Micellar-Enhanced Ultrafiltration to Remove Nickel Ions: A Response Surface Method and Artificial Neural Network Optimization
Bibliographic record
Abstract
Nickel ions from aqueous solutions were removed by micellar-enhanced ultrafiltration (MEUF), using the surfactant sodium dodecyl sulfate (SDS) as a chelating agent. Process variables and indicators were modeled and optimized by a response surface methodology (RSM), using the Box–Behnken design (BBD). The generated quadratic models described the relationship between a performance indicator (nickel rejection rate or permeate flux) and process variables (pressure, nickel concentration, SDS concentration, and molecular weight cut-off (MWCO)). The analysis of variance (ANOVA) showed that both models are statistically significant. To remove 1 mM of nickel ions, the optimal condition for maximum nickel removal and flux were: pressure = 30 psi, CSDS = 10.05 mM, and MWCO = 10 kDa, resulting in a rejection rate of 98.16% and a flux of 119.20 L/h∙m2. Experimental verification indicates that the RSM model could adequately describe the performance indicators within the examined ranges of the process variables. An artificial neural network (ANN) modelling followed to predict the MEUF performance and validate the RSM results. The obtained ANN models showed good fitness to the experimental data.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".