市政污泥富氧燃烧特性影响因素分析及预测

doi:10.16085/j.issn.1000-6613.2024-1235

摘要/Abstract

摘要：

市政污泥富氧燃烧特性的影响因素及影响规律尚不完全明确，富氧燃烧特性的预测及量化评估方法依然缺乏。针对此，本文研究管式炉预热温度、管式炉入口气体流量、氧气浓度对污泥燃烧特性及反应域内温度场分布状态的影响规律，提出了基于数据驱动的污泥富氧燃烧特性回归预测模型，并完成了模型的准确性验证分析。研究结果表明，预热温度对污泥富氧燃烧特性的影响最为显著。当预热温度从873.15K升高到1073.15K时，污泥的着火延迟时间和着火温度分别降低了75%和49%。着火延迟时间及着火温度均随着气体流量的增加而增大，上升幅度减缓。高温富氧氛围下，氧气浓度提升对污泥富氧燃烧特性的影响较小。预热温度为1073.15K、氧气质量分数在0.3~0.8范围内变化时，污泥着火延迟时间及着火温度的变化量分别为1.04s和2.93K。基于遗传算法优化的污泥富氧燃烧特性回归预测模型能够较准确预测污泥着火延迟时间及着火温度，模型预测结果与数值计算结果之间的相对误差整体小于10%，与试验测试结果之间的平均相对误差小于15%。

关键词: 市政污泥, 富氧燃烧, 燃烧特性, 数值模拟, 遗传算法, 预测

Abstract:

The underlying factors and mechanisms that influence the oxy-fuel combustion characteristics of municipal sludge remain largely elusive. Predictive and quantitative evaluation methods for these characteristics are currently lacking. In view of this, the influence of tube furnace preheating temperature, gas flow rate, and oxygen concentration on the combustion characteristics and temperature distribution within the reaction domain were investigated. A data-driven regression model for predicting the oxy-fuel combustion characteristics of sludge was developed and verified. The findings indicated that preheating temperature exerted the most substantial influence on the characteristics of sludge’s oxy-fuel combustion. As the preheating temperature rose from 873.15K to 1073.15K, the ignition delay time and ignition temperature of sludge decreased by 75% and 49%, respectively. The ignition delay time and ignition temperature exhibited an increase with the increase of gas flow rate, yet the rate of increase attenuated. In a high-temperature and oxygen-enriched atmosphere, an increment in oxygen concentration exerted minimal influence on oxygen-enriched combustion characteristics of sludge. At a preheating temperature of 1073.15K, the change in oxygen mass fraction from 0.3 to 0.8 led to a variation of 1.04s in sludge ignition delay time and 2.93K in ignition temperature. The regression model for sludge oxy-fuel combustion characteristics optimized by the genetic algorithm can accurately predicts sludge ignition delay time and temperature. The model’s predictions exhibited a relative error of less than 10% compared to numerical calculations and less than 15% compared to experimental data.

Key words: municipal sludge, oxygen-enriched combustion, combustion characteristics, numerical simulation, genetic algorithm, prediction

中图分类号:

X705

张光辉, 江金旭, 黄磊, 陈士祥, 马天添. 市政污泥富氧燃烧特性影响因素分析及预测[J]. 化工进展, 2025, 44(9): 5460-5470.

ZHANG Guanghui, JIANG Jinxu, HUANG Lei, CHEN Shixiang, MA Tiantian. Influencing factors analysis and prediction for oxygen-enriched combustion characteristics of municipal sludge[J]. Chemical Industry and Engineering Progress, 2025, 44(9): 5460-5470.

图/表 18

图1 污泥富氧燃烧数值模型

表1 污泥工业分析和元素分析

工业分析^①（质量分数）/%					元素分析^①（质量分数）/%				低位发热量Q/MJ·kg^-1
C	H	O	N	S	挥发分	固定碳	灰分	水分	低位发热量Q/MJ·kg^-1
26.33	4.56	15.59	4.84	0.87	48.12	4.07	44.31	3.50	9.70

图2 管式炉内流体域初始温度场分布

图3 气相反应域温度及固相反应域挥发分浓度分布云图

图4 污泥颗粒的热失重曲线

图5 不同预热温度及氧气浓度占比下污泥颗粒热失重曲线

图6 不同氧气浓度占比下污泥着火温度实验结果与数值计算结果对比分析

图7 不同预热温度下污泥颗粒温度分布云图

图8 不同预热温度下管式炉内部温度分布云图

图9 污泥富氧燃烧特性随预热温度变化关系

图10 不同气体流量下污泥颗粒温度分布云图

图11 不同气体流量下管式炉内部温度分布云图

图12 污泥富氧燃烧特性随气体流量变化关系

图13 不同氧气质量分数下污泥颗粒温度分布云图

图14 不同氧气质量分数下管式炉内部温度分布云图

图15 污泥燃烧特性参数随氧气质量分数变化关系

图16 BP神经网络预测原理及模型优化流程

图17 污泥富氧燃烧特性预测结果验证分析

参考文献 20

[1]	代敏怡, 郭占斌, 赵立欣, 等. 玉米秸秆与市政污泥混合热解特性及动力学分析[J]. 农业工程学报, 2021, 37(2): 242-250.
	DAI Minyi, GUO Zhanbin, ZHAO Lixin, et al. Pyrolysis characteristics and kinetic analysis of maize stovers mixed with municipal sludge[J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(2): 242-250.
[2]	王美清, 郁鸿凌, 陈梦洁, 等. 城市污水污泥热解和燃烧的实验研究[J]. 上海理工大学学报, 2014, 36(2): 185-188, 193.
	WANG Meiqing, YU Hongling, CHEN Mengjie, et al. Experimental study on the pyrolysis and combustion of municipal sewage sludge[J]. Journal of University of Shanghai for Science and Technology, 2014, 36(2): 185-188, 193.
[3]	GHODKE Praveen Kumar, SHARMA Amit Kumar, PANDEY J K, et al. Pyrolysis of sewage sludge for sustainable biofuels and value-added biochar production[J]. Journal of Environmental Management, 2021, 298: 113450.
[4]	江子箫, 陈晓平, 蒋志坚, 等. 城市污泥流化床燃烧过程中气态污染物排放特性[J]. 化工进展, 2018, 37(1): 368-374.
	JIANG Zixiao, CHEN Xiaoping, JIANG Zhijian, et al. Gaseous pollutants emissions from fluidized bed combustion of municipal sewage sludge[J]. Chemical Industry and Engineering Progress, 2018, 37(1): 368-374.
[5]	HANNL Thomas Karl, SKOGLUND Nils, Juraj PRIŠČÁK, et al. Bubbling fluidized bed co-combustion and co-gasification of sewage sludge with agricultural residues with a focus on the fate of phosphorus[J]. Fuel, 2024, 357: 129822.
[6]	赵佳琪, 黄亚继, 李志远, 等. 污泥同农林废弃物共热解重金属迁移转化特性研究[J]. 化工进展, 2025, 44(2): 1064-1075.
	ZHAO Jiaqi, HUANG Yaji, LI Zhiyuan, et al. Characteristics of heavy metal migration and transformation during co-pyrolysis of sludge with agroforestry wastes [J]. Chemical Industry and Engineering Progress, 2025, 44(2): 1064-1075.
[7]	李志远, 黄亚继, 赵佳琪, 等. 污泥与聚氯乙烯共热解重金属特性[J]. 化工进展, 2023, 42(9): 4947-4956.
	LI Zhiyuan, HUANG Yaji, ZHAO Jiaqi, et al. Characterization of heavy metals during co-pyrolysis of sludge with PVC[J]. Chemical Industry and Engineering Progress, 2023, 42(9): 4947-4956.
[8]	WEI Daining, AN Dong, WANG Tao, et al. Influence of fuel distribution on co-combustion of sludge and coal in a 660MW tangentially fired boiler[J]. Applied Thermal Engineering, 2023, 227: 120344.
[9]	DOTTEI Alexander, HOLTZ Dorian, Karsten MÜLLER. Numerical investigation of sewage sludge combustion in a fluidized bed reactor: A comparison of 2D and 3D simulations[J]. Powder Technology, 2023, 428: 118834.
[10]	叶聪, 邢献军, 张学飞, 等. 城市污泥与稻壳水热炭混合燃烧特性与动力学[J]. 过程工程学报, 2020, 20(3): 362-370.
	YE Cong, XING Xianjun, ZHANG Xuefei, et al. Co-combustion characteristics and kinetics of municipal sludge and rice husk hydrochar[J]. The Chinese Journal of Process Engineering, 2020, 20(3): 362-370.
[11]	KUTTIN Kannie Winston, DING Lu, YU Guangsuo. Numerical and experimental assessment of parametric effect on steam-carbon dioxide co-gasification of sewage sludge and coal in an updraft reactor[J]. International Journal of Hydrogen Energy, 2024, 91: 204-218.
[12]	KUTTIN Kannie Winston, SALEM Ahmed M, DING Lu, et al. Parametric evaluation of carbon dioxide and steam co-gasification of sewage sludge and palm kernel shell in a downdraft fixed bed reactor: Computational Fluid Dynamics (CFD) approach[J]. Applied Energy, 2025, 379: 124949.
[13]	KUZNETSOV Viktor A, BOZHEEVA Daria M, MINAKOV Andrey V. Numerical study on processes of oxy-fuel combustion of coal-water slurry in the furnace chamber[J]. Fuel, 2024, 371: 132034.
[14]	ÁLVAREZ L, GHAREBAGHI M, POURKASHANIAN M, et al. CFD modelling of oxy-coal combustion in an entrained flow reactor[J]. Fuel Processing Technology, 2011, 92(8): 1489-1497.
[15]	李伟, 刘敦禹, 陈军, 等. 基于鼓泡反应器高压联合脱除富氧燃烧烟气中NO _x 和SO _x 的建模分析[J]. 环境工程, 2019, 37(7): 135-141.
	LI Wei, LIU Dunyu, CHEN Jun, et al. A modeling analysis on simultaneous removal of NO _x and SO _x in a bubbling reactor under high pressure for oxy-fuel combustion flue gas[J]. Environmental Engineering, 2019, 37(7): 135-141.
[16]	毕三宝. O₂/CO₂气氛下污泥混煤燃烧特性及常规污染物排放特性研究[D]. 济南: 山东大学, 2018.
	BI Sanbao. Study on combustion characteristics and conventional pollutant emission characteristics of sludge blended coal in O₂/CO₂atmosphere[D]. Jinan: Shandong University, 2018.
[17]	余峰, 王珂佳, 张文龙, 等. 基于遗传算法优化BP神经网络的水生态修复原位控浊混凝投药预测[J]. 环境工程, 2023, 41(4): 154-163.
	YU Feng, WANG Kejia, ZHANG Wenlong, et al. Prediction of coagulant dosage for in situ turbidity control in water ecological restoration based on bp neural network optimized by genetic algorithm[J]. Environmental Engineering, 2023, 41(4): 154-163.
[18]	陈静妍, 徐荣吉, 吴青平, 等. 基于BP神经网络的不凝性气体对脉动热管传热影响的分析[J]. 化工进展, 2020, 39(7): 2574-2582.
	CHEN Jingyan, XU Rongji, WU Qingping, et al. Heat transfer of non-condensable gas to pulsating heat pipe based on BP neural network model[J]. Chemical Industry and Engineering Progress, 2020, 39(7): 2574-2582.
[19]	李凯, 魏鹤琳, 尹志凡, 等. 基于GA-BP神经网络模型预测水基炭黑-胶原蛋白纳米流体热导率和黏度[J]. 化工进展, 2024, 43(7): 4138-4147.
	LI Kai, WEI Helin, YIN Zhifan, et al. Prediction of thermal conductivity and viscosity of water-based carbon black nanofluids based on GA-BP neural network model[J]. Chemical Industry and Engineering Progress, 2024, 43(7): 4138-4147.
[20]	王成鑫. 污泥燃烧与污染物排放特性研究[D]. 合肥: 中国科学技术大学, 2021.
	WANG Chengxin. Study on sludge combustion and pollutant emission characteristics[D]. Hefei: University of Science and Technology of China, 2021.