria decision making has been developed. Human health risk, cost, technical feasibility, and disinfection performance have been incorporated as the criteria for evaluation of the disinfection approach. A fuzzy synthetic evaluation technique has been incorporated where fuzzy triangular membership functions were developed to capture the uncertainties of the basic attributes. This paper compares three disinfection approaches: chlorination, granular activated carbon with post chlorination, and chloramination through a multistage hierarchy risk management model in which the analytical hierarchy process has been used to determine the relative importance of various attributes at different hierarchy levels. The evaluation process was found to be sensitive to the assignment of relative importance of the attributes. Chlorination was evaluated as the best disinfection approach in most of the cases.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".