Optimizing Nitrate Removal from Water Using Ordered Mesoporous nZVI/Zr-Ce-SBA-15 Composites Assisted by RSM and ANN

Abstract Excessive concentrations of nitrate (NO3-N) in water can stimulate the growth of algae, resulting in the deterioration of water quality, reduction of biodiversity and degradation of ecosystems. Zero valent nanoiron (nZVI) has been successfully supported on many carriers for NO3-N removal, of which there are few reports concerning silicon-based materials. Therefore, the present study investigated NO3-N removal from simulated wastewater by nZVI supported on ordered mesoporous Zr-Ce-SBA-15 composites (nZVI/Zr-Ce-SBA-15) assisted by response surface methodology (RSM), an artificial neural network combined with a genetic algorithm (ANN-GA) and a radial basis neural network (RBF). The successful support of nZVI on Zr-Ce-SBA-15 was confirmed using XRD, FTIR, TEM, SEM–EDS, N2 adsorption and XPS, which indicated ordered mesoporous materials. The absolute error of the predicted and actual values exhibited that the ANN model was better than the RSM model for optimizing the conditions of NO3-N removal, and its optimized removal rate of NO3-N by the composites reached 95.71% under the following optimal parameters: initial pH = 4.89, contact time = 62.27 min, initial NO3-N concentration = 74.84 mg/L and temperature = 24.77 ℃. Moreover, the RBF neural network further confirmed the reliability of the ANN-GA model. The process of NO3-N adsorption on Zr-Ce-SBA-15 followed pseudo second-order kinetic and Langmuir models (maximum adsorption capacity of 47.17 mg/g), and this reaction was spontaneous, endothermic and entropy driven. The yield of N2 can be improved after nZVI was supported on Zr-Ce-SBA-15, and the composites exhibited strong renewable ability in the short term within 3 cycles. Our study suggested that the ordered mesoporous nZVI/Zr-Ce-SBA-15 composites were an efficient and promising material for simultaneously removing NO3-N and improving the selectivity of N2, which provides a theoretical reference for NO3-N remediation from wastewater.