Back-propagation neural network: Box–Behnken design modelling for optimization of copper adsorption on orange zest biochar

Abstract

Heavy metals adsorption by adsorbents prepared from natural materials is a low-cost effective method for their removal from aqueous environments. This study aims to assess the applicability of orange zest biochar to adsorb divalent copper (cupric chloride) from its aqueous solution by maximizing the adsorption capacity using feed-forward back-propagation neural network (FFBPNN)-Box-Behnken design (BBD) modelling. BBD modelling predicted the maximum of 99.61% copper removal at an initial concentration of copper, adsorbent dosage and temperature of 100 ppm, 192.5 mg per 100 mL of feed solution and 38 °C. The results showed the best fit between experimental, BBD and FFBPNN predicted values. Langmuir isotherm fitted well with the experimental data than Freundlich model, and the maximum adsorption capacity was found to be 116.28 mg/g. Also, adsorption kinetic data followed the Lagergren's pseudo-first-order kinetic model. Thus, the obtained results conclude that the orange zest biochar was found to be one of the potential adsorbents for the removal of divalent copper from its aqueous solution.

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Excellent Cr(vi) adsorbent made from pyrolyzed green coconut trash with parametric modelling and optimization using RSM and experimental data

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