城市生活垃圾与市政污泥共燃烧过程污染物生成特性及环境影响分析

张薇; 孙昱楠; 陈冠东; 崔卓; 武双; 马娇媚; 陈冠益

doi:10.13205/j.hjgc.202607001

城市生活垃圾与市政污泥共燃烧过程污染物生成特性及环境影响分析

doi: 10.13205/j.hjgc.202607001

张薇¹,
孙昱楠^1, ,,
陈冠东²,
崔卓¹,
武双¹,
马娇媚³,
陈冠益^1,2,4

1. 天津商业大学机械工程学院环境能源+X创新实验室, 天津 300134;
2. 西藏大学生态环境学院西藏自治区高原环境工程与污染控制重点实验室, 拉萨 850012;
3. 天津水泥工业设计研究院有限公司, 天津 300400;
4. 天津大学环境科学与工程学院, 天津 300072

基金项目:

国家科技重大专项“冀南地区多产业固废协同资源化关键技术研究及园区化示范”（2025ZD1203000）

详细信息

作者简介:
张薇（2000—），女，硕士研究生，主要研究方向为有机固废热化学处理。zhangwei1@stumail.tjcu.edu.cn

通讯作者:
孙昱楠（1992—），女，副教授，主要研究方向为固废处置与污染物控制。sunyunan@tjcu.edu.cn

计量
- 文章访问数: 25
- HTML全文浏览量: 1
- PDF下载量: 1
- 被引次数: 0
出版历程
- 收稿日期: 2026-04-02
- 网络出版日期: 2026-09-01

Research on pollutant generation characteristics and environmental impact analysis during co-combustion of municipal solid waste and sewage sludge

1. Interdisciplinary Innovation Laboratory for Environment and Energy, School of Mechanical Engineering, Tianjin University of Commerce, Tianjin 300134, China;
2. Key Laboratory of Plateau Environmental Engineering and Pollution Control, Xizang Autonomous Region, School of Ecology and Environment, Xizang University, Lhasa 850012, China;
3. Tianjin Cement Industry Design and Research Institute Co., Ltd., Tianjin 300400, China;
4. School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China

摘要

摘要: 为探究城市生活垃圾（MSW）与市政污泥（SS）共烧过程中污染物释放规律及环境效应，分析了不同掺混比的MSW和SS在850，950，1050 ℃下的燃烧特性、污染物生成及环境影响，并通过机器学习预测污染物生成，结合沙普利加性解释方法（SHAP）分析输入变量对排放的影响。结果表明：燃烧温度和掺混比例显著影响MSW与SS混合物的燃尽率及污染物排放，温度对燃尽率的影响更为突出，MSW与SS混合样品中SS质量占比为20%时燃烧效果相对较好。在达到显著水平的污染物中，N₂O和C₂H₄同时受温度、掺混比例及其交互作用影响显著，表明其生成过程对工况变化更为敏感；CO和C₆H₆主要受掺混比例影响显著；C₇H₈则同时受温度和掺混比例影响显著。N₂O和CH₄主要在燃烧初期释放，升温显著抑制N₂O生成，共烧总体有利于降低CH₄排放；SS质量占比为20%对SO₂减排作用较为明显，950 ℃条件下NO协同减排效果相对较好；CO、C₂H₄、C₆H₆和C₇H₈的排放在共烧条件下总体呈拮抗作用。人工神经网络模型在污染物预测上表现优异，燃烧温度以及掺混样品的挥发分和固定碳是影响污染物生成的重要因素。升高燃烧温度有利于降低全球变暖潜值和光化学氧化生成潜值，降低MSW质量占比有助于削弱光化学氧化生成潜值，但会提高全球变暖潜值和酸化潜值。综合燃烧特性、污染物释放规律及环境影响评价结果，建议SS质量占比取值20%，以实现协同处置MSW与SS燃烧性能与环境效应的协调优化。
- 城市生活垃圾 /
- 市政污泥 /
- 共燃烧 /
- 机器学习 /
- 环境影响评价
Abstract: To investigate the pollutant release behavior and environmental impacts during the co-combustion of municipal solid waste （MSW） and sewage sludge （SS）， the combustion characteristics， pollutant release， and environmental impacts of MSW-SS blends at 850， 950， 1050 ℃ were analyzed. Machine learning was further used to predict pollutant generation， and interpretability analysis was applied to identify the effects of input variables on emissions. The results showed that combustion temperature and blending ratio significantly affected the burnout performance of MSW-SS blends， with combustion temperature playing a more prominent role. A relatively favorable combustion performance was achieved when the SS proportion was 20 %. Among the pollutants showing significant differences， N₂O and C₂H₄ were simultaneously affected by temperature， blending ratio， and their interaction， indicating higher sensitivity to operating conditions； CO and C₆H₆ were mainly affected by blending ratio； and C₇H₈ was significantly influenced by both temperature and blending ratio. N₂O and CH₄ were mainly released at the initial combustion stage， and increasing temperature markedly suppressed N₂O formation， while co-combustion generally reduced CH₄ emissions. A 20 % SS blend showed a relatively strong SO₂ reduction effect， and the synergistic reduction of NO was more pronounced at 950 °C. The emissions of CO， C₂H₄， C₆H₆， and C₇H₈ generally showed antagonistic behavior under co-combustion conditions. Among the tested models， the artificial neural network performed best in pollutant prediction， and combustion temperature， volatile matter， and fixed carbon contents of the blends were identified as important factors affecting pollutant release. Increasing temperature helped reduce global warming potential and photochemical ozone creation potential， whereas lowering the MSW proportion reduced photochemical ozone creation potential but increased global warming potential and acidification potential. Considering combustion performance， pollutant release characteristics， and environmental impact assessment together， an SS proportion of 20 % is recommended to achieve a better balance between combustion performance and environmental impacts during the co-combustion of MSW and SS.
- municipal solid waste /
- sewage sludge /
- co-combustion /
- machine learning /
- environmental impact assessment

HTML全文

参考文献(43)

[1]	CHEN G，SUN Y，CHEN C，et al. Thermogravimetric analysis and synergistic optimization of multi-organic solid wastes co-incineration[J]. Journal of Environmental Chemical Engineering，2025，13（3）：116557.
[2]	国家统计局. 中国统计年鉴2025[M]. 北京：中国统计出版社，2025. National Bureau of Statistics of China. China statistical yearbook 2025[M]. Beijing：China Statistics Press，2025.
[3]	中华人民共和国生态环境部. 2024年中国生态环境统计年报[R]. 北京：中国环境出版集团，2025. Ministry of Ecology and Environment，People’s Republic of China. Report on the state of the ecology and environment in China 2024[R]. Beijing：China Environment Publishing Group，2025.
[4]	JIAN X，LIU Y，YE Z L，et al. Influence of mandatory waste classification on environmental and economic impacts of residual waste treatment in Xiamen，China[J]. Waste Manag Res，2025，43（4）：555-567.
[5]	GAO H. Research on the components and characteristics of recyclable materials in Guangzhou city under the background of domestic waste classification[J]. Environmental Sanitation Engineering，2025，33（3）：90-96. 高慧. 垃圾分类背景下广州市可回收物组分与特性研究[J]. 环境卫生工程，2025，33（3）：90-96.
[6]	YUAN H，ZHAI S，FU H，et al. Environmental and economic life cycle assessment of emerging sludge treatment routes[J]. Journal of Cleaner Production，2024，449：141792.
[7]	LIN T，LIAO Y，DAI T，et al. Investigation on co-disposal technology of sludge and municipal solid waste based on numerical simulation[J]. Fuel，2023，343：127882.
[8]	CHEN L，LIAO Y，XIA Y，et al. Combustion characteristics of co-combusted municipal solid wastes and sewage sludge[J]. Energy Sources，Part A：Recovery，Utilization，and Environmental Effects，2020，46：1-13.
[9]	GAN Z，GUO Q，LIU T，et al. Optimization of sludge-derived fuel formulation based on co-combustion behaviors and artificial neural network modeling，with economic assessment[J]. Energy，2025，334：137750.
[10]	QI Y P，HE P J，LAN D Y，et al. Novel method for predicting concentrations of incineration flue gas based on waste composition and machine learning[J]. Journal of Environmental Management，2025，373：123588.
[11]	AHMAD M B，EMBAYE T M，DENG S，et al. Investigation on co-combustion behavior of domestic waste and sewage sludge using TG-FTIR-Py-GC/MS：Thermal behavior and gaseous pollutants emission[J]. Journal of the Energy Institute，2025，118：101933.
[12]	SUN Y，CHEN G，YAN B，et al. Behaviour of mercury during co-incineration of sewage sludge and municipal solid waste[J]. Journal of Cleaner Production，2020，253：119969.
[13]	HU Y，ZHANG H，NIU Y，et al. Intelligent control of sludge and municipal solid waste incineration using artificial neural network[J]. Energy Sources，Part A：Recovery，Utilization，and Environmental Effects，2025，47（2）：2526671.
[14]	SUN Z W，ZHANG Y X，TANG Y T，et al. Combustion characteristics and kinetic analysis of the co-combustion of MSW and excavated waste[J]. Environmental Sanitation Engineering，2023，31（6）：1-10. 孙子维，张雨轩，唐玉婷，等. 城市生活垃圾与陈腐垃圾掺烧的燃烧特性与反应动力学分析[J]. 环境卫生工程，2023，31（6）：1-10.
[15]	DING Y，ZHAO J，LIU J W，et al. A review of China’s municipal solid waste（MSW）and comparison with international regions：Management and technologies in treatment and resource utilization[J]. Journal of Cleaner Production，2021，293：126144.
[16]	VALIZADEH B，ABDOLI M A，DOBARADARAN S，et al. Risk control of heavy metal in waste incinerator ash by available solidification scenarios in cement production based on waste flow analysis[J]. Scientific Reports，2024，14（1）：6252.
[17]	WANG T，TANG J，XIA H，et al. Intelligent optimal control of furnace temperature for the municipal solid waste incineration process using multi-loop controller and particle swarm optimization[J]. Expert Systems with Applications，2024，257：125015.
[18]	LIU H，ZHANG S，FENG S，et al. Combustion characteristics and typical pollutant emissions of corn stalk blending with municipal sewage sludge[J]. Environmental Science and Pollution Research，2021，28（8）：9792-9805.
[19]	WANG Y，LIU Y，YANG W，et al. Evaluation of combustion properties and pollutant emission characteristics of blends of sewage sludge and biomass[J]. Science of the Total Environment，2020，720：137365.
[20]	SONG Y R，SUN Y N，ZHANG H N，et al. Incineration characteristics and kinetics of photocured 3D printing waste[J]. Environmental Engineering，2024，42（9）：292-300. 宋玉茹，孙昱楠，张闳楠，等. 光固化3D打印废物焚烧特性与动力学研究[J]. 环境工程，2024，42（9）：292-300.
[21]	YENENEH K，SUFE G. Experimental and ANN-based analysis of performance，combustion，and emission characteristics of a CI engine fueled with waste plastic oil-diethyl ether-diesel blends[J]. PLoS One，2026，21（2）：e0341627.
[22]	SIHLANGU E，LUSEBA D，REGNIER T，et al. Investigating methane，carbon dioxide，ammonia，and hydrogen sulphide content in agricultural waste during biogas production[J/OL]. Sustainability，2024，16（12）：5145. https：//doi.org/10.3390/su16125145.
[23]	BI H，WANG C，JIANG X，et al. Thermodynamics，kinetics，gas emissions and artificial neural network modeling of co-pyrolysis of sewage sludge and peanut shell[J]. Fuel，2021，284：118988.
[24]	LIU X，WEN Z，ASIM T，et al. Development of a deep learning-based framework for operational optimisation of municipal solid waste incinerators[J]. Energy Conversion and Management：X，2026，30：101610.
[25]	RICHARDSON G，FOREMAN N，KNUDBY A，et al. Global deep learning model for delineation of optically shallow and optically deep water in Sentinel-2 imagery[J]. Remote Sensing of Environment，2024，311：114302.
[26]	LIPS J，DEYOUNG S，SCHöNSTEINER M，et al. Closed-loop identification of a MSW grate incinerator using Bayesian Optimization for selecting model inputs and structure[J]. Control Engineering Practice，2024，153：106075.
[27]	WANG Y，HE P J，YU S Y，et al. Prediction of NOx emissions in waste-to-energy plants using machine learning models based on hybrid feature selection method[J]. Energy，2025，339：139146.
[28]	ZHAO Q，TANG W，HAN M，et al. Estimation of reduced greenhouse gas emission from municipal solid waste incineration with electricity recovery in prefecture-and county-level cities of China[J]. Science of the Total Environment，2023，875：162654.
[29]	GUO H W，XU H Y，NIE X Q，et al. Greenhouse gas emission inventory of urban and rural solid waste treatment in China[J]. Environmental Engineering，2023，41（S2）：286-290. 郭含文，徐海云，聂小琴，等. 我国城乡生活垃圾处理温室气体排放清单研究[J]. 环境工程，2023，41（增刊2）：286-290.
[30]	YANG L，HONG S. Impact of the clean energy structure of building operation on the co-benefits of CO₂ and air pollutant emission reductions in Chinese provinces[J]. Journal of Cleaner Production，2023，413：137400.
[31]	LI H，LI M，WANG H，et al. A review on migration and transformation of nitrogen during sewage sludge thermochemical treatment：Focusing on pyrolysis，gasification and combustion[J]. Fuel Processing Technology，2023，240：107562.
[32]	LIU S，FAN W. Formation characteristics of NO and N₂O during air-staged combustion of pulverized coal[J]. Fuel，2024，372：132093.
[33]	FAN H，HE K，XIONG J. Two-stage pyrolysis of municipal sewage sludge：Effect of cracking temperature[J]. Case Studies in Thermal Engineering，2025，75：107198.
[34]	SHEN J，MENG J，YAO B，LIANG J. Co-combustion of organic medical solid waste and municipal sewage sludge：Combustion characteristics and gaseous pollutant emissions[J]. Journal of Environmental Chemical Engineering，2025，13（5）：117363.
[35]	WANG X，XU J，LING P，et al. A study on the release characteristics and formation mechanism of SO₂ during co-combustion of sewage sludge and coal slime[J]. Fuel，2023，333：126511.
[36]	WU Y，ZHANG L，LIU M，et al. Synergistic reduction of NO/SO₂ through co-firing municipal sewage sludge with coal gangue[J]. Chemical Engineering Journal，2024，496：154025.
[37]	LIAO W，ZHANG X，FU Z，et al. Emission characteristics of typical gas pollutants during oxygen-enriched waste incineration process[J]. Environmental Technology& Innovation，2023，32：103358.
[38]	SUN C，WANG Z，YANG Y，et al. Characteristics，secondary transformation and odor activity evaluation of VOCs emitted from municipal solid waste incineration power plant[J]. Journal of Environmental Management，2023，326：116703.
[39]	YADAV V，YADAV A K，SINGH V，et al. Artificial neural network an innovative approach in air pollutant prediction for environmental applications：a review[J]. Results in Engineering，2024，22：102305.
[40]	YE G，WAN J，DENG Z，et al. Prediction of effluent total nitrogen and energy consumption in wastewater treatment plants：Bayesian optimization machine learning methods[J]. Bioresource Technology，2024，395：130361.
[41]	WANG T，TANG J，ALJERF L，et al. Emission reduction optimization of multiple flue gas pollutants in Municipal solid waste incineration power plant[J]. Fuel，2025，381：133382.
[42]	HOUDOU A，BADISY I EL，KHOMSI K，et al. Interpretable machine learning approaches for forecasting and predicting air pollution：a systematic review[J]. Aerosol and Air Quality Research，2024，24（1）：230151.
[43]	LIU B，YANG P，ZHOU H，et al. Environmental and economic performance of sewage sludge-dominated organic solid waste management strategies from a life cycle perspective：co-treatment versus mono-treatment[J]. Resources，Conservation and Recycling，2026，227：108737.