TY - JOUR
T1 - Modeling and optimization of imidacloprid degradation by catalytic percarbonate oxidation using artificial neural network and Box-Behnken experimental design
AU - de Luna, Mark Daniel G.
AU - Sablas, Michael M.
AU - Hung, Chang Mao
AU - Chen, Chiu Wen
AU - Garcia-Segura, Sergi
AU - Dong, Cheng Di
N1 - Funding Information:
The authors would like to thank the Ministry of Science and Technology, Taiwan (Contract Nos. MOST 106-2221-E-022-002-MY3 and 106-2221-E-022-003-MY3 ) and the Department of Science and Technology, Philippines for providing financial support for this research undertaking.
Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/7
Y1 - 2020/7
N2 - 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.
AB - 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.
KW - Advanced oxidation processes
KW - Artificial neural network
KW - Box-Behnken design
KW - Imidacloprid
KW - Mathematical modeling
KW - Wastewater treatment
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U2 - 10.1016/j.chemosphere.2020.126254
DO - 10.1016/j.chemosphere.2020.126254
M3 - Article
C2 - 32155499
AN - SCOPUS:85080993315
SN - 0045-6535
VL - 251
JO - Chemosphere
JF - Chemosphere
M1 - 126254
ER -