An artificial neural network (ANN)-based framework for the Cr removal from the spiked water samples by chitosan oligosaccharide-coated iron oxide nanoparticles

Abstract

Abstract: Chromium contamination in water has become a major concern worldwide due to its adverse health effects. Chitosan oligosaccharide-coated iron oxide nanoparticles (CSO-INPs) were used in the present study for the magnetic separation of chromium from a chromium spiked water samples. Transmission electron microscope–energy-dispersive X-ray spectroscopy elemental mapping and scanning electron microscope–energy-dispersive X-ray spectroscopy elemental analysis were carried out to confirm the successful removal of the contaminant (total Cr) from the spiked water samples. A feedforward artificial neural network (ANN) model has been developed to predict the optimum efficiency of chromium ions removal from aqueous solution by CSO-INPs. Both the batch experiments and the ANN have been applied to assess the impact of various factors such as pH, nanoparticles dose, temperature, and time influencing the Cr removal efficiency of CSO-INPs. Removal efficiency has been found to be higher at low pH, with higher dose of nanoparticles at higher temperature. The ANN simulation further gives us a set of desired conditions (pH 3, CSO-INPs dose 0.7 mg/ml, temperature 28–30 °C, time 60 min) to achieve an optimum Cr removal efficiency of CSO-INPs. Graphical Abstract: [Figure not available: see fulltext.]