Deep-mining risk evolution path of chemical processes based on community structure

doi:10.11949/0438-1157.20221618

Abstract

Abstract:

In the process of chemical production, due to the danger of materials and the complexity of the process, serious accidents occur frequently. It is necessary to reveal risk evolution mechanism to ensure the safe and stable operation of chemical processes, as well as to inhibit the spread, diffusion, or even evolution of secondary and derivative disasters. However, many traditional methods heavily rely on expert experience or prior information, which will cause the inaccuracy of risk assessment results. Although the community structure in complex network can be regarded as a highly abstract of risk evolution path, the existing algorithms cannot consider both of the rationality and accuracy division results. Therefore, a deep mining method of risk evolution path is proposed for chemical processes based on the DSAE-Louvain community structure. First, multi-source process data should be processed to establish a risk evolution network, and the Dijkstra and hop-count algorithms are further applied to obtain a similarity matrix. Then, the deep sparse autoencoder (DSAE) and Louvain algorithm are combined to divide the community structure using sparse analysis. Finally, the risk weak nodes and critical evolution paths in whole chemical processes are deeply mined according to the ranking of node importance. To illustrate its validity, the Tennessee Eastman (TE) process is selected as a test case. The results show that the proposed DSAE-Louvain method are more refined and high-efficiency in the community structure division by comparing with the GN algorithm, Louvain algorithm, and DSAE-GN method. Particularly, the mined risk evolution paths are more in accord with actual production processes.

Key words: chemical processes, risk evolution, path mining, process systems, neural networks, safety

摘要：

在化工生产过程中，由于物料的危险性和流程的复杂性，导致恶性事故频发。为了保证化工过程的安全平稳运行，需对其风险演化机制进行深入解析，以抑制次衍生灾害事故的传播、扩散及演变。然而，传统方法过于依赖专家经验或先验信息，风险评估结果不精准；以复杂网络为基础的社团结构虽可视为风险演化路径的高度抽象，但已有算法难以兼顾划分结果的合理性和准确率。为此，提出一种基于DSAE-Louvain社团结构的化工过程风险演化路径深度挖掘方法。首先对多源过程数据进行处理，构建风险演化网络模型，同时利用Dijkstra算法、跳数法等手段，获取相似度矩阵；进而引入深度稀疏自编码器（DSAE）与Louvain算法，经稀疏处理开展社团结构划分；最后，根据节点重要度排序追踪整个化工过程的风险薄弱节点和关键演化路径。以Tennessee Eastman （TE）过程为例，对比GN算法、Louvain算法和DSAE-GN方法，结果表明DSAE-Louvain方法能够提升社团结构划分的精细化、高效化程度，且所挖掘的风险演化路径更为符合实际生产工艺流程。

关键词: 化工过程, 风险演化, 路径挖掘, 过程系统, 神经网络, 安全

CLC Number:

X 795

Cheng YUN, Qianlin WANG, Feng CHEN, Xin ZHANG, Zhan DOU, Tingjun YAN. Deep-mining risk evolution path of chemical processes based on community structure[J]. CIESC Journal, 2023, 74(4): 1639-1650.

贠程, 王倩琳, 陈锋, 张鑫, 窦站, 颜廷俊. 基于社团结构的化工过程风险演化路径深度挖掘[J]. 化工学报, 2023, 74(4): 1639-1650.

Figures/Tables 17

Fig.1 The structure of deep sparse autoencoder (DSAE)

Fig.2 The flowchart of the proposed method

Table 1 An example of multi-source process data

时刻	$a 1$	$a 2$	$a 3$	$a 4$
1	1.0	0.2	2.5	1.2
2	1.2	0.4	2.5	1.2
3	0.7	2.7	1.0	2.5
4	0.8	0.5	2.9	1.7
5	2.7	0.3	1.0	2.0
6	0.6	0.3	2.4	2.5
7	0.4	0.6	2.4	2.5

Table 1 An example of multi-source process data

时刻	$a 1$	$a 2$	$a 3$	$a 4$
1	1.0	0.2	2.5	1.2
2	1.2	0.4	2.5	1.2
3	0.7	2.7	1.0	2.5
4	0.8	0.5	2.9	1.7
5	2.7	0.3	1.0	2.0
6	0.6	0.3	2.4	2.5
7	0.4	0.6	2.4	2.5

Fig.3 An example of risk evolution network

Table 2 An example of calculation process for the shortest paths

复杂网络	邻接矩阵	最短路径结果
	$0101000101010100$	$0011001100010010$

Table 2 An example of calculation process for the shortest paths

复杂网络	邻接矩阵	最短路径结果
	$0101000101010100$	$0011001100010010$

Fig.4 The P&ID of the TE process

Table 3 Measured variables of the TE process （22 process variables）

变量	说明	变量	说明
XMEAS(1)	A进料流量值	XMEAS(12)	产品分离器液位值
XMEAS(2)	D进料流量值	XMEAS(13)	产品分离器压力值
XMEAS(3)	E进料流量值	XMEAS(14)	产品分离器下部出料值
XMEAS(4)	A和C进料流量值	XMEAS(15)	汽提塔液位值
XMEAS(5)	循环流量值	XMEAS(16)	汽提塔压力值
XMEAS(6)	反应器进料流量值	XMEAS(17)	汽提塔下部出料值
XMEAS(7)	反应器压力值	XMEAS(18)	汽提塔温度值
XMEAS(8)	反应器液位值	XMEAS(19)	汽提塔蒸汽流量值
XMEAS(9)	反应器温度值	XMEAS(20)	压缩机功率值
XMEAS(10)	放空流量值	XMEAS(21)	反应器冷却水出口温度值
XMEAS(11)	产品分离器温度值	XMEAS(22)	冷凝器冷却水出口温度值

Table 4 Adjacency matrix of the TE process

Item	XMEAS(1)	XMEAS(2)	···	XMEAS(9)	XMEAS(10)	···	XMEAS(21)	XMEAS(22)
XMEAS(1)	0	0		0	0		0	0
XMEAS(2)	0	0		0	0		0	0
$⋮$			···			···
XMEAS(9)	0	0		0	0.571		1	0
XMEAS(10)	0	0		0.800	0		0.800	0
$⋮$			···			···
XMEAS(21)	0	0		1	0.571		0.857	0
XMEAS(22)	0	0		0	0		0	0

Table 4 Adjacency matrix of the TE process

Item	XMEAS(1)	XMEAS(2)	···	XMEAS(9)	XMEAS(10)	···	XMEAS(21)	XMEAS(22)
XMEAS(1)	0	0		0	0		0	0
XMEAS(2)	0	0		0	0		0	0
$⋮$			···			···
XMEAS(9)	0	0		0	0.571		1	0
XMEAS(10)	0	0		0.800	0		0.800	0
$⋮$			···			···
XMEAS(21)	0	0		1	0.571		0.857	0
XMEAS(22)	0	0		0	0		0	0

Table 5 Results from the shortest paths of the TE process

Item	XMEAS(1)	XMEAS(2)	···	XMEAS(9)	XMEAS(10)	···	XMEAS(21)	XMEAS(22)
XMEAS(1)	0	0		0	0		0	0
XMEAS(2)	0	0		0	0		0	0
$⋮$			···			···
XMEAS(9)	0	0		0	1		1	0
XMEAS(10)	0	0		1	0		1	0
$⋮$			···			···
XMEAS(21)	0	0		1	1		0	0
XMEAS(22)	0	0		0	0		0	0

Table 5 Results from the shortest paths of the TE process

Item	XMEAS(1)	XMEAS(2)	···	XMEAS(9)	XMEAS(10)	···	XMEAS(21)	XMEAS(22)
XMEAS(1)	0	0		0	0		0	0
XMEAS(2)	0	0		0	0		0	0
$⋮$			···			···
XMEAS(9)	0	0		0	1		1	0
XMEAS(10)	0	0		1	0		1	0
$⋮$			···			···
XMEAS(21)	0	0		1	1		0	0
XMEAS(22)	0	0		0	0		0	0

Table 6 Similarity matrix of the TE process

Item	XMEAS(1)	XMEAS(2)	···	XMEAS(9)	XMEAS(10)	···	XMEAS(21)	XMEAS(22)
XMEAS(1)	0	0		0	0		0	0
XMEAS(2)	0	0		0	0		0	0
$⋮$			···			···
XMEAS(9)	0	0		0	0		1	0
XMEAS(10)	0	0		0	0		0	0
$⋮$			···			···
XMEAS(21)	0	0		1	0		0	0
XMEAS(22)	0	0		0	0		0	0

Table 6 Similarity matrix of the TE process

Item	XMEAS(1)	XMEAS(2)	···	XMEAS(9)	XMEAS(10)	···	XMEAS(21)	XMEAS(22)
XMEAS(1)	0	0		0	0		0	0
XMEAS(2)	0	0		0	0		0	0
$⋮$			···			···
XMEAS(9)	0	0		0	0		1	0
XMEAS(10)	0	0		0	0		0	0
$⋮$			···			···
XMEAS(21)	0	0		1	0		0	0
XMEAS(22)	0	0		0	0		0	0

Table 7 Optimal parameters of the DSAE model

参数	最优值
各层节点数	256-64-16-64-256
训练批次	4
迭代次数	150
惩罚因子β	1×10^-6
损失	0.691

Fig.5 Community structures of the TE process (DSAE-Louvain)

Table 8 The node corresponding to every community structure in risk evolution network

序号	网络节点
社团Ⅰ	XMEAS(4)—A和C组分进料流量、XMEAS(10)—放空流量、XMEAS(16)—汽提塔压力、XMEAS(22)—冷凝器冷却水出口温度
社团Ⅱ	XMEAS(2)—D组分进料流量、XMEAS(3)—E组分进料流量、XMEAS(7)—反应器压力、XMEAS(17)—汽提塔下部出料、XMEAS(18)—汽提塔温度
社团Ⅲ	XMEAS(5)—循环流量、XMEAS(6)—反应器进料流量、XMEAS(8)—反应器液位、XMEAS(12)—产品分离器液位、XMEAS(14)—产品分离器下部出料、XMEAS(19)—汽提塔蒸汽流量、XMEAS(20)—压缩机功率
社团Ⅳ	XMEAS(1)—A组分进料流量、XMEAS(9)—反应器温度、XMEAS(11)—产品分离器温度、XMEAS(13)—产品分离器压力、XMEAS(15)—汽提塔液位、XMEAS(21)—反应器冷却水出口温度

Table 9 Node importance ranking of the TE process

序号	重要度值	序号	重要度值
5	4.538	18	2.443
15	4.538	8	2.153
21	4.538	10	1.827
17	3.921	22	1.601
20	3.921	4	1.553
1	3.591	2	0
13	3.591	3	0
7	2.890	9	0
11	2.890	12	0
14	2.890	16	0
6	2.786	19	0

Fig.6 Risk evolution paths of the TE process (DSAE-Louvain)

Fig.7 Comparison of the modularity Q in different models

Fig.8 Community structures of the TE process (DSAE-GN)

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