Abstract
In the present study, cerium-loaded intercalated bentonite (Ce-bentonite) was prepared by doping rare earth cerium (Ce) in sodium bentonite. Response surface methodology (RSM) was employed to arrange the tests. Then genetic algorithm-back propagation neural network (GA-BPNN) was applied to optimize the preparation process of Ce-bentonite. The simultaneous removal potential of the Ce-bentonite for ammonia nitrogen (NH3-N) and phosphorus (P) from wastewater was investigated. SEM and XRD images revealed that Ce was successfully doped into bentonite. The obtained results showed that the optimal preparation conditions were obtained when roasting 2.0 g of cerium sulfate at 275 °C for 85 min. Under this condition, the removal rate of NH3-N and P increases to 75% and 96%, respectively. Moreover, it is found that compared with Langmuir adsorption isotherm, the Freundlich adsorption isotherm can better fit the adsorption process of Ce-bentonite for NH3-N and P removal. The performed analyses reveal that the prepared Ce-bentonite has multi-molecular layer adsorption properties. It is concluded that deeply learned RSM by GA-BPNN has high feasibility in the optimization of the preparation process and provides a new strategy to prepare materials.
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