数字孪生驱动的热交换器降阶建模及智能感知方法研究

doi:10.11949/0438-1157.20230707

化工学报 ›› 2023, Vol. 74 ›› Issue (10): 4218-4228.DOI: 10.11949/0438-1157.20230707

数字孪生驱动的热交换器降阶建模及智能感知方法研究

籍帅航¹^,³(), 王金江¹^,²^,³(), 蔡睿¹^,³, 孙雪皓²^,³, 葛伟凤⁴

^1.中国石油大学（北京）人工智能学院，北京 102249
^2.中国石油大学（北京）安全与海洋工程学院，北京 102249
^3.应急管理部油气生产安全与应急技术重点实验室，北京 102249
^4.中海油安全技术服务有限公司，天津 300456

收稿日期:2023-07-07 修回日期:2023-09-10 出版日期:2023-10-25 发布日期:2023-12-22
通讯作者: 王金江
作者简介:籍帅航（1999—），男，博士研究生，jishh@student.cup.edu.cn
基金资助:
国家自然科学基金重点项目(52234007)

Research on reduced order modeling and intelligent sensing method for heat exchangers driven by digital twin

Shuaihang JI¹^,³(), Jinjiang WANG¹^,²^,³(), Rui CAI¹^,³, Xuehao SUN²^,³, Weifeng GE⁴

^1.College of Artificial Intelligence, China University of Petroleum, Beijing 102249, China
^2.College of Safety and Ocean Engineering, China University of Petroleum, Beijing 102249, China
^3.Key Laboratory of Oil and Gas Safety and Emergency Technology, Ministry of Emergency Management, Beijing 102249, China
^4.CNOOC Safety & Technology Services Co. , Ltd. , Tianjin 300456, China

Received:2023-07-07 Revised:2023-09-10 Online:2023-10-25 Published:2023-12-22
Contact: Jinjiang WANG

摘要/Abstract

摘要：

管壳式热交换器是能源系统中的重要组成部分，长时间的运行容易在导热管内造成结垢故障，导致热交换器传热效率下降、流动阻力增加、耗能增加、系统压力下降等问题。结垢故障往往隐藏在设备内部，通过运行数据监测或者仿真手段往往不足以感知和预测多工况下的设备状态，数字化的热交换器状态监测技术为解决问题提供了新思路，但存在数字孪生体难以构建、降阶效果不理想、结垢数据难以获取等问题。为了能够建立数字孪生驱动的热交换器高保真降阶模型，提出了一种基于本征正交分解的径向基自适应模型降阶方法。基于物理信息的自适应采样算法采集更有效的样本数据，利用POD-RBF建立高保真降阶模型，开展热交换器的结垢故障的仿真实验，通过BP神经网络进行热交换器的结垢感知和预测。实验结果表明所建立的自适应采样降阶模型与不使用采样的降阶模型相比求解效率提高了1倍，与全阶模型的误差在4%左右，通过降阶模型快速生成更符合物理机理的结垢数据，预测误差保持在0.0554 mm左右，能有效地对换热器的结垢进行感知和预测。

关键词: 热交换器, 数字孪生, 模型降阶, 状态监测, 故障诊断

Abstract:

Shell and tube heat exchangers are important part of the energy system and are prone to fouling failures in the heat conduction tubes over long periods of operation, resulting in reduced heat transfer efficiency, increased flow resistance, increased energy consumption and reduced system pressure, etc. Fouling failures are often hidden within the equipment, and monitoring through operational data or simulation is often insufficient to sense and predict the equipment status under multiple operating conditions. Digital heat exchanger condition monitoring techniques are relatively lacking. In order to establish a digital twin-driven high-fidelity downscaling model for heat exchangers, a radial basis adaptive model downscaling method based on proper orthogonal decomposition is proposed in this paper. The adaptive sampling algorithm based on physical information collects more effective sample data and establishes a high-fidelity reduced-order model of the heat exchanger by POD-RBF, conducts simulation experiments on fouling faults in heat exchangers, and perceives and predicts fouling in heat exchangers using BP neural networks. The experimental results show that the established adaptive sampling reduced-order model improves the solution efficiency by a factor of 1 compared with the reduced-order model without sampling, and the error is about 4% compared with the full-order model, and the fouling data is quickly generated by the reduced-order model which is more consistent with the physical mechanism, and the prediction error is kept at about 0.0554 mm, which can effectively sense and predict the heat exchanger fouling.

Key words: heat exchanger, digital twin, model order reduction, condition monitoring, fault diagnosis

中图分类号:

TQ 051.5

籍帅航, 王金江, 蔡睿, 孙雪皓, 葛伟凤. 数字孪生驱动的热交换器降阶建模及智能感知方法研究[J]. 化工学报, 2023, 74(10): 4218-4228.

Shuaihang JI, Jinjiang WANG, Rui CAI, Xuehao SUN, Weifeng GE. Research on reduced order modeling and intelligent sensing method for heat exchangers driven by digital twin[J]. CIESC Journal, 2023, 74(10): 4218-4228.

图/表 11

图1 数字孪生驱动的智能感知方法框架

Fig.1 Framework for intelligent perception methods driven by digital twin

图2 基于物理信息的自适应采样算法

Fig.2 Adaptive sampling algorithm based on physical information

图3 数字孪生驱动的智能感知方法框架

Fig.3 A framework for a digital twin-driven approach to intelligent perception

表1 结构参数及物性参数

Table 1 Structural parameter and physical property parameters

参数	数值	参数	数值
壳体内径/m	0.160	铝密度/(kg/m³)	2719
壳体厚度/m	0.01	铝比热容/(J/(kg·℃))	871
换热面积/m²	0.78	铝热导率/(W/(m·℃))	202.4
换热管长/m	1	液态水密度/(kg/m³)	998.2
换热管外径/m	0.012	液态水比热容/(J/(kg·℃))	4182
换热管壁厚/m	0.0016	液态水热导率/(W/(m·℃))	0.6
换热管排列方式	正三角形	液态水动力黏度/(Pa·s)	0.001003
换热管数量	26	碳酸钙密度/(kg/m³)	2800
折流板数量	3	碳酸钙比热容/(J/(kg·℃))	856
		碳酸钙热导率/(W/(m·℃))	2.25

表2 热交换器仿真工况设置

Table 2 Heat exchanger simulation conditions settings

项目	入口温度/℃	入口压力/MPa	流量/（m³/h）	介质
管程	60	0.1	0.8	水
壳程	25	0.1	0.5	水

图4 换热器截面温度分布云图

Fig.4 Cross section temperature distribution nephogram of heat exchanger

表3 降阶模型性能对比

Table 3 Model performance comparison of reduced order models

采样方法	构建快照时间/s	模型训练时间/s	模型推理时间/s	MAPE
不使用采样	4.2087	0.0656	0.0010	0.0425
LHS	136.6628	0.0330	0.0010	0.0430
RS	3.8994	0.0337	0.0010	0.2585
PAS	3.9554	0.0331	0.0010	0.0408

表4 奇异值累计值占比

Table 4 Percentage of cumulative values of singular values

序号	奇异值	奇异值累计值占比/%
1	3.4356×10^-3	99.25
2	2.3597×10^-5	99.94
3	1.9781×10^-6	99.99
4	1.2714×10^-7	99.99

图5 不同截断奇异值下MAPE以及奇异值累计值

Fig.5 MAPE and cumulative values of singular values at different truncated singular values

图6 结垢厚度为0.33 mm时温度场的误差曲线

Fig.6 Error curve of the temperature field for a scale thickness of 0.33 mm

表5 基于BP模型的热交换器结垢厚度预测结果

Table 5 Prediction results of fouling thickness in heat exchangers based on BP model

工况	实际厚度/mm	预测厚度/mm	相对误差/%
1	1.794	1.736	3.23
2	1.347	1.394	3.48
3	0.824	0.867	5.21
4	0.198	0.185	6.57

参考文献 32

1	李宁. 天然气管道压气站燃机排气余热用于进气冷却系统设计及其经济性分析[D]. 北京: 中国石油大学(北京), 2019.
	Li N. Design and economic analysis of gas turbine exhaust waste heat used in intake cooling system of natural gas pipeline compressor station[D]. Beijing: China University of Petroleum, 2019.
2	杨政华. 基于频域设计方法的柔性换热网络旁路控制系统研究[D]. 北京: 中国石油大学(北京), 2019.
	Yang Z H. Research on bypass control system of flexible heat exchanger network based on frequency domain design method[D]. Beijing: China University of Petroleum, 2019.
3	杨鹏飞. 常减压装置及其换热网络的柔性优化[D]. 北京: 中国石油大学(北京), 2020.
	Yang P F. Flexibility optimization of atmospheric and vacuum distillation unit and its heat exchange network[D]. Beijing: China University of Petroleum, 2020.
4	马仲麟. 带换热式的旋风分离器性能研究[D]. 北京: 中国石油大学(北京), 2021.
	Ma Z L. Performance research of cyclone separator with heat exchange[D]. Beijing: China University of Petroleum, 2021.
5	王右磊. 级联冷却系统优化及级联温度分割点确定方法研究[D]. 北京: 中国石油大学(北京), 2021.
	Wang Y L. Study on optimization of cascade cooling system and determination of cascade temperature split point[D]. Beijing: China University of Petroleum, 2021.
6	林林. 管壳式换热器结垢和泄漏的传热特性及预测研究[D]. 大庆: 东北石油大学, 2014.
	Lin L. Study on heat transfer characteristics and prediction of scaling and leakage in shell-and-tube heat exchanger[D]. Daqing: Northeast Petroleum University, 2014.
7	张翠翠. 循环水的结垢监测以及有机污水处理方法研究[D]. 武汉: 华中科技大学, 2011.
	Zhang C C. Scaling monitoring of circulating water and study on organic sewage treatment method[D]. Wuhan: Huazhong University of Science and Technology, 2011.
8	郭亮, 张煜. 数字孪生在制造中的应用进展综述[J]. 机械科学与技术, 2020, 39(4): 590-598.
	Guo L, Zhang Y. Review on application progress of digital twin in manufacturing[J]. Mechanical Science and Technology for Aerospace Engineering, 2020, 39(4): 590-598.
9	陶飞, 马昕, 胡天亮, 等. 数字孪生标准体系[J]. 计算机集成制造系统, 2019, 25(10): 2405-2418.
	Tao F, Ma X, Hu T L, et al. Research on digital twin standard system[J]. Computer Integrated Manufacturing Systems, 2019, 25(10): 2405-2418.
10	曲宁. 板式换热器传热与流动分析[D]. 济南: 山东大学, 2005.
	Qu N. Heat transfer and flow analysis of plate heat exchanger[D]. Jinan: Shandong University, 2005.
11	He R, Chen G M, Dong C, et al. Data-driven digital twin technology for optimized control in process systems[J]. ISA Transactions, 2019, 95: 221-234.
12	Fguiri A, Marvillet C, Jeday M R. Estimation of fouling resistance in a phosphoric acid/steam heat exchanger using inverse method[J]. Applied Thermal Engineering, 2021, 192: 116935.
13	Al Hadad W, Schick V, Maillet D. Fouling detection in a shell and tube heat exchanger using variation of its thermal impulse responses: methodological approach and numerical verification[J]. Applied Thermal Engineering, 2019, 155: 612-619.
14	Davoudi E, Vaferi B. Applying artificial neural networks for systematic estimation of degree of fouling in heat exchangers[J]. Chemical Engineering Research and Design, 2018, 130: 138-153.
15	Jonsson G R, Lalot S, Palsson O P, et al. Use of extended Kalman filtering in detecting fouling in heat exchangers[J]. International Journal of Heat and Mass Transfer, 2007, 50(13/14): 2643-2655.
16	Delrot S, Guerra T M, Dambrine M, et al. Fouling detection in a heat exchanger by observer of Takagi-Sugeno type for systems with unknown polynomial inputs[J]. Engineering Applications of Artificial Intelligence, 2012, 25(8): 1558-1566.
17	Afgan N H, Carvalho M G. Knowledge-based expert system for fouling assessment of industrial heat exchangers[J]. Applied Thermal Engineering, 1996, 16(3): 203-208.
18	Ingimundardóttir H, Lalot S. Detection of fouling in a cross-flow heat exchanger using wavelets[J]. Heat Transfer Engineering, 2011, 32(3/4): 349-357.
19	Cid N, Patiño D, Porteiro J, et al. Validation of a fouling measurement procedure[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-8.
20	Withers P M. Ultrasonic, acoustic and optical techniques for the non-invasive detection of fouling in food processing equipment[J]. Trends in Food Science & Technology, 1996, 7(9): 293-298.
21	Guépié B K, Grall-Maës E, Beauseroy P, et al. Reliable leak detection in a heat exchanger of a sodium-cooled fast reactor[J]. Annals of Nuclear Energy, 2020, 142: 107357.
22	高士根, 周敏, 郑伟, 等. 基于数字孪生的高端装备智能运维研究现状与展望[J]. 计算机集成制造系统, 2022, 28(7): 1953-1965.
	Gao S G, Zhou M, Zheng W, et al. Intelligent operation and maintenance for advanced equipment based on digital twin: challenges and future[J]. Computer Integrated Manufacturing Systems, 2022, 28(7): 1953-1965.
23	Zhuang C B, Liu J H, Xiong H. Digital twin-based smart production management and control framework for the complex product assembly shop-floor[J]. The International Journal of Advanced Manufacturing Technology, 2018, 96(1): 1149-1163.
24	Fuhg J N, Fau A, Nackenhorst U. State-of-the-art and comparative review of adaptive sampling methods for kriging[J]. Archives of Computational Methods in Engineering, 2021, 28(4): 2689-2747.
25	吕小龙, 黄丹, 姜冬菊. 基于POD-RBF代理模型的迭代更新反演方法[J]. 计算力学学报, 2022, 39(4): 506-511.
	Lyu X L, Huang D, Jiang D J. Iterative updating inversion method based on POD-RBF surrogate model[J]. Chinese Journal of Computational Mechanics, 2022, 39(4): 506-511.
26	Awais M, Bhuiyan A A. Recent advancements in impedance of fouling resistance and particulate depositions in heat exchangers[J]. International Journal of Heat and Mass Transfer, 2019, 141: 580-603.
27	许晓红, 郭建辉, 雷勇刚, 等. 双壳程管壳式换热器的传热和阻力特性[J]. 热能动力工程, 2018, 33(12): 20-25.
	Xu X H, Guo J H, Lei Y G, et al. Heat transfer and flow resistance performance of a double shell-pass shell-and-tube heat exchanger[J]. Journal of Engineering for Thermal Energy and Power, 2018, 33(12): 20-25.
28	钱剑峰, 吴学慧, 孙德兴, 等. 管壳式污水换热器结垢厚度对流动换热的影响[J]. 流体机械, 2007, 35(1): 74-78.
	Qian J F, Wu X H, Sun D X, et al. Effect on fluxion and heat transfer due to the thickness of soft dirt in shell sewage heat exchanger[J]. Fluid Machinery, 2007, 35(1): 74-78.
29	朱文琦. 考虑结垢问题的换热网络优化[D]. 北京: 中国石油大学(北京), 2019.
	Zhu W Q. Optimization of heat exchange network considering scaling problem[D]. Beijing: China University of Petroleum, 2019.
30	陈鹤天. 常减压蒸馏流程模拟及传热潜力分析[D]. 北京: 中国石油大学(北京), 2018.
	Chen H T. Simulation of atmospheric and vacuum distillation process and analysis of heat transfer potential[D]. Beijing: China University of Petroleum, 2018.
31	谢洪虎, 江楠. 管壳式换热器壳程流体流动与换热的数值模拟[J]. 化学工程, 2009, 37(9): 9-12.
	Xie H H, Jiang N. Numerical simulation of shell-side flow and heat transfer characteristics in shell-and-tube heat exchanger[J]. Chemical Engineering (China), 2009, 37(9): 9-12.
32	陶文铨. 数值传热学[M]. 2版. 西安:西安交通大学出版社, 2001.
	Tao W Q. Numerical Heat Transfer[M]. 2nd ed. Xi'an: Xi'an Jiaotong University Press, 2001.

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数字孪生驱动的热交换器降阶建模及智能感知方法研究

Research on reduced order modeling and intelligent sensing method for heat exchangers driven by digital twin

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