A comprehensive experimental and artificial network investigation of the performance of an ultrafiltration titanium dioxide ceramic membrane: application in produced water treatment
Bibliographic record
Abstract
Abstract This work is an experimental investigation of the effects of the operating conditions on the performance of a novel titanium‐based ceramic ultrafiltration membrane used to treat field produced water. To design the experiments and optimize the operating conditions, the Taguchi method was used to predict the optimal operating conditions. Under optimal conditions, an almost oil free permeation is obtained (98.93%) along with the removal of more than 99% for the total organic carbon (TOC), a high turbidity removal (99.82%) and a good salinity rejection for a UF membrane. The membrane was capable of treating a high steady flux of 441 L/m 2 h with an overall flux decay of 28.6%. The Hermia’s cake formation model fitted the flux declining behaviour better than the three other associated models. Finally, four different techniques based on artificial intelligence (AI) methods were used to fit the flux declining behaviour. They seem to outperform the simple Hermiaˊs model for the modelling of oily water filtration.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".