摘要
为了提高柴油机车尾气SCR后处理系统降污增效性能,本文首先利用有限元原理,建立三维流动传热耦合的SCR后处理系统数值预测模型;然后,考虑到直接利用数值模型优化的高计算成本,采用响应面分析方法(RSM)建立与该数值模型精度相当的响应面降解代理模型;再次,以代理模型为正问题求解器,以改进的非支配排序遗传算法(NSGA-Ⅱ)和优劣解距离决策方法(TOPSIS)为反问题优化求解器;最后,以系统排气压降、氨均匀性和速度均匀性等关键性能指标为多目标优化函数,并将优化问题转化为一个在可行域内的多目标寻优问题,开展柴油机车SCR后处理性能优化。研究结果表明:与初始设计相比,经NSGA-II优化以及TOPSIS决策的最优后处理系统排气压降下降20.09%,氨均匀性与速度均匀性分别上升12.46%和10.41%。
In order to improve performance of pollution reduction and efficiency enhancement of diesel locomotive exhaust SCR post-processing system, firstly, the numerical predication model of SCR post-processing system coupling 3D flow and heat transfer was established based on the finite element principle. Secondly, a response surface surrogate model with the same accuracy as the numerical model was established by the RSM method considering the high optimization computational cost of numerical model. Thirdly, the surrogate model was used as the positive problem solver, and the improved non-dominated sorting genetic algorithm(NSGA-Ⅱ)and technique for order preference by similarity to an ideal solution method(TOPSIS) were used as the inverse problem solvers. Finally, the key performance indicators such as system exhaust pressure drop, ammonia uniformity and speed uniformity were taken as multi-objective optimization functions. The SCR post-processing performance optimization was carried out, which transforms the system structure optimization into a multiobjective optimization problem in the feasible region. The result shows that compared with the initial design, the exhaust pressure drop of the optimal system determined by NSGA-Ⅱ and TOPSIS decreases by 20.09%, and the ammonia uniformity and velocity uniformity increase by 12.46% and 10.41%, respectively.
作者
刘严
殷雷
吴涛涛
张锴
孟境辉
LIU Yan;YIN Lei;WU Taotao;ZHANG Kai;MENG Jinghui(College of Control Science and Engineering,Zhejiang University,Hangzhou 310027,China;Lionserv Co,Ltd.,Shanghai 201800,China;Tenneco Suzhou Emission System Co,Ltd.,Suzhou 215300,China;Beijing Key Laboratory of Emission Surveillance and Control for Thermal Power Generation,North China Electric Power University,Beijing 102206,China)
出处
《中南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2022年第12期4833-4844,共12页
Journal of Central South University:Science and Technology
基金
国家自然科学基金资助项目(51706067)。
关键词
多物理场建模
响应面分析
多目标优化
选择性催化还原(SCR)后处理
智能优化方法
multiphysics modeling
response surface analysis
multi-objective optimization
SCR(selective catalytic reduction)post-processing
intelligent optimization method
