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

doi:10.11949/0438-1157.20230707

Abstract

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

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

CLC Number:

TQ 051.5

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.

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.

Figures/Tables 11

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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

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

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

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

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