基于深度模糊推理模型的烟气脱硫设备运行状态预测

王功明; 李欣怡; 王自鹏; 张梦华; 韩红桂; 乔俊飞

doi:10.16383/j.aas.c250596

基于深度模糊推理模型的烟气脱硫设备运行状态预测

doi: 10.16383/j.aas.c250596 cstr: 32138.14.j.aas.c250596

王功明^{1, 2,},
李欣怡^{1, 2,},
王自鹏^{1, 2,},
张梦华^3,,
韩红桂^{1, 2,},
乔俊飞^{1, 2,}

1.
北京工业大学信息科学技术学院北京 100124
2.
北京工业大学智慧环保北京实验室北京 100124
3.
济南大学自动化与电气工程学院济南 250022

基金项目: 国家自然科学基金(62373018)资助, 北京市自然科学基金(4232043), 中国博士后科学基金(2025T180479)资助

详细信息

作者简介:
王功明：北京工业大学信息科学技术学院教授. 2019年获得北京工业大学博士学位. 主要研究方向为模式识别与智能系统和神经动力学驱动的智能特征建模与优化控制. 本文通信作者. E-mail: wanggm@bjut.edu.cn

李欣怡：北京工业大学信息科学技术学院硕士研究生. 2024年获得曲阜师范大学学士学位. 主要研究方向为神经动力学驱动的智能特征建模与优化控制. E-mail: XINYILI599@bjut.edu.cn

王自鹏：北京工业大学信息科学技术学院教授. 2017年获得北京航空航天大学博士学位. 主要研究方向为智能时空建模与控制, 容侵与容错安全控制和网络化控制系统. E-mail: zipengwang@bjut.edu.cn

张梦华：济南大学自动化与电气工程学院教授. 主要研究方向为机器人控制. E-mail: cse_zhanamh@uin.edu.cn

韩红桂：北京工业大学信息科学技术学院教授. 主要研究方向为城市污水处理过程智能优化控制和神经网络结构设计与优化. E-mail: rechardhan@bjut.edu.cn

乔俊飞：北京工业大学信息科学技术学院教授. 主要研究方向为神经网络, 智能系统和复杂工业过程建模与最优控制. E-mail: junfeiq@bjut.edu.cn

中图分类号: Y
计量
- 文章访问数: 11
- HTML全文浏览量: 7
- 被引次数: 0
出版历程
- 收稿日期: 2025-11-03
- 录用日期: 2026-03-31
- 网络出版日期: 2026-06-03

Prediction of Operating Status for Flue-Gas Desulfurization Equipment Based on Deep Fuzzy Inference Model

WANG Gong-Ming^{1, 2
,},
LI Xin-Yi^{1, 2
,},
WANG Zi-Peng^{1, 2
,},
ZHANG Meng-Hua^3
,,
HAN Hong-Gui^{1, 2
,},
QIAO Jun-Fei^{1, 2
,}

1.
School of Information Science and Technology, Beijing University of Technology, Beijing 100124
2.
Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124
3.
School of Automation and Electrical Engineering, University of Jinan, Jinan 250022

Funds: Supported by National Natural Science Foundation of China (62373018), Beijing Natural Science Foundation (4232043), and China Postdoctoral Science Foundation(2025T180479)

More Information

Author Bio:
WANG Gong-Ming　Professor at the School of Information Science and Technology, Beijing University of Technology. He received his PH.D. degree from Beijing University of Technology in 2019. His research interests include pattern recognition and intelligent systems and neurodynamic-driven intelligent feature modeling and optimal control. Corresponding author of this paper

LI Xin-Yi　Master＇s student at the School of Information Science and Technology, Beijing University of Technology. She received bachelor degree from Qufu Normal University in 2024. Her research interests include intelligent feature modeling and optimal control driven by neural dynamics

WANG Zi-Peng　Professor at the School of Information Science and Technology, Beijing University of Technology. He received his PH.D. degree from Beihang University in 2017. His research interests include intelligent spatiotemporal modeling and control, attack-tolerant, and fault-tolerant security control and networked control systems

ZHANG Meng-Hua　Professor at the School of Automation and Electrical Engineering, University of Jinan. Her main research interest is robot control

HAN Hong-Gui　Professor at the School of Information Science and Technology, Beijing University of Technology. His research interests include intelligent optimal control of municipal waste-water treatment process and structure design and optimization of neural networks

QIAO Jun-Fei　Professor at the School of Information Science and Technology, Beijing University of Technology. His research interests include neural networks, intelligent systems and modeling, and optimal control of complex industrial processes

摘要

摘要: 烟气脱硫处理是聚焦污染防治、实现节能减排的重要举措, 其安全、稳定运行直接影响着经济社会可持续发展与居民生命健康. 然而, 长期运行下的烟气脱硫设备往往不可避免地出现故障且难以预测, 进而导致连锁反应甚至事故. 鉴于此, 本文提出一种基于深度模糊推理模型(DFIM)的烟气脱硫设备运行状态预测方法. 首先, 构建数据驱动的深度模糊信念网络(DFBN), 通过模糊特征学习算法将传感器获取的原始数据映射为抗干扰的模糊语义特征, 并实现模糊特征的预测输出. 其次, 建立推理门控层, 进一步对输出模糊特征进行识别, 并聚焦设备故障模式. 然后, 结合操作经验和运行环境, 对标设备运行状态和故障模式, 给出处置方案. 最后, 以实际烟气脱硫系统循环泵内浆液pH和轴承温度为设备运行状态变量为例, 测试DFIM在烟气脱硫设备运行状态预测中的有效性, 实验结果表明所提出的DFIM在预测精度、稳定性与快速性等方面均优于现有方法, 对设备运维与管理具有重要的指导意义.
- 烟气脱硫过程 /
- 运行状态预测 /
- 深度模糊推理模型 /
- 深度模糊信念网络 /
- 推理门控层
Abstract: Flue-gas desulfurization treatment is an important measure to focus on pollution prevention and achieve emission reduction. The safe and stable operation of flue-gas desulfurization process directly affects the sustainable development of the economy and society as well as the health of residents. However, under long-term operation, faults inevitably occur in this equipment, which are difficult to be predicted and thus lead to a chain reaction and even accidents. In view of these problems, this paper proposes a deep fuzzy inference model (DFIM) to predict the operating status of flue-gas desulfurization equipment. First, a data-driven deep fuzzy belief network (DFBN) is constructed and trained by fuzzy feature learning, which can map the raw data obtained from the sensor into anti-interference fuzzy semantic features and further give the predicted outputs. Second, an inference gating layer is designed to recognize the output fuzzy features, focusing on the fault modes of flue-gas desulfurization equipment. Third, according to the operational experience and operating status as well as fault modes of the equipment, the optimal disposal plan is then provided. Finally, taking the PH of the slurry in the circulating pump and the temperature of the circulating pump bearing as the variables of the equipment operating status, the effectiveness of DFIM in the prediction of the operating status of flue-gas desulfurization equipment is tested. The experimental results show that the proposed DFIM outperforms the existing methods in terms of prediction accuracy, stability and rapidity, which has important guiding significance for the operation and maintenance as well as management of the operating equipment.
- Flue-gas desulfurization process /
- operating status prediction /
- deep fuzzy inference model /
- deep fuzzy belief network /
- inference gating layer

HTML全文

图 1 烟气脱硫过程工作流程图

Fig. 1 Flow chart of flue gas desulfurization

下载: 全尺寸图片幻灯片

图 2 基于DFIM的烟气脱硫设备状态预测

Fig. 2 Prediction of operating status for flue-gas desulfurization equipment based on DFIM

下载: 全尺寸图片幻灯片

图 3 DFIM对PH的训练结果

Fig. 3 Training results of PH using DFIM

下载: 全尺寸图片幻灯片

图 4 DFIM对轴承温度的训练结果

Fig. 4 Training results of bearing temperature using DFIM

下载: 全尺寸图片幻灯片

图 5 DFIM对PH的预测结果

Fig. 5 Prediction results of PH using DFIM

下载: 全尺寸图片幻灯片

图 6 DFIM对轴承温度的预测结果

Fig. 6 Prediction results of bearing temperature using DFIM

下载: 全尺寸图片幻灯片

图 7 模型对PH的预测结果对比

Fig. 7 Comparison of the prediction results of the model for PH

下载: 全尺寸图片幻灯片

图 8 模型对轴承温度的预测结果对比

Fig. 8 Comparison of the prediction results of the model for bearing temperature

下载: 全尺寸图片幻灯片

图 9 DFIM对PH的训练误差与预测误差对比

Fig. 9 Comparison results of training errors and prediction errors for PH using DFIM

下载: 全尺寸图片幻灯片

图 10 DFIM对轴承温度的训练误差与预测误差对比

Fig. 10 Comparison results of training errors and prediction errors for bearing temperature using DFIM

下载: 全尺寸图片幻灯片

图 11 DFIM对PH在训练集与预测集上的散点图

Fig. 11 Scatter plot on the training and prediction sets for PH using DFIM

下载: 全尺寸图片幻灯片

图 12 DFIM对轴承温度在训练与预测集上的散点图

Fig. 12 Scatter plot on the training and prediction sets for bearing temperature using DFIM

下载: 全尺寸图片幻灯片

图 13 DFIM建议运维策略与故障事件匹配的准确率

Fig. 13 Accuracy rate of matching operation and maintenance strategies suggested by DFIM with fault events

下载: 全尺寸图片幻灯片

表 1 离心泵运行状态关联变量

Table 1 Correlation variables of centrifugal pump

标号	单位	相关特征变量
x1	r/min	电机转速
x2	℃	入口浆液温度
x3	m³/h	浆液流量
x4	%	出口阀门开度
x5	℃	烟气温度
x6	A	泵运行电流
x7	mm/s	泵振动值
x8	kPa	入口压力
y1	-	浆液pH
y2	℃	轴承温度

下载: 导出CSV

表 2 事件定义

Table 2 Event definitions

事件	浆液pH	轴承温度	故障情况
A1	$A_{{\rm{PH}}}(1)$	$A_{{\rm{T}}}(1)$	酸性腐蚀加速
A2	$A_{{\rm{PH}}}(1)$	$A_{{\rm{T}}}(2)$	腐蚀
A3	$A_{{\rm{PH}}}(1)$	$A_{{\rm{T}}}(3)$	严重腐蚀
A4	$A_{{\rm{PH}}}(2)$	$A_{{\rm{T}}}(2)$	机械负载异常
A5	$A_{{\rm{PH}}}(2)$	$A_{{\rm{T}}}(3)$	轴承紧急故障
A6	$A_{{\rm{PH}}}(3)$	$A_{{\rm{T}}}(1)$	结垢初期
A7	$A_{{\rm{PH}}}(3)$	$A_{{\rm{T}}}(2)$	结垢
A8	$A_{{\rm{PH}}}(3)$	$A_{{\rm{T}}}(3)$	管路堵塞、过热
A9	$A_{{\rm{PH}}}(2)$	$A_{{\rm{T}}}(1)$	安全运行

下载: 导出CSV

表 3 运维建议

Table 3 Operation and maintenance suggestions

标签	运维建议
${\rm{Adv}}_1$	调整浆液配方, 添加石灰石等碱性物质, 将pH值回调至正常范围, 抑制酸性腐蚀.
${\rm{Adv}}_2$	降低设备负载, 必要时停机避免腐蚀扩散, 检查关键部件的腐蚀深度, 更换受损部件.
${\rm{Adv}}_3$	检查浆液流量、电机转速是否超出范围, 补充润滑油, 减少摩擦导致的负载增加.
${\rm{Adv}}_4$	立即停机, 切断电源, 防止轴承过热烧毁; 拆解轴承组件, 检查是否存在滚珠碎裂、保持架断裂等故障, 排查轴承过热原因.
${\rm{Adv}}_5$	调整浆液成分, 降低pH值至临界结垢点以下, 抑制碳酸钙/硫酸钙结晶, 对喷淋层喷嘴、浆液管道进行低压冲洗.
${\rm{Adv}}_6$	维持当前运行参数, 定期记录关键指标.

下载: 导出CSV

表 4 DFIM与其他模型对设备状态的预测结果对比

Table 4 Comparison results for predicting equipment states of DFIM and other models

参数	模型	MAE	R²
浆液PH	DFIM	0.12±0.05	0.89±0.07
	DFIM(无正则化)	0.16±0.09	0.83±0.09
	DBN	0.24±0.05	0.63±0.11
	LSTM	0.21±0.08	0.66±0.13
	SOFNN	0.18±0.10	0.71±0.15
	FNN	0.20±0.09	0.67±0.14
	RBFNN	0.23±0.06	0.65±0.12
	BPNN	0.26±0.12	0.62±0.19
轴承温度(℃)	DFIM	2.28±0.52	0.87±0.12
	DFIM(无正则化)	5.38±0.74	0.81±0.14
	DBN	7.63±0.61	0.56±0.17
	LSTM	7.15±0.76	0.59±0.18
	SOFNN	6.87±0.82	0.64±0.21
	FNN	6.95±0.79	0.62±0.20
	RBFNN	7.49±0.73	0.58±0.16
	BPNN	8.34±0.96	0.54±0.24

下载: 导出CSV

表 5 DFIM预测结果与触发的运维建议

Table 5 DFIM prediction results and triggered operation and maintenance suggestions

PH	温度(℃)	事件	运维	执行情况
6.1	52.87	A6	${\rm{Adv}}_5$	执行冲洗1次
5.7	74.98	A5	${\rm{Adv}}_4$	触发2次, 人工确认后停机1次
4.8	48.03	A1	${\rm{Adv}}_1$	在PH低于$B_{\mathrm{PH,\;low}}(t)$时执行3次
4.9	72.67	A3	${\rm{Adv}}_2$	触发1次, 人工确认后停机0次
5.4	46.6	A9	${\rm{Adv}}_6$	多次触发, 作为正常运行建议
5.7	61.89	A4	${\rm{Adv}}_3$	在负载异常预警下执行润滑补充2次

下载: 导出CSV

参考文献(27)

[1]	Wang G M, Li X Y, Gu K, Chen H, Han H G, Qiao J F. Neurodynamics-driven model predictive control with soft-measurement for desulfurization system. IEEE Transactions on Automation Science and Engineering, 2025, 22: 19546−19554 doi: 10.1109/TASE.2025.3594118
[2]	田昊, 汤健, 夏恒, 王天峥, 余文, 乔俊飞. 基于IT2FBLS强化学习PID的MSWI过程炉膛温度控制. 自动化学报, 2025, 51(7): 1626−1641 Tian Hao, Tang Jian, Xia Heng, Wang Tian-Zheng, Yu Wen, Qiao Jun-Fei. Furnace temperature control using IT2FBLS-based reinforcement learning PID for MSWI process. Acta Automatica Sinica, 2025, 51(7): 1626−1641
[3]	Wang G M, Chen H, Han H G, Bi J, Qiao J F, Tirkolaee E. Predicting water quality with nonstationarity: event-triggered deep fuzzy neural network. IEEE Transactions on Fuzzy Systems, 2024, 32(5): 2690−2699 doi: 10.1109/TFUZZ.2024.3354919
[4]	李欣怡, 王功明, 王自鹏, 乔俊飞. 基于事件触发的污水处理过程水质智能预测研究. 化工学报, 2025, 76(6): 2828−2837 Li Xin-Yi, Wang Gong-Ming, Wang Zi-Peng, Qiao Jun-Fei. Research on intelligent prediction of water quality in sewage treatment process based on event triggering. CIESC Journal, 2025, 76(6): 2828−2837
[5]	李征, 庄铠泽, 赵东杰, 宋燕星, 王功明. 事件驱动的深度信念网络软测量模型设计方法. 化工学报, 2025, 76(4): 1693−1701 Li Zheng, Zhuang Kai-Ze, Zhao Dong-Jie, Song Yan-Xing, Wang Gong-Ming. Design method of event-driven deep belief network soft-sensing model. CIESC Journal, 2025, 76(4): 1693−1701
[6]	Yuan X F, Qi S B, Wang Y L. Stacked enhanced auto-encoder for data-driven soft sensing of quality variable. IEEE Transactions on Instrumentation and Measurement, 2020, 69(10): 7953−7961 doi: 10.1109/TIM.2020.2985614
[7]	王功明, 乔俊飞, 关丽娜, 贾庆山. 深度信念网络研究现状与展望. 自动化学报, 2021, 47(1): 35−49 Wang Gong-Ming, Qiao Jun-Fei, Guan Li-Na, Jia Qing-Shan. Review and prospect on deep belief network. Acta Automatica Sinica, 2021, 47(1): 35−49
[8]	韩红桂, 王玉爽, 刘峥, 孙浩源, 乔俊飞. 知识和数据驱动的污水处理反硝化脱氮过程协同优化控制. 自动化学报, 2024, 50(6): 1221−1233 Han Hong-Gui, Wang Yu-Shuang, Liu Zheng, Sun Hao-Yuan, Qiao Jun-Fei. Knowledge-data-driven cooperative optimal control for wastewater treatment denitrification process. Acta Automatica Sinica, 2024, 50(6): 1221−1233
[9]	Yi H, Su Z G, Wang P H. Closed-loop shaping-based active disturbance rejection control with delay robustness and application to wet desulfurization process. IEEE Transactions on Industrial Electronics, 2024, 71(6): 6117−6127 doi: 10.1109/TIE.2023.3290252
[10]	Wang G M, Zhao Y D, Liu C X, Qiao J F. Data-driven robust adaptive control with deep learning for wastewater treatment process. IEEE Transactions on Industrial Informatics, 2024, 20(1): 149−157 doi: 10.1109/TII.2023.3257296
[11]	Yang W W, Tang J, Tian H, Yu W, Qiao J F. Flue gas oxygen content control based on interval type-2 fuzzy broad learning system and PID for MSWI process. IEEE Transactions on Automation Science and Engineering, 2025, 22: 15473−15487 doi: 10.1109/TASE.2025.3563863
[12]	朱霁霖, 桂卫华, 蒋朝辉, 陈致蓬, 方怡静. 基于料面视频图像分析的高炉异常状态智能感知与识别. 自动化学报, 2024, 50(7): 1345−1362 doi: 10.16383/j.aas.c230674 Zhu Ji-Lin, Gui Wei-Hua, Jiang Zhao-Hui, Chen Zhi-Peng, Fang Yi-Jing. Intelligent perception and recognition of blast furnace anomalies via burden surface video image analysis. Acta Automatica Sinica, 2024, 50(7): 1345−1362 doi: 10.16383/j.aas.c230674
[13]	黄如意, 李霁蒲, 王震, 夏景演, 陈祝云, 李巍华. 基于多任务学习的工业设备智能诊断与预测方法. 中国科学: 技术科学, 2022, 52(1): 123−137 Huang Ru-Yi, Li Ji-Pu, Wang Zhen, Xia Jing-Yan, Chen Zhu-Yun, Li Wei-Hua. Intelligent diagnostic and prognostic method based on multitask learning for industrial equipment. Scientia Sinica Technologica, 2022, 52(1): 123−137
[14]	Emroozi V, Kazemi M, Doostparast M, Pooya A. Improving industrial maintenance efficiency: A Holistic Approach to Integrated production and maintenance planning with human error optimization. Process Integration and Optimization for Sustainability, 2024, 8: 539−564 doi: 10.1007/s41660-023-00374-3
[15]	Liu S, Zhong W Q, Chen X, Sun L, Yang L K. Multi-objective economic model predictive control using utopia-tracking for the wet flue gas desulphurization system. Chinese Journal of Chemical Engineering, 2023, 54: 343−352 doi: 10.1016/j.cjche.2022.03.014
[16]	Wang Q Y, Zhao H, Zhao Q L, Hou J, Tian S L, Li Y J, et al. Prediction of SO₂ emission concentration in industrial flue gas based on deep learning: The ammonia desulfurization system of the Yunnan aluminum carbon plant as the research object. Process Safety and Environmental Protection, 2024, 185: 340−349 doi: 10.1016/j.psep.2024.03.042
[17]	Chen H S, Liu H, Chu X N, Liu Q X, Xue D Y. Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network. Renewable Energy, 2021, 172: 829−840 doi: 10.1016/j.renene.2021.03.078
[18]	伍小龙, 姜韦宇, 韩红桂, 李佟, 赵德政. 基于随机事件代理模型的污水处理鼓风机轴承温度智能预测. 中国科学: 技术科学, 2025, 55: 879−892 doi: 10.1360/SST-2024-0251 Wu Xiao-Long, Jiang Wei-Yu, Han Hong-Gui, Li Tong, Zhao De-Zheng. Intelligent prediction of wastewater treatment blower bearing temperature based on random event agent model. Scientia Sinica Technologica, 2025, 55: 879−892 doi: 10.1360/SST-2024-0251
[19]	陈俊英, 席月芸, 李朝阳. 多尺度局部特征和Transformer全局学习融合的发动机剩余寿命预测. 自动化学报, 2024, 50(9): 1818−1830 doi: 10.16383/j.aas.c230634 Chen Jun-Ying, Xi Yue-Yun, Li Zhao-Yang. Prediction of aeroengine remaining life by combining multi-scale local features and Transformer global learning. Acta Automatica Sinica, 2024, 50(9): 1818−1830 doi: 10.16383/j.aas.c230634
[20]	Han H G, Sun C X, Wu X L, Yang H Y, Zhao N, Li J, Qiao J F. Dynamic-static model for monitoring wastewater treatment processes. Control Engineering Practice, 2023, 132: Article No.105424 doi: 10.1016/j.conengprac.2022.105424
[21]	Han H G, Wang J Q, Liu Z, Yang H Y, Qiao J F. Self-organizing robust fuzzy neural network for nonlinear system modeling. IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(1): 911−923 doi: 10.1109/TNNLS.2023.3334150
[22]	Menegatti D, Giuseppi A, Pietrabissa A. Tractable data-driven model predictive control using one-step neural networks predictors. IEEE Transactions on Automation Science and Engineering, 2025, 22: 6740−6751 doi: 10.1109/TASE.2024.3453668
[23]	Wang G M, Jia Q S, Zhou M C, Bi J, Qiao J F. Soft-sensing of wastewater treatment process via deep belief network with event-triggered learning. Neurocomputing, 2021, 436: 103−113 doi: 10.1016/j.neucom.2020.12.108
[24]	Wang G M, Jia Q S, Qiao J F, Bi J, Liu C X. A sparse deep belief network with efficient fuzzy learning framework. Neural Networks, 2020, 121: 430−440 doi: 10.1016/j.neunet.2019.09.035
[25]	Bandara K, Bergmeir C, Hewamalage H. LSTM-MSNet: Leveraging forecasts on sets of related time series with multiple seasonal Patterns. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(4): 1586−1599 doi: 10.1109/TNNLS.2020.2985720
[26]	Rivest F, Kohar R. A new timing error cost function for binary time series prediction. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(1): 174−185 doi: 10.1109/TNNLS.2019.2900046
[27]	Li Y H, Anastasiu D C. MC-ANN: A mixture clustering-based attention neural network for time series forecasting. IEEE transactions on pattern analysis and machine intelligence, 2025, 47(8): 6888−6899 doi: 10.1109/TPAMI.2025.3565224