Modeling and analysis of particulate matter concentration in incinerator under benchmark conditions based on coupled numerical simulation

doi:10.16085/j.issn.1000-6613.2024-0822

Abstract

Abstract:

Particulate matter, a by-product of the municipal solid waste incineration (MSWI) process, can cause air pollution and contribute to haze formation. Exploring the formation mechanism of particulate matter in incinerators and the influencing factors from the generation source is very important for pollution reduction. In this article, the modeling and analysis of particle concentration in incinerator under benchmark conditions based on coupled numerical simulation was proposed. Firstly, the process flow for particulate matter generation was analyzed to determine the main factors affecting the concentration of particulate matter. Then, a numerical simulation model of particulate matter in incinerator coupled with fluid dynamic incinerator code (FLIC) and Fluent software was constructed for the real benchmark conditions. Then, the influence of the above main factors on the concentration of particulate matter was analyzed by single factor method. Finally, the optimal parameter combination of multiple factors (particle injecting velocityis 0.15m/s, 60μm, wall-jet) was obtained based on orthogonal experiment and range analysis. The effectiveness of the proposed method was verified by the actual data provided by an MSWI plant in Beijing. The results indicated that when the particle size was between 35—55μm, the concentration of particulate matter gradually increased with the increase of particle size, which can provide data support for reducing the concentration of particulate matter from the perspective of process improvement and optimal control.

Key words: municipal solid waste incineration (MSWI), particle concentration, coupled numerical modeling, univariate analysis, orthogonal experiment, optimal parameter

摘要：

城市固废焚烧（municipal solid waste incineration，MSWI）过程产生的副产品颗粒物会造成大气污染和促使雾霾形成，从生成根源上探究其在焚烧炉内的形成机制与影响因素对降污减排极为关键。本文提出了基于耦合数值仿真的基准工况下焚烧炉内颗粒物浓度建模与分析。首先，进行面向颗粒物生成的工艺流程分析以确定影响颗粒物浓度的多个主要因素；其次，面向真实基准工况构建耦合流体动力焚烧炉代码（fluid dynamic incinerator code，FLIC）与Fluent软件的炉内颗粒物数值仿真模型；然后，利用单因素法分析上述主要因素对颗粒物浓度的影响；最后，基于正交实验和极差分析获取多个因素的最佳参数组合（颗粒喷射速度0.15m/s，60μm，射流）。采用北京某MSWI厂提供的实际数据验证了所提方法的有效性，结果表明在颗粒粒径为35~55μm之间时，颗粒物浓度随颗粒粒径的增大而逐渐增大，该结果能够为从工艺改进和优化控制的角度降低颗粒物浓度提供数据支撑。

关键词: 城市固废焚烧, 颗粒物浓度, 耦合数值建模, 单因素分析, 正交实验, 最优参数

CLC Number:

TQ545

LIANG Yongqi, TANG Jian, XIA Heng, CHEN Jiakun, QIAO Junfei. Modeling and analysis of particulate matter concentration in incinerator under benchmark conditions based on coupled numerical simulation[J]. Chemical Industry and Engineering Progress, 2024, 43(S1): 106-120.

梁永琪, 汤健, 夏恒, 陈佳昆, 乔俊飞. 基于耦合数值仿真的基准工况下焚烧炉内颗粒物浓度建模与分析[J]. 化工进展, 2024, 43(S1): 106-120.

Figures/Tables 21

组成	MSW
工业分析/%
水	49.7
挥发分	32.22
固定碳	7.82
灰	10.26
元素分析/%
C	60.62
H	8.09
O	29.93
N	1.12
S	0.11

参数	数值
额定处理量/t·d^-1	800
实际处理量/t·d^-1	624
炉排类型	往复式炉排炉
炉排长×宽/m	11×12.9
炉排速度/m·h^-1	7.5
一次风流量/m³·h^-1	65400
二次风流量/m³·h^-1	5580
一次风温度/℃	473
一次风分布	24.31, 43.35, 19.27, 13.07

参数	数值
进料速度/kg·h^-1	24000
一次风量/m³·h^-1	65400
一次风温度/℃	473
炉排速度/m·h^-1	7.5
MSW粒径/mm	25
混合系数	2~6
辐射率/%	80
含水率/%	49.7

设置参数	温度/K
二次风入口	312.5
出口	1000
一烟道壁面	750
二烟道壁面	600
三烟道壁面	450

序号	设置参数	设置值
1	喷射源类型	Group
2	喷射位置	炉排上方
3	材料	Ash-soild
4	直径分布	Rosin-rammler
5	干燥段上方喷射流数量	10
6	燃烧段1上方喷射流数量	28
7	燃烧段2上方喷射流数量	12
8	燃烬段上方喷射流数量	12

序号	设置参数	设置值
1	温度/K	300
2	速度大小/m·s^-1	0
3	干燥段上方颗粒流量/kg·s^-1	0.02
4	燃烧段1上方颗粒流量/kg·s^-1	0.04
5	燃烧段2上方颗粒流量/kg·s^-1	0.02
6	燃烬段上方颗粒流量/kg·s^-1	0.005
7	最小直径/μm	35
8	最大直径/μm	75
9	平均直径/μm	45
10	发散系数	3.5
11	曳力准则	Grace
12	旋转曳力准则	Dennis-et-al
13	Magnus升力定律	Oesterle-Bui-Dinh
14	粗糙壁面模型	开启
15	离散随机轨迹模型	开启
16	尝试次数	3
17	时间尺度常数	0.15

水平	A/m·s^-1	B/μm	C
1	0.05	35	trap
2	0.1	40	reflect
3	0.15	45	wall-jet
4	0.2	50	—
5	0.25	55	—
6	0.3	60	—
7	0.35	65	—

案例	A/m·s^-1	B/μm	C	颗粒物浓度/kg·m^-3
1	1	1	1	0.0004364565
2	1	2	2	0.005192173
3	1	3	3	0.004967875
$⋮$	$⋮$	$⋮$	$⋮$	$⋮$
47	5	7	1	0.001560492
48	6	1	1	0.0008457572
49	7	5	1	0.001173455

案例	A/m·s^-1	B/μm	C	颗粒物浓度/kg·m^-3
1	1	1	1	0.0004364565
2	1	2	2	0.005192173
3	1	3	3	0.004967875
$⋮$	$⋮$	$⋮$	$⋮$	$⋮$
47	5	7	1	0.001560492
48	6	1	1	0.0008457572
49	7	5	1	0.001173455

指标	A	B	C
极差R	0.001733	0.002255	0.004010
主次顺序		C>B>A
最优水平	$A 3$	$B 6$	$C 3$

指标	A	B	C
极差R	0.001733	0.002255	0.004010
主次顺序		C>B>A
最优水平	$A 3$	$B 6$	$C 3$

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