Neuro-genetic machine learning framework accelerates the optimization of Ag/MnOx catalyst for total oxidation of toluene

Abstract

A neuro-genetic machine learning framework (ANN-GA) was employed to optimize and predict the optimum preparation parameters for the precipitation synthesis of high-efficiency silver-doped manganese oxides (Ag/MnOx) for toluene total oxidation. The preparation conditions of Ag/MnOx for maximum CO2 yield were predicted to be at 13.2 wt% of silver loading, 41.5 min of stirring time, 461 ℃ of calcination temperature, and 4.3 h of calcination time. The resulting Ag/MnOx-GA catalyst achieved the lowest T50 (CO2) of 206 ℃ comparing to the catalyst suggested by the response surface methodology (RSM) (214 ℃) and synthesized catalyst Ag/MnOx-R16 (220 ℃).The obtained relative importance of each factor involved in the preparation process revealed that all of the parameters should be taken into account, among which the silver loading had the most significant impact on the catalytic reactivity. The synthesized materials were characterized by ICP-OES, SEM/EDS, BET, Raman, XRD, XPS, TGA, H2-TPR, and O2-TPD. The results indicated that the Ag/MnOx-GA exhibited the largest surface area (84.4), the lowest average oxidation state (AOS) of Mn (3.25), the best redox capacity, and the highest ratios of Oads/Olatt (0.75). These consequences provided evidence that the toluene total oxidation was more likely to occur on the surface of Ag/MnOx-GA at a relatively lower temperature.