Optimization of Fe1-xO ammonia synthesis catalyst by BP neural network model
A prediction model between the content of promoter and the activity of catalyst was established by BP neural network,with which the promoter of Fe1-xO ammonia synthesis catalyst was optimized.Firstly,the preliminary experimental data were summarized into five types of catalysts including three,four,five,six and seven promoters.With the content of the promoters(volume fraction)as the input model variable and the ammonia concentration(reactivity)at the outlet of the reactor at 425℃as the output one,the formula of the promoter was optimized.The results showed that maximum mean square error of fitting values of BP neural network prediction model was 0.2784,while that of the predicted values was 0.1592,indicating the accuracy of the BP neural network model was high.On the basis of this model,multiple population genetic algorithm was used to search the extreme value,and the optimal catalyst formula was obtained and verified by experiments.The maximum relative error between the measured values of 5 samples prepared according to the optimized formula and the predicted ones was 2.88%.The highest activity was 18.83%for the catalyst containing seven promoters,1.31%higher than the average reactivity value of the original sample(17.52%),and a relative increase of 7.48%.
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