基于遗传算法的SCR动态模型参数辨识与仿真

Ren Hongjuan, Lou Diming, Zhu Jian, Luo Yiping
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引用次数: 0

摘要

研究了选择性催化还原反应(SCR)。利用遗传算法对SCR动力学模型方程的未知参数进行拟合,拟合结果与实验数据的误差在允许范围内。然后在AVL Boost软件中,得到了SCR反应的仿真结果。通过与试验数据的比较,仿真结果证明了该方法的有效性。最后,在AVL Boost中模拟了SCR反应,并在相同排气温度下,研究了GHSV和NSR对SCR反应的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic Model Parameter Identification and Simulation of SCR Based on Genetic Algorithm
The Selective Catalytic Reduce (SCR) is studied. The unknown parameters of the SCR kinetic model equations are fitted based on the Genetic Algorithm (GA), which is in the range of the allowable error, compared to the experimental data. Then in AVL Boost software, the simulation results of SCR reaction are obtained. Compared to the test data, the simulation results prove that the parameter identification is effective. At last, the SCR reaction is simulated in AVL Boost, and at the same exhaust temperature, the effect of GHSV and NSR on the SCR reaction is studied.
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