Methods And Research On Artificial Intelligence Optimization Of Graphene-loaded Iron-based Nanomaterials And Fenton-like Reagents For Removing Dyes（Carmine,Congo Red）

Advanced oxidation（e.g.,Fenton-like reagent oxidation and ozone oxidation）is a highly important technology that uses strong oxidizing free radicals to degrade organic pollutants and mineralize them.The Fenton-like reactions have the characteristics of low cost,simple operation,thorough reaction and no secondary pollution.Fenton-like reagents refer to a strong oxidation system composed of transition metal ions(e.g.,Fe³⁺,Mn²⁺and Ag⁺)and oxidants（hydrogen peroxide,potassium persulfate,sodium persulfate,etc）.Graphene and carbon nanotube possess a distinctive mechanical strength,flexibility,electrical and thermal conductivity and a very large specific surface area,which can work as an excellent carrier to disperse the catalyst and prevent its agglomeration.Fullerene can synergize with iron-based materials to promote the reaction of hydroxyl groups with organic pollutants and enhance the catalytic effect.Fenton-like catalysts influence the catalytic behavior by inducing electron transfer under strong interactions with the support.Due to the short lifespan of free radicals,the treatment effect is usually enhanced with the assistance of external conditions（ultraviolet and electric fields）to expand the application of Fenton-like catalysts in water treatment.Reduced graphene oxide loaded with an iron-copper nanocomposite was prepared in this study,using graphene oxide as a carrier and ferrous sulfate,copper chloride and sodium borohydride as raw materials.The obtained material was prepared for eliminating hazardous dye carmine and the binary dye mixture of carmine and Congo red.The process of carmine dye removal by the nanocomposite was modeled and optimized through response surface methodology and artificial intelligence（artificial neural network–particle swarm optimization and artificial neural network–genetic algorithm）based on single-factor experiments.The results demonstrated that the surface area of the nanocomposite was 41.255 m~2/g,the pore size distribution was centered at 2.125 nm,and the saturation magnetization was up to 108.33 emu/g.A comparison of the material before and after the reaction showed that the material could theoretically be reused three times.The absolute error between the predicted and experimental values derived by using an artificial neural network–particle swarm optimization was the smallest,indicating that this model was suitable to remove carmine from simulated wastewater.The dose factor was the key factor in the adsorption process.This process could be described with the pseudo-second-order kinetic model,and the maximum adsorption capacity was 1848.96 mg/g.The removal rate of the mixed dyes reached 96.85%under the optimal conditions（the dosage of r GO/Fe/Cu was 20 mg,the p H was equal to 4,the initial concentration of the mixed dyes was 500 mg/L,and the reaction time was 14 min）,reflecting the excellent adsorption capability of the material.In this experiment,reduced graphene oxide loaded iron-cobalt-nickel trimetallic nanocompounds were prepared by co-precipitation method and coadministered with oxidant hydrogen peroxide to form Fenton-like reagents.The degradation of carmine dye simulated industrial wastewater by this type of Fenton catalyst in combination with ultrasound was investigated and it can be identified that ultrasound is synergistic when combined with this type of Fenton process.In this experiment,the synthesized nanocomposites were characterized by X-ray diffraction,scanning electron microscopy,energy dispersive spectroscopy,Fourier transform infrared spectroscopy,Raman spectroscopy,N₂ adsorption,and superconducting quantum interferometry,and the results demonstrated that the materials have a typical graphite porous structure and excellent magnetic properties.The influences of four factors（V（H₂O₂）,reaction time,initial concentration and catalyst dosage）on the degradation of carmine dye were further analyzed.The experiment was then modeled and optimized with response surfaces and artificial intelligence.Through the comparative analysis of the experimental value and the predicted value,it is found that the absolute error of the artificial neural network-particle swarm optimization model is the smallest,which indicates that the model makes a better prediction of the experimental results.The importance of influencing factors in the dye removal process was ranked according to F-test,random forest and Garson’s formula,and it was discovered that V（H₂O₂）played the most dominant role.Finally,by a simple magnetic separation,the r GO/Fe/Co/Ni trimetallic nanocompound was still retained47%of its catalytic capacity after five consecutive recoveries using this catalyst and applying it to the next degradation experiment,which indicates its excellent reusability and reflects the outstanding degradation ability of this compounds.In conclusion,these mesoporous nanocomposites have the advantages of large specific surface area and saturation magnetization intensity,simplicity of operation and easiness of separation,and high removal rate for both monomeric and binary dyes.And it is further demonstrated that artificial intelligence techniques are helpful for modeling and optimization of dye removal processes.