Estimation of Bench-Scale Chlorine Decay in Drinking Water Using nth-Order Kinetic and Back Propagation Neural Network Models
Bibliographic record
Abstract
Abstract This paper presents the development of two models for simulating residual chlorine decay in raw and treated waters collected from six different utilities of the Quebec City area. The data for modelling was generated by means of several bench-scale chlorination assays undertaken with chlorine doses that varied according to the content of natural organic matter in the water. The first model is a classical kinetic model in which chlorine decay is represented by a first or a second order function, according to the contact time of chlorine. A decay coefficient is estimated based on water quality and operational parameters, using linear regression. The second is a non-linear back propagation neural network model in which all the parameters responsible for chlorine decay are processed within a single model. The performances of both models were evaluated and compared using the database for model development and an independent database for validation. Both models demonstrated acceptable abilities for simulating residual chlorine decay. However, the back propagation neural network model gave significantly better results for conditions involving high chlorine dosage, high organic matter content and long reaction times during chlorination experiments. In the other cases, the performance of the kinetic model was slightly superior. Back propagation neural networks are also advantageous because they do not require assumptions about model order or the estimation of a chlorine decay coefficient. Also included in the paper is a discussion of possible strategies for use in future research work in order to generalize the results obtained.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".