Artificial neural network model of the capacity of diethylenetriamine functionalised zinc oxide-apricot stone shell nanocomposite for sequestering 2,4-dichlorophenoxyacetic acid from wastewater

Abstract

A novel nanocomposite, diethylenetriamine functionalised zinc oxide-apricot stone shell (ZnO@Ap/DETA), has been synthesised for the efficient sequestration of 2,4-dichlorophenoxyacetic acid (2,4-D). The capacity and removal efficiency were evaluated using an artificial neural network (ANN). The characterisation of ZnO@Ap/DETA was performed to determine its physicochemical properties through various instrumental techniques. The nanocomposite displayed a more uniform mesoporous structure, having pores with an average size of 34.065 nm and a significantly enhanced surface area of 26.56 m² g⁻¹, approximately 13 times greater than that of the pristine apricot stone (AP) shells (2.076 m² g⁻¹). ZnO@Ap/DETA was utilised to adsorb 2,4-D from synthetic wastewater at pH 3, 50 mg adsorbent dose and 30 mg L⁻¹ initial 2,4-D concentration, and a real wastewater sample at 18.5 mg L⁻¹ 2,4-D concentration and 50 mg adsorbent dose, achieving performance efficiencies of 98.6% and 85.41%, respectively, after a 90-minute agitation period at 25 °C. The data on adsorption were consistent with the pseudo-second-order kinetic model and Langmuir isotherm, and thermodynamic investigations suggested that the process was spontaneous, favourable, and exothermic, as evidenced by a ΔH° value of −92.85 KJ mol⁻¹. To optimise the adsorption process, an ANN model was developed, comprising five input parameters, two hidden layers, and two output parameters. The developed model successfully predicted 2,4-D removal efficiency with a mean absolute error (MAE) of 0.2952, a mean squared error (MSE) of 0.4227, and a high R² of 0.9991. These results highlight the potential of ZnO@Ap/DETA for environmental remediation and for wastewater treatment.