Artificial neural network modelling and experimental investigations of malachite green adsorption on novel carboxymethyl cellulose/β-cyclodextrin/nickel cobaltite composite

Abstract

This study presents a novel polymer nanocomposite based on carboxymethyl cellulose and β-cyclodextrin crosslinked with succinic acid (CMC-SA-β-CD) containing nickel cobaltite (NCO) nano-reinforcement. Various analytical techniques have been employed to investigate the structural, thermal, and morphological features of the resulting nanocomposite. The CMC-SA-β-CD/NCO nanocomposite has been utilized as an adsorbent for the removal of bisphenol-A (BPA, R% <40 %), malachite green (MG, R% > 75 %)), and Congo red (CR, no adsorption) from the synthetic wastewater. The study systematically explored the impact of various parameters on the adsorption process, and the interactions between MG and CMC-SA-β-CD/NCO were discussed. The adsorption data were fitted to different models to elucidate the kinetics and thermodynamics of the adsorption process. An artificial neural network (ANN) analysis was employed to train the experimental dataset for predicting adsorption outcomes. Despite a low BET surface area (0.798 m2 g−1), CMC-SA-β-CD/NCO was found to exhibit high MG adsorption capacity. CMC-SA-β-CD/NCO exhibited better MG adsorption performance at pH 5.5, 40 mg L−1 MG dye concentration, 170 min equilibrium time, 20 mg CMC-SA-β-CD/NCO dose with more than 90 % removal efficiency. Moreover, the thermodynamic studies suggest that the adsorption of MG was exothermic with ΔH° value −9.93 ± 0.76 kJ mol−1. The isotherm studies revealed that the Langmuir model was the best model to describe the adsorption of MG on CMC-SA-β-CD/NCO indicating monolayer surface coverage with Langmuir adsorption capacity of 182 ± 4 mg g−1. The energy of adsorption (11.4 ± 0.8 kJ mol−1) indicated chemisorption of MG on the composite surface. The kinetics studies revealed that the pseudo-first-order model best described the adsorption kinetics with qe = 86.7 ± 2.9 mg g−1. A good removal efficiency (>70 %) was retained after five regeneration reuse cycles. The ANN-trained data showed good linearity between predicted and actual data for the adsorption capacity (R-value>0.99), indicating the reliability of the prediction model. The developed nanocomposite, composed predominantly of biodegradable material, is facile to synthesize and exhibited excellent monolayer adsorption of MG providing a new sustainable adsorbent for selective MG removal.