Abstract
The dairy industry produces large amounts of dairy wastewater containing ammonia nitrogen (NH3-N). Sustainable treatment technologies are needed which can reduce the environmental pollution caused by NH3-N emissions from dairy wastewater. Chemical coagulation combined with the photo-electro-Fenton (PEF) treatment process has been considered a promising technology that can effectively remove NH3-N from dairy wastewater. In this study, Taguchi design was used first to narrow down the operating factors from five to three. The three most influential factors were then further optimized for an optimum NH3-N removal efficiency using response surface methodology (RSM) coupled with Box–Behnken design. Both RSM and artificial neural network (ANN) models were developed to predict the NH3-N removal efficiency. Under the optimal conditions of 0.51 mM Fe2+, 49.44 mA/cm2 current density, and 118.60 min treatment time, removal of 92.13% NH3-N from dairy wastewater with 90% N2 selectivity was achieved during validation experiments. The ANN model showed a superior predictive performance to the RSM model. The NH3-N degradation rate was calculated at 0.0229 min−1 based on a pseudo-first-order kinetic model. These findings demonstrate the applicability of the integrated chemical coagulation and PEF process for significantly reducing ammonia nitrogen in dairy wastewater.