Abstract: The molecular sieve catalyst of Ce-Cu-SSZ-13 has a good selective catalytic reduction (SCR) denitration performance at the temperature consistent with that of exhaust gas of industrial furnaces. It is proposed to be coated on the surface of honeycomb ceramics containing expansion and contraction channels to manufacture a high-temperature composite SCR regenerator, so that the integration of flue gas denitration and waste heat recovery equipment can be realized. To optimize the comprehensive performance of the new SCR regenerator, numerical simulation of SCR flue gas denitrification, coupled with regenerative heat transfer, is conducted with Fluent software, where regenerator length (L), expansion/contraction angle (θ), and flue gas flow rate (V) are adopted as design variables, and energy recovery ratio (ERR), denitrification efficiency (η), and flue gas pressure loss (?P) as objective functions. Based on the simulation results, an objective function regression model is established by using the response surface methodology (RSM). On this basis, the advanced non-dominated genetic algorithm (NSGA-II) is used to obtain the Pareto optimal solution set, from which the EWM (entropy weight method)-TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method is employed to calculate the relative closeness between Pareto optimal solutions and negative ideal solutions, then the optimal solution is determined. Analysis of variance verifies the significance of the regression model, while response surface analysis reveals the synergistic effects of different design variables on the objective function. For the current new composite SCR regenerator, the optimal objective functions take the values of η=99.38%, ?P=52.57 Pa, and ERR=68.13%, while the corresponding design variables are V=4.03 m/s, θ=12.19°, and L=717.57 mm. At this time, the average temperature of flue gas outlet is about 150℃, and the mass concentration of NO is 4.3 mg/m3. Besides, after limiting the flue gas velocity to 5 and 6 m/s, it is found that a larger V results in a smaller optimal θ to prevent the pressure loss increase significantly.

Key words: multi-objective optimization, numerical simulation, honeycomb regenerator, SCR denitration, expansion and contraction channel

摘要： 分子筛催化剂Ce-Cu-SSZ-13在与工业炉排烟温度一致的条件下具有卓越的选择性催化还原(Selective Catalytic Reduction, SCR)脱硝性能，将其涂覆于内含扩缩通道的蜂窝陶瓷表面制作高温复合SCR蓄热体，可实现烟气脱硝与余热回收设备一体化。为优化新型SCR蓄热体综合性能，以蓄热体长度(L)、扩张/收缩角(θ)和烟气流速(V)为设计变量，能量回收率(ERR)、脱硝效率(η)和烟气压力损失(?P)为目标函数，开展耦合蓄热式传热的SCR烟气脱硝Fluent数值模拟研究。根据模拟结果，采用响应面方法建立目标函数回归模型。在此基础上，采用多目标优化遗传算法NSGA-II求得帕累托最优解集，使用基于熵权法(Entropy Weight Method, EWM)的熵权TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution)法计算其与负理想解的相对接进度并以此确定最优解。方差分析验证回归模型的显著性，响应曲面分析则揭示了不同设计变量对目标函数的协同影响规律。结果表明，新型复合SCR蓄热体最佳设计变量为：L=717.57 mm, θ=12.19°和V=4.03 m/s，对应目标函数为ERR=68.13%, η=99.38%和?P=52.57 Pa。此时烟气出口温度约150℃，NO质量浓度为4.3 mg/m3。另外，将烟气速度约束至5和6 m/s后发现最优解减小了扩张/收缩角，以防止蓄热体压力损失大幅增加。

关键词: 多目标优化, 数值模拟, 蜂窝蓄热体, SCR脱硝, 扩缩通道