Abstract: A real-time data processing rule was proposed based on the process mechanism and experience of variable screening, significant error processing of Raida rule, and variable correlation analysis of MIC maximum information coefficient method, and the quality of modeling data was improved. Based on the production control demand of toluene and non-aromatic hydrocarbon content in benzene products of catalytic reforming unit, a prediction model of non-aromatic hydrocarbon and toluene content in benzene products was established by using BP neural network algorithm. The root mean square error of the two products predicted by the model was 0.012 4 and 0.046 3 respectively, and the average relative error was 1.036% and 3.312% respectively. Using genetic algorithm NSGA-II, the content of non-aromatic hydrocarbon and toluene in benzene could be reduced by 24.38% and 82.58% respectively. The model could be used to support the analysis of optimization scheme of the plant production, and the proposed modelling method could be used for the intelligent platform construction of the related devices.

Key words: catalytic reforming, data processing rules, BP neural network, NSGA-II method, smart plant