Multi-parameter correlation early warning method of heat exchanger based on multivariate state estimation

doi:10.11949/0438-1157.20210848

Abstract

Abstract:

With the rapid development of process industry and the increasing complexity of process, heat exchangers which serve as the core equipment suffer from the difficulty of the fault early warning. This paper presents a novel method for the fault early warning of tubular heat exchangers. The monitoring indexes are constructed and selected considering the easily measured parameters including temperature, pressure and flow rate of the process fluid. The monitoring indexes are applied to represent the heat transfer and flow resistance performance of the heat exchangers. The EMA algorithm is introduced to process the data and extract the dynamic features of the indexes. A three-dimensional memory matrix is built to represent the working condition of the heat exchangers based on the theory of multiple state estimation (MSET), which avoids the influence of varying condition on the monitoring indexes. Then, the operating observation is applied to calculate the real-time deviation, which achieves the state assessment and fault early warning of the heat exchangers. The abnormal parameters are identified by calculating the real-time deviation contribution rate of each index to achieve accurate fault location.The experimental results show that the method can realize the early warning of leakage faults when the leakage volume accounts for more than 1% of the process fluid flow rate and the early warning of scaling faults when the thickness of the scale layer accounts for more than 2.2% of the pipe diameter.

Key words: safety, heat conduction, fouling, heat exchanger, state estimation, fault early warning, data treating

摘要：

随着流程工业快速发展及工艺过程复杂程度的不断增加，作为核心装备的热交换器存在有效故障早期预警难的问题。以管壳式热交换器为例，提出一种针对热交换器故障早期预警的新方法。该方法基于工艺流体温度、压力、流量等易测量参数构建、选取表征热交换器传热和流阻性能的监测指标，并引入指数移动平均（EMA）算法进行数据处理与指标动态特征提取；在多元状态估计理论（MSET）的基础上，构建表征热交换器健康运行状态的三维健康记忆矩阵，实现热交换器多变工况的融合，避免工况变化对监测指标的影响，然后针对设备当前运行观测值计算实时偏差，实现热交换器运行状态评估及故障预警；通过统计计算过程中各指标实时偏差贡献率锁定异常参数，实现故障精准定位。实验结果表明，该方法能够实现泄漏量占工艺流体流量1%以上泄漏故障预警及垢层厚度占管径2.2%以上结垢故障预警。

关键词: 安全, 热传导, 结垢, 热交换器, 状态估计, 故障预警, 数据处理

CLC Number:

TK 172

Fengli ZHANG, Hui PAN, Jinjiang WANG. Multi-parameter correlation early warning method of heat exchanger based on multivariate state estimation[J]. CIESC Journal, 2022, 73(2): 814-826.

张凤丽, 潘辉, 王金江. 基于多元状态估计的热交换器多参数关联预警方法[J]. 化工学报, 2022, 73(2): 814-826.

Figures/Tables 15

Fig.1 The scheme of multivariate state estimation

Fig.2 Framework of multi-parameter correlation early warning method for heat exchangers

Table 1 Secondary indexes of monitoring performance of heat exchangers

名称	公式	名称	公式
传热效率	$ε = t - t ″ m a x / t 1' - t 2'$	流动功耗	$N = G h (P h i - P h o) + G c (P c i - P c o)$
有效平均温差	$Δ T m = Δ T h - Δ T c / l n (Δ T h / Δ T c)$	传热单元数	$N T U = K A / C m i n$
实际换热量	$Q c = V c ρ c C p c (T c o - T c i)$	效率	$η e = - Δ E x c / Δ E x h$
总换热系数	$K = Q c / A Δ T m$	熵产值	$N s = l n T h o / T h i / W + l n T c o / T c i$
管程压损	$Δ p t = λ d i L + 3 F t N p N s ρ u i 2 / 2$	壳程压损	$h f s' = F f l n c (g + 1) + N B (3.5 - 2 g / D i) ρ u l 2 f s N s / 2$

Table 1 Secondary indexes of monitoring performance of heat exchangers

名称	公式	名称	公式
传热效率	$ε = t - t ″ m a x / t 1' - t 2'$	流动功耗	$N = G h (P h i - P h o) + G c (P c i - P c o)$
有效平均温差	$Δ T m = Δ T h - Δ T c / l n (Δ T h / Δ T c)$	传热单元数	$N T U = K A / C m i n$
实际换热量	$Q c = V c ρ c C p c (T c o - T c i)$	效率	$η e = - Δ E x c / Δ E x h$
总换热系数	$K = Q c / A Δ T m$	熵产值	$N s = l n T h o / T h i / W + l n T c o / T c i$
管程压损	$Δ p t = λ d i L + 3 F t N p N s ρ u i 2 / 2$	壳程压损	$h f s' = F f l n c (g + 1) + N B (3.5 - 2 g / D i) ρ u l 2 f s N s / 2$

Fig.3 The structure of the multi-dimensional health memory matrix

Fig.4 Experimental setup of the heat exchanger

Table 2 Experimental conditions of heat exchanger scaling

序号	进口温度/℃		进口压力/kPa		进口流量/(m³/h)		垢层厚度/mm	垢层厚度占管径比/%
序号	热侧流体	冷侧流体	热侧流体	冷侧流体	热侧流体	冷侧流体	垢层厚度/mm	垢层厚度占管径比/%
工况1	60	26	5.3	9.3	0.81	0.48	0.3185	2.65
工况2	70	36	5.4	9.3	0.82	0.47	0.3830	3.19
工况3	65	31	5.0	4.5	0.72	0.44	0.3063	2.21

Table 3 Working conditions of heat exchanger leakage for experiments

序号	进口温度/℃		进口压力/kPa		进口流量/(m³/h)		泄漏量/ (m³/h)	泄漏率/%
序号	热侧流体	冷侧流体	热侧流体	冷侧流体	热侧流体	冷侧流体	泄漏量/ (m³/h)	泄漏率/%
工况1	55	35	1420	102	30	20	0.5904	1.968
工况2	50	35	1800	110	35	20	0.3021	0.8631
工况3	45	35	1820	110	25	20	0.2660	1.064

Fig.5 Data processing results

Fig.6 Comparison of primary monitoring indexes for heat exchanger with scaling

Fig.7 Comparison of primary monitoring indexes for heat exchanger with leakage

Fig.8 Contribution of each parameter error under the working condition of scaling

Fig.9 Contribution of each parameter error in leakage

Fig.10 Early warning status before and after data processing under scaling condition

Fig.11 Early warning status before and after data processing under leakage condition

Table 4 Statistics of early warning effect under various working conditions

序号	相似度值			误报对比		漏报对比
序号	正常运行	结垢/泄漏状态	预警阈值	原始数据	处理数据	原始数据	处理数据
结垢工况1	0.7104	0.5123	0.6739	1	0	0	0
结垢工况2	0.6887	0.4735	0.5347	2	0	0	0
结垢工况3	0.5437	0.4234	0.4510	5	1	0	0
泄漏工况1	0.8950	0.8230	0.8540	6	0	0	0
泄漏工况2	0.8743	0.8103	0.8496	4	0	0	0
泄漏工况3	0.8889	0.8324	0.8612	1	0	0	0

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