城市固废焚烧过程数据驱动建模与自组织控制

丁海旭; 汤健; 乔俊飞

doi:10.16383/j.aas.c220570

城市固废焚烧过程数据驱动建模与自组织控制

doi: 10.16383/j.aas.c220570

丁海旭^{1, 2,},
汤健^{1, 2,},
乔俊飞^{1, 2,}

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

基金项目: 科技创新2030 — “新一代人工智能”重大项目 (2021ZD0112300), 国家自然科学基金创新群体项目 (62021003), 国家科技重大专项 (61890930), 国家自然科学基金(62073006), 北京市自然科学基金(4212032, 4192009)资助

详细信息

作者简介:
丁海旭：北京工业大学信息学部博士研究生. 主要研究方向为城市固废焚烧过程特征建模与智能控制. E-mail: dinghaixu@emails.bjut.edu.cn

汤健：北京工业大学信息学部教授. 主要研究方向为小样本数据建模, 城市固废处理过程智能控制. E-mail: freeflytang@bjut.edu.cn

乔俊飞：北京工业大学信息学部教授. 主要研究方向为智能控制与智能信息处理, 环境感知和保护自动化技术. 本文通信作者. E-mail: adqiao@bjut.edu.cn

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出版历程
- 收稿日期: 2022-07-12
- 录用日期: 2022-09-01
- 网络出版日期: 2022-10-26
- 刊出日期: 2023-03-20

Data-driven Modeling and Self-organizing Control of Municipal Solid Waste Incineration Process

DING Hai-Xu^{1, 2
,},
TANG Jian^{1, 2
,},
QIAO Jun-Fei^{1, 2
,}

1.
Faculty of Information Technology, Beijing University of Technology, Beijing 100124
2.
Beijing Laboratory of Smart Environmental Protection, Beijing 100124

Funds: Supported by Science and Technology Innovation 2030 — “New Generation Artificial Intelligence” Major Project of China (2021ZD0112300), Innovative Research Group Project of the National Natural Science Foundation of China (62021003), National Science and Technology Major Project of China (61890930), National Natural Science Foundation of China (62073006), and Natural Science Foundation of Beijing (4212032, 4192009)

More Information

Author Bio:
DING Hai-Xu　Ph.D. candidate at the Faculty of Information Technology, Beijing University of Technology. His research interest covers feature modeling and intelligent control of municipal solid waste incineration process

TANG Jian　Professor at the Faculty of Information Technology, Beijing University of Technology. His research interest covers small sample data modeling and intelligent control of municipal solid waste treatment process

QIAO Jun-Fei　Professor at the Faculty of Information Technology, Beijing University of Technology. His research interest covers intelligent control and intelligent information processing, environmental awareness, and protection automation technology. Corresponding author of this paper

摘要

摘要: 城市固废焚烧(Municipal solid waste incineration, MSWI)是处置城市固废(Municipal solid waste, MSW) 的主要手段之一. 中国MSW来源范围广、组分复杂、热值波动大, 其焚烧过程通常依靠人工干预, 这导致MSWI过程智能化水平较低且难以满足日益提升的控制需求. MSWI具有多变量耦合、工况漂移等诸多不确定性特征, 因而难以建立其被控对象模型并设计在线控制器. 针对以上问题, 提出了一种面向MSWI过程的数据驱动建模与自组织控制方法. 首先, 构建了基于多输入多输出Takagi Sugeno 模糊神经网络(Multi-input multi-output Takagi Sugeno fuzzy neural network, MIMO-TSFNN) 的被控对象模型; 然后, 设计了基于多任务学习的自组织模糊神经网络控制器(Multi-task learning self-organizing fuzzy neural network controller, MTL-SOFNNC)用于同步控制炉膛温度与烟气含氧量, 其通过计算神经元的相似度与多任务学习(Multi-task learning, MTL)能力对控制器结构进行自组织调整; 接着, 通过Lyapunov定理对MTL-SOFNNC稳定性进行了证明; 最后, 通过北京市某MSWI厂的过程数据验证了模型与控制器的有效性.
- 城市固废焚烧 /
- 多任务学习 /
- 自组织控制 /
- 数据驱动建模 /
- 模糊神经网络
Abstract: Municipal solid waste incineration (MSWI) is one of the main means to dispose of municipal solid waste (MSW). MSW in China has a wide range of sources, complex components, and large fluctuations in calorific value. Its incineration process usually relies on manual intervention. This will lead to a low degree of intelligence in the MSWI process and it is difficult to meet the increasing control requirements. MSWI has many uncertain characteristics such as multivariable coupling and working condition drift, so it is difficult to build the model of controlled object and design the on-line controller. To solve the above problems, this paper proposes a data-driven modeling and self-organizing control method for MSWI process. Firstly, the model of controlled object based on multi-input multi-output Takagi Sugeno fuzzy neural network (MIMO-TSFNN) is constructed. Secondly, a multi-task learning self-organizing fuzzy neural network controller (MTL-SOFNNC) is designed to synchronously control the furnace temperature and flue gas oxygen content, which can self-organize the structural parameters of the controller by calculating the similarity of neurons and the ability of multi-task learning (MTL). Meanwhile, the stability of MTL-SOFNNC is proved by Lyapunov theorem. Finally, the effectiveness of the model and controller is verified by the process data of an MSWI plant in Beijing.
- Municipal solid waste incineration (MSWI) /
- multi-task learning (MTL) /
- self-organizing control /
- data-driven modeling /
- fuzzy neural network

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图 1 MSWI的工艺流程

Fig. 1 MSWI process flow

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图 2 MIMO-TSFNN模型

Fig. 2 MIMO-TSFNN model

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图 3 基于MTL-SOFNNC的MSWI控制系统

Fig. 3 MSWI control system based on MTL-SOFNNC

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图 4 基于MTL-SOFNNC的控制框架

Fig. 4 Control structure based on MTL-SOFNNC

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图 5 MSWI执行机构和检测设备

Fig. 5 MSWI actuators and detection equipment

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图 6 模型训练过程的RMSE

Fig. 6 RMSE of the model training process

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图 7 测试样本拟合效果

Fig. 7 Testing samples fitting effect

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图 8 MTL-SOFNNC的结构自组织过程

Fig. 8 Self-organizing process of MTL-SOFNNC structure

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图 9 操作变量的自校正过程

Fig. 9 Self-tuning process of manipulated variables

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图 10 多变量跟踪控制结果

Fig. 10 Multi-variables control tracking results

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图 11 多变量跟踪控制误差

Fig. 11 Multi-variables control tracking errors

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表 1 实验对象的运行范围

Table 1 Operating range of experimental subjects

变量名	运行范围	单位
一次风总流量	40 ~ 100	km³N/h
干燥炉排速度百分比	0 ~ 100	%
二次风流量	0 ~ 30	km³N/h
炉膛温度	850 ~ 1050	℃
烟气含氧量	2 ~ 14	%
主蒸汽流量	65 ~ 85	t/h

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表 2 被控对象建模效果评价

Table 2 Evaluation of modeling effect of controlled object model

被控模型	评价指标		炉膛温度模型	烟气含氧量模型	主蒸汽流量模型
MIMO-TSFNN	训练	RMSE	3.88 ℃	0.30%	0.43 t/h
	训练	APE	0.27%	3.16%	0.45%
	测试	RMSE	4.18 ℃	0.58%	0.49 t/h
	测试	APE	0.31%	6.97%	0.59%

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表 3 MSWI过程多变量控制器性能比较

Table 3 Performance comparison of multi-variable controllers for MSWI process

控制器	神经元个数	炉膛温度				烟气含氧量
控制器	神经元个数	IAE	ISE	$ \bar{\sigma}{\text{%}} $	$\bar{t}_r \;({\rm{s} })$	IAE	ISE	$ \bar{\sigma} {\text{%}}$	$\bar{t}_r\; ({\rm{s} })$
MTL-SOFNNC	10	1.883	28.828	0.39%	23.93	0.151	0.124	3.12%	21.47
M-DSNNC	21	2.379	29.374	0.58%	25.68	0.188	0.150	3.14%	29.47
SOFC	20	2.464	30.229	0.46%	30.43	0.194	0.152	3.92%	29.37
SOTSFNNC	17	2.872	30.414	0.72%	23.38	0.214	0.136	4.13%	27.72
TSFNNC	20	2.854	30.728	0.75%	24.14	0.217	0.151	5.45%	34.77

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