Response surface methodology-artificial neural network application for bisphenol A treatment using carbon nanotubes manganese oxide composite
Bibliographic record
Abstract
Bisphenol A (BPA) is an emerging pollutant that easily escapes conventional treatment techniques. It requires application of novel composite materials along with mathematical modelling for optimisation and evaluation of the treatment process. In the present study, manganese (IV) oxide (MnO2) nanoparticles were doped onto the surface of multi-walled carbon nanotubes to develop an adsorptive–oxidative composite. The composite was characterised using transmission electron microscopy, X-ray diffraction, Raman spectroscopy, X-ray photoelectron spectroscopy, Fourier transform infrared spectroscopy and surface area analysis to confirm composite formation and study its properties. Conventional optimisation of pH (4–10), initial BPA concentration (10–50 mg/l) and contact time (0–60 min) was carried out and found to fit well with the Freundlich isotherm model (R 2 > 0.99) and followed a pseudo-second-order kinetic reaction. A central composite design model was applied using response surface methodology (RSM) to study individual parameters and their interaction effects to enhance process efficiency. Further, the experimental data sets and their responses from RSM were analysed using an artificial neural network. From random experimental sets (80%) of which (10%) each to train, validate and test were selected to analyse the variance of models for higher efficiency using Levenberg–Marquardt back-propagation (LM-BP) algorithm. Additionally, BPA-spiked simulated pharmaceutical waste water was treated with the composite to explore its treatment potential. This systematic experimental and computational approach aided in optimising treatment efficiency for real-time application.
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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) | 0.000 | 0.001 |
| 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".