【摘要】 通过研究选择性催化还原(SCR)技术机理，建立催化裂化(FCC)装置再生烟气SCR系统脱硝机理微分方程组模型。基于大量工业SCR系统数据，利用龙格库塔吉尔(RKG)方法对脱硝机理微分方程组进行求解，并结合遗传算法对模型参数进行寻优。结果表明，模型对FCC装置SCR系统出口氮氧化物(NO_x)浓度预测的平均绝对误差为5.75%,模型预测值与装置实际值拟合的可决系数为0.906。这说明所建SCR脱硝机理模型具有较强的泛化能力和较高模拟精度，可用于优化FCC装置再生烟气SCR系统的操作条件，实现NO_x排放达标。更多还原

【Abstract】 Based on the mechanism of selective catalytic reduction(SCR) technology, a set of denitrification mechanism modeling equations of regenerator flue gas SCR system in fluid catalytic cracking unit were established. According to a large amount data of SCR system, the mechanism model equations were solved by Runge-Kutta-Gill method, and the model parameters were optimized by combining genetic algorithm. The verification results show that the mean absolute percentage error of the model is 5.75% and the coefficient of determination is 0.906,which indicate that the established SCR denitrification mechanism model has strong generalization ability and high simulation accuracy. The model will be expected to play an important role in optimizing the operating conditions of the SCR systems for achievement standard of nitrogen oxides emission of the treated flue gas.更多还原