Response surface methodology-artificial neural network application for bisphenol A treatment using carbon nanotubes manganese oxide composite

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.