基于改进混沌优化的选择性催化还原系统参数辨识方法

doi:10.13229/j.cnki.jdxbgxb.20220409

Abstract

Abstract:

The SCR system is an important component of the emission aftertreatment systems for diesel engines and is primarily responsible for reducing NO_x emissions. With increasingly stringent emission regulations， the control technology based on precise models for SCR systems is becoming an inevitable choice. During the working process of SCR systems， the model parameters will be shifted due to aging， wear and other effects. In particular， catalyst sulfur poisoning can cause a severe drop in the maximum ammonia adsorption capacity， which can lead to the failure of the SCR system. In this paper， a stepped-up chaos optimization algorithm with single search algorithm （SCOA+SSA） is proposed to solve multi-parameter online identification for SCR systems. This method can simultaneously identify eight key parameters in the chemical reaction kinetics of the SCR system. The experimental results show that this method can identify new system parameters in real time when the maximum adsorption capacity of ammonia decreases in steps， that as compared to the traditional SCOA method， the identification accuracy of the proposed SCOA+SSA method is improved by 5.44% with a computational time penalty of 2.9%.

Key words: automatic control technology, parameter online identification, stepped-up chaos optimization algorithm, SCR systems

CLC Number:

TP273

Jing-hua ZHAO,Shi-hao DU,Liang-wei LIU,Yun-feng HU,Yao SUN,Fang-xi XIE. Parameter identification for SCR systems based on improved chaos optimization algorithm[J].Journal of Jilin University(Engineering and Technology Edition), 2024, 54(2): 550-557.

Figures/Tables 14

Fig. 1

Table 1

Nomenclature of parameters"

符号	描述
$E F v$	排气体积流量和SCR系统体积的比值/（m³·s^-1）
$C N O x, i n$	SCR系统入口NO _x 浓度/（mol·m^-3）
$C N O x$	SCR系统出口NO _x 浓度/（mol·m^-3）
$C N H 3, i n$	SCR系统入口NH₃浓度/（mol·m^-3）
$C N H 3$	SCR系统出口NH₃浓度/（mol·m^-3）
$θ M A X$	SCR催化剂的最大氨吸附能力
$θ c r i l$	SCR催化器表面氨的临界覆盖度
$E F M$	排气质量流量/（g·s^-1）
$R$	气体常数
$T$	SCR系统内部温度/°C
$P 0$	大气压/Pa
$M$	排气摩尔质量/（g·mol^-1）
$V$	SCR系统体积/L
$C H N C O$	异氰酸浓度/（mol·m^-3）
$E 1$	异氰酸水解反应活化能/（J·mol^-1）
$k 1$	异氰酸水解反应速率因子
$m 1$	反应特性参数
$r 1$	异氰酸水解反应速率/s^-1
$r 2$	NH₃吸附反应速率/s^-1
$r 3$	NH₃解吸附反应速率/s^-1
$r 4$	NO _x 催化还原反应速率/s^-1
$r 5$	NH₃氧化反应速率/s^-1

Table 1

Table 2

Nomenclature of parameters to be identified"

参数	描述
$E 2$	NH₃吸附反应活化能
$E 3$	NH₃解吸附反应活化能
$E 4$	NO _x 催化还原反应活化能
$E 5$	NH₃氧化反应活化能
$k 2$	NH₃吸附反应速率因子
$k 3$	NH₃解吸附反应速率因子
$k 4$	NO _x 催化还原反应速率因子
$k 5$	NH₃氧化反应速率因子

Table 2

Fig.2

Fig.3

Table 3

Fig.4

Fig. 5

Table 4

Results of parameter identification between the algorithms under the first stage"

参数	COA	PCOA^［12］	SCOA	SCOA+SSA
$E 2$	894.18	600.11	234.83	97.48
$E 3$	2.6×10⁶	5.9×10⁶	7.3×10⁶	2.7×10⁵
$E 4$	2992.07	2789.35	2793.00	404.18
$E 5$	16 697.74	36 386.52	17 167.48	16 254.76
$k 2$	811.66	495.33	377.55	264.92
$k 3$	1.1×10⁷	1.8×10⁷	1.7×10⁶	41 235.36
$k 4$	1.0×10⁶	1.0×10⁶	1.5×10⁷	8.5×10⁵
$k 5$	17 543.16	8107.43	74 015.25	10 042.43

Table 4

Table 5

Table 6

Results of parameter identification between the algorithms under the second stage"

参数	COA	PCOA^［12］	SCOA	SCOA+SSA
$E 2$	1000.4	950.4	1910.2	1030.8
$E 3$	1.0e+06	9.6e+06	4.0e+05	1.8e+06
$E 4$	4000.9	4233.5	3297.4	1970.1
$E 5$	18 199.1	18 198.3	18 198.0	18 197.7
$k 2$	2532.3	2076.6	7178.2	2059.2
$k 3$	1.2e+06	3.0e+06	4090.6	5.7e+06
$k 4$	1.2e+06	2.5e+06	1.0e+06	1.8e+05
$k 5$	7285.0	7286.0	7285.2	7285.0

Table 6

Table 7

Table 8

Fig. 6

References 20

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评价指标	NO _x 模型精准度/%	NH₃模型精准度/%
PE	98.56	90.23
COA	84.82	74.97

评价指标	NO _x 模型精准度/%	NH₃模型精准度/%
COA	84.82	74.97
PCOA^［12］	89.78	80.59
SCOA	93.86	82.14
SCOA+SSA	97.51	90.18

评价指标	NO _x 模型精准度/%	NH₃模型精准度/%
COA	88.11	69.22
PCOA^［12］	91.02	76.23
SCOA	84.58	75.79
SCOA+SSA	83.86	86.55

评价指标	NO _x 模型精准度/%	NH₃模型精准度/%
COA	86.47	72.10
PCOA^［12］	90.40	78.41
SCOA	89.22	78.97
SCOA+SSA	90.69	88.37

Parameter identification for SCR systems based on improved chaos optimization algorithm

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