﻿ 柴油机SCR装置自抗扰控制器非线性反馈增益研究
 舰船科学技术  2024, Vol. 46 Issue (3): 131-136    DOI: 10.3404/j.issn.1672-7649.2024.03.023 PDF

Research on nonlinear feedback gain of active disturbance rejection controller for SCR unit of diesel engine
LI Hong-liang, JIN Hua-biao, LIU Bing-shan
School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China
Abstract: In order to improve the emission control quality of diesel engine selective catalytic reduction (SCR) unit under transient operating conditions, a nonlinear active disturbance rejection control system was designed for SCR unit and optimization of nonlinear feedback gain was carried out based on improved particle swarm optimization algorithm. The results show that compared with the previous nonlinear feedback gain, the nonlinear feedback gain adjusted by the improved algorithm improves the robustness of the control system, and the calibrated nonlinear feedback gain can increase the accuracy of NOx emission control by 1.5% and shorten the transition process time by 11%.
Key words: diesel engine     selective catalytic reduction     active disturbance rejection control     particle swarm optimization
0 引　言

1 SCR装置数学模型的建立 1.1 SCR装置结构

SCR装置作为柴油机达到排放国标的主要技术平台，其主要由尿素加注模块、催化器模块和控制诊断模块构成，具体结构见图1

 图 1 SCR装置结构示意图 Fig. 1 SCR device structure diagram
1.2 SCR简单数学模型

SCR后处理装置中，降低内燃机NOx排放物的化学反应，分为两大过程即尿素的分解过程和氨气与氮氧化合物进行的催化还原反应过程，上述2个过程在排气管与催化器上反应[9]

1）尿素的分解过程

 ${\left( {{\rm{N}}{{\rm{H}}_2}} \right)_2}{\rm{CO}} + {{\rm{H}}_2}{\rm{O}} \to 2{\rm{N}}{{\rm{H}}_3} + 2{\rm{C}}{{\rm{O}}_2}$ (1)

2）氨气还原氮氧化物过程

 ${\rm{2N{H_3}\left( S \right) + NO + N{O_2} \to 2{N_2} + 3{H_2}O}}$ (2)
 ${\rm{4N{H_3}\left( S \right) + 4NO + {O_2} \to 4{N_2} + 6{H_2}O}}$ (3)

 $G\left( S \right) = {e^{ - \tau s}}\frac{k}{{TS + 1}}。$ (4)

2 SCR自抗扰控制器设计 2.1 时滞对象改造

 $w\left( s \right) = \frac{{{e^{ - \tau s}}}}{{Ts + 1}} = \frac{1}{{Ts + 1}}\frac{1}{{\tau s + 1}} = \frac{1}{{T\tau {s^2} + \left( {T + \tau } \right)s + 1}} 。$ (5)

2.2 SCR自抗扰装置控制系统结构

 图 2 SCR自抗扰装置反应过程示意图 Fig. 2 Schematic diagram of the reaction process of SCR active disturbance rejection device

2.3 仿真模型

 图 3 SCR非线性自抗扰控制器仿真模型 Fig. 3 Simulation model of SCR nonlinear active disturbance rejection controller

 \left\{\begin{aligned} & {v}_{id}\left(t+1\right)=\omega \cdot {v}_{id}\left(t\right)+\\ & {c}_{1}{r}_{1}\left({p}_{id}-{x}_{id}\left(t\right)\right)+{c}_{2}{r}_{2}\left(t\right)\left({p}_{gd}-{x}_{gs}\left(t\right)\right)，\\ & {x}_{id}\left(t+1\right)={x}_{id}\left(t\right)+{v}_{id}\left(t+1\right)。\end{aligned}\right. (6)

 $F = \int_0^\infty {\left( {{\omega _1}\left| {e\left( t \right)} \right| + {\omega _2}{u^2}\left( t \right)} \right)} {\rm{d}}t。$ (8)

 图 4 算法优化流程 Fig. 4 Algorithm optimization process
4 仿真结果及分析

4.1 改进算法迭代次数对比

 图 5 PSO改进前后迭代次数对比 Fig. 5 Comparison of iterations before and after PSO improvement
4.2 补偿因子改变

 图 6 补偿因子改变时仿真结果 Fig. 6 Simulation results when compensation factor changes
4.3 氮氧化物浓度改变

 图 7 氮氧化物浓度改变时仿真结果 Fig. 7 Simulation results when nitrogen oxide concentration changes
4.4 SCR时滞时间改变

 图 8 时滞时间改变时仿真结果 Fig. 8 Simulation results when delay time changes
4.5 尿素溶液浓度改变

 图 9 尿素浓度改变时仿真结果 Fig. 9 Simulation results when urea concentration changes
5 结　语

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