Novel approaches for predicting efficiency in helically coiled tube flocculators using regression models and artificial neural networks
Bibliographic record
Abstract
Abstract In this paper, prediction models for turbidity removal efficiency (TRE) in helically coiled tube flocculators (HCTFs) are presented. The TRE was determined by physically modelling a compact, high‐performance and low detention time clarification system composed of a HCTF coupled to a decantation system. The values of hydrodynamic representative parameters of the flow were determined by CFD modelling. Eighty‐four different configurations of HCTFs were evaluated. Multiple linear/non‐linear regression and artificial neural network analyses were performed. A determination coefficient ( R 2 ) of 0.81 was obtained using multiple linear regression with the geometric and hydraulic parameters. In this model, the root mean squared error (RMSE) was 3.29%. Adding hydrodynamic parameters and using the artificial neural networks, R 2 reaches 0.96 and RMSE decay to 1.58%. These results indicate that the use of effective efficiency prediction models can be helpful in the design of new flocculation units and for the improvement of existing ones.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".