Synthesis of eco-friendly polyaniline-zeolite nanocomposite for pollutant remediation: Empowered by robust machine learning algorithm
Authors :- Parmar M.; Shukla V.; Bandyopadhyay M.; Singh D.K.; Gaur R.; Shahabuddin S.
Publication :- Journal of Cleaner Production, (Elsevier) 2024.
The present research assesses the synthesis of an eco-friendly polyaniline and zeolite 4A (4 Armstrong) nanocomposite for enhanced adsorption of dye and heavy metal. Zeolite has been synthesized utilizing natural Kutch-sourced Kaolin clay. The materials synthesized were characterized via various instrumental techniques which confirms the formation of the desired materials. The composite exhibits superior performance in removing hazardous pollutants, demonstrating the removal of 98.5 % chromium in 12 min and 99.8% Congo red in 50 min, individually. Furthermore, for simultaneous removal, the composite continued displaying remarkable performance having a removal rate of 99% and 98.5 % for Congo red and chromium, respectively within 50 min, only. The nanocomposite follows the Freundlich isotherm model for chromium removal indicating physisorption and the Langmuir isotherm model for Congo removal signifying chemisorption. Artificial intelligence and machine learning (ML) techniques have been employed on the data set for the simulation of the adsorption percentage of the synthesized adsorbent. Various uncharted data visualizing techniques, including scatter plots, correlation coefficients, and mutual information were applied to explain the correlation between experimental variables. Conventional regression techniques showed poor performance with a high mean squared error (MSE) of 23 and an R2 value of 0.89. However, Bayesian optimization assisted ML model significantly improved the results. The Gaussian process with a polynomial function was the best performer, achieving an R2 value of 0.99 and mean squared error of 0.008. This research highlights the novel synthesis and effectiveness of the adsorbent materials, demonstrating the potential of machine learning in environmental remediation.