基于多目标粒子群算法的城市固废焚烧过程智能操作优化

蒙西; 侯启正; 乔俊飞

doi:10.16383/j.aas.c240044

基于多目标粒子群算法的城市固废焚烧过程智能操作优化

doi: 10.16383/j.aas.c240044

蒙西^{1, 2, 3,},
侯启正^{1, 2, 3,},
乔俊飞^{1, 2, 3,}

1.
北京工业大学信息学部北京 100124
2.
智慧环保北京实验室北京 100124
3.
智能感知与自主控制教育部工程研究中心北京 100124

基金项目: 国家自然科学基金(62273013, 62021003), 北京市科技新星计划(20230484310), 科技创新2030“新一代人工智能”重大项目(2021ZD0112301)资助

详细信息

作者简介:
蒙西：北京工业大学信息学部副教授. 主要研究方向为人工神经网络结构分析与设计, 城市固废焚烧过程智能优化控制. 本文通讯作者. E-mail: mengxi@bjut.edu.cn

侯启正：北京工业大学信息学部硕士研究生. 主要研究方向为城市固废焚烧过程智能操作优化. Email: houqizheng@emails.bjut.edu.cn

乔俊飞：北京工业大学信息学部教授. 主要研究方向为计算智能与智能优化控制, 环保自动化. Email: adqiao@bjut.edu.cn

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出版历程
- 收稿日期: 2024-01-17
- 录用日期: 2024-05-16
- 网络出版日期: 2024-08-17

Intelligent Operational Optimization of Municipal Solid Waste Incineration Process Based on Multi-objective Particle Swarm Algorithm

MENG Xi^{1, 2, 3
,},
HOU Qi-Zheng^{1, 2, 3
,},
Qiao Jun-Fei^{1, 2, 3
,}

1.
Faculty of Information Technology, Beijing University of Technology, Beijing 100124
2.
Beijing Laboratory of Smart Environmental Protection, Beijing 100124
3.
Engineering Research Center of Intelligent Perception and Autonomous Control, Ministry of Education, Beijing 100124

Funds: Supported by National Natural Science Foundation of China (62273013, 62021003), Beijing Nova Program (20230484310), and the National Key Research and Development Project of China (2021ZD0112301)

More Information

Author Bio:
MENG Xi　Associate professor at the Faculty of Information Technology, Beijing University of Technology. Her research interest covers analysis and design of artificial neural network structure and intelligent optimization control of municipal solid waste incineration process. Corresponding author of this paper

HOU Qi-Zheng　Master student at the Faculty of Information Technology, Beijing University of Technology. Her research interest covers intelligent operational optimization of municipal solid waste incineration process

QIAO Jun-Fei　Professor at the Faculty of Information Technology, Beijing University of Technology. His research interest covers computational intelligence and intelligent optimization control, as well as environmental automation

摘要

摘要: 城市固废焚烧技术因兼具减量化、无害化、资源化等特点, 已成为治理固废污染的主要方式. 由于城市固废成分复杂, 含水率、热值动态波动, 固废燃烧、余热利用、烟气净化等环节耦合冲突, 实际工业过程难以高效运行. 为此, 文中提出了一种基于多目标粒子群算法的城市固废焚烧过程智能操作优化方法, 以期实现燃烧效率和烟气净化效率的协同优化. 首先, 设计自组织径向基函数神经网络建立运行指标模型, 实现城市固废焚烧过程运行性能的在线评价; 其次, 引入区域拥挤度指标提出了一种改进的多目标粒子群优化算法, 以获取操作变量的Pareto解集; 然后, 利用熵权法确定操作变量最佳设定值, 实现城市固废焚烧过程高效运行; 最后, 通过北京某城市固废焚烧厂的实际运行数据对所提方法进行验证, 实验结果表明基于多目标粒子群算法的智能操作优化方法可以实现燃烧效率与脱硝效率的协同提升.
- 城市固废焚烧过程 /
- 燃烧效率 /
- NOx排放 /
- 运行指标模型 /
- 多目标粒子群算法
Abstract: Municipal solid waste incineration (MSWI) technology has become the main way to address solid waste pollution due to its characteristics of reduction, harmlessness, and resource utilization. However, it is difficult for actual industries to operate efficiently due to the complex composition of municipal solid waste, dynamic fluctuations in moisture content and calorific value, coupling conflicts in solid waste combustion, waste heat utilization and flue gas purification. To enhance combustion efficiency and flue gas purification efficiency, this paper proposes an intelligent operational optimization method of MSWI process based on multi-objective particle swarm algorithm. First, operational index models are established by designing self-organizing radial basis function neural networks to achieve online evaluation of operational performance in MSWI process. Second, an improved multi-objective particle swarm optimization algorithm is presented by incorporating regional congestion degree index to obtain the Pareto solutions of operating variables. Then, the entropy weight method is employed to determine the optimal set value of operating variables, achieving efficient operation of MSWI process. Finally, the proposed method is verified through actual operational data from a MSWI plant in Beijing, and the experimental results demonstrate that the intelligent operational optimization method based on multi-objective particle swarm algorithm can improve combustion efficiency and reduce nitrogen oxide emissions.
- Municipal solid waste incineration process /
- combustion efficiency /
- NOx emissions /
- operational index model /
- multi-objective particle swarm algorithm

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图 1 城市固废焚烧过程智能操作优化方法框架

Fig. 1 The framework for intelligent operational optimization method of municipal solid waste incineration process

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图 2 RBF神经网络结构

Fig. 2 RBF neural network structure

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图 3 MOPSO-RC算法流程图

Fig. 3 The flowchart of MOPSO-RC algorithm

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图 4 确定全局最优领导者示意图

Fig. 4 The diagram of determining the global optimal leader

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图 5 NOx指标模型结果

Fig. 5 The results of NOx index model

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图 7 CO₂指标模型结果

Fig. 7 The results of CO₂ index model

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图 6 CO指标模型结果

Fig. 6 The results of CO index model

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图 8 NOx排放的优化结果

Fig. 8 Optimization results of NOx emissions

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图 9 燃烧效率的优化结果

Fig. 9 Optimization results of CE

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图 10 炉排速度设定

Fig. 10 The settings of grate speed

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图 13 二次风入口流量设定

Fig. 13 The settings of the inlet flow of secondary air

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图 14 不同算法对 NOx排放的优化结果

Fig. 14 Optimization results of NOx emissions using different algorithms

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图 15 不同算法对CE的优化结果

Fig. 15 Optimization results of CE using different algorithms

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图 11 一次风入口流量设定

Fig. 11 The settings of the inlet flow of primary air

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图 12 一次风出口压力设定

Fig. 12 The settings of the outlet pressure of primary air

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表 1 实验数据基本信息

Table 1 Basic information of experimental data

变量名称	取值范围	单位
炉排速度	30.05 ~ 30.08	%
一次风入口流量	47698.32 ~ 63444.03	m³/h
一次风出口压力	1988.02 ~ 3189.73	pa
二次风入口流量	4924.86 ~ 5124.70	m³/h
NOx排放浓度	79.21 ~ 231.27	mg/m³
CO排放浓度	0.39 ~ 7.38	mg/m³
CO₂排放浓度	4.80 ~ 6.55	mg/m³

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表 2 不同指标模型精度

Table 2 Accuracy of different index models

方法	NOx指标模型		CO指标模型		CO₂指标模型
方法	RMSE	MAPE (%)	RMSE	MAPE (%)	RMSE	MAPE (%)
LSSVR	11.2729	7.0716	0.6281	17.8164	0.1970	2.6587
Kriging	10.3483	6.1612	0.5376	15.5635	0.1967	2.5834
RBF	11.6462	7.3384	0.5595	16.5597	0.1908	2.4366
SORBF	9.3939	5.5603	0.5368	15.4444	0.1866	2.2523

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表 3 不同算法优化结果均值比较

Table 3 Comparison of mean optimization results using different algorithms

不同优化算法	NOx排放(mg/m³)	燃烧效率(%)
实际运行	133.840 2	79.274 0
NSGAII	123.567 4	84.460 1
MOPSO	121.792 7	83.122 0
MOPSO-CD	119.997 0	84.890 7
MOPSO-RC	113.462 5	85.299 0

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