Fuzzy modeling of arsenic removal process from groundwater by iron oxide nanoparticles

In recent studies in Iran, arsenic anomalies have shown in excess of the WHO standard (10 ppb) in water resources that the use this water has irreparable effects on human health. Therefore, it is necessary to offer a solution for reducing the arsenic anomalies. In recent years, nanoparticles have been used to reduce the arsenic concentration in water resources at syntheses samples. However, the performance of these nanoparticles to reduce the arsenic concentration in groundwater resources, which generally have different complexes, has not been investigated. In this study, iron oxide nanoparticles have been used to reduce the arsenic anomalies from groundwater resources. Previous studies on iron oxide nanoparticles have been to isolate arsenic added in the laboratory, but the main challenge of this study is the separation of arsenic from groundwater sample. The results of investigation the effective various parameters such as pH, temperature, filtration time and the adsorbent amount on the separation of arsenic from groundwater showed that the highest separation is the temperature conditions above ambient temperature, low pH, time 5 to 15 minutes and the adsorbent amount 0.3 g. Finally, Sugeno fuzzy model was used to simulate and model the process of removing arsenic pollutants from groundwater sources. The fuzzy model results showed this model is very efficient for approximate prediction of arsenic adsorption by iron oxide nanoparticles with NRMSE = 0.03 and R2 = 0.8.