Modeling and optimization of imidacloprid degradation by catalytic percarbonate oxidation using artificial neural network and Box-Behnken experimental design

Abstract

Due to its toxicity and persistence, pesticide pollution poses a serious threat to human health and the environment. Imidacloprid or IMD is an archetypal neonicotinoid insecticide commonly used to protect a variety of crops worldwide. The present study examines the applicability of two numerical tools -- artificial neural network (ANN) and response surface methodology – Box Behnken design (RSM-BBD) -- to model and optimize oxidative IMD degradation by sodium percarbonate (SPC). The influences of SPC dose, Fe2+ catalyst dosage, and solution pH on IMD removal were evaluated. An ANN composed of an input layer with three neurons, a hidden layer with eight optimum neurons, and an output layer with one neuron was developed to map the complex non-linear process at different levels. Seventeen designed runs of different experimental conditions were derived from RSM-BBD. These experimental conditions and their response values showed to be best fitted in a reduced cubic model equation. Sensitivity analyses revealed the relative importance of the various components: Fe2+ (40.4%) > pH (31.1%) > SPC dose (28.5%). The two model were highly predictive with overall coefficients of determination and root-mean-square errors of 0.9983 and 0.31 for ANN, while 0.9996 and 0.20 for RSM-BBD. Overall, the present study established ANN and RSM-BBD as valuable and effective tools for catalytic SPC oxidation of IMD contaminants. SPC is a cleaner alternative to other oxidants for pesticide degradation as it is non-toxic, safe to handle, and produces by-products that inherently exist in the natural water matrix.