In a chemical material network, a small fault is easy to spread and cause serious consequences, which is the embodiment of a complex network cascading fault. In order to ensure the safety and stability of chemical production, it is necessary to study the cascading fault propagation of a chemical material network. Firstly, the chemical material network model is constructed, and the SR algorithm is used to identify the important nodes in the network, which is the basis for selecting attack nodes. Secondly, the fault propagation strength is defined by the fault propagation probability and material hazard degree. The

In the complex network of chemical process systems, if a node fails, it may trigger cascading failures and affect normal operation. To enhance the ability of chemical process systems to maintain normal operation after the cascading failure, this paper presents cascading failure modelling and robustness analysis of chemical process systems based on the complex network non‐linear load capacity model. First, based on complex network theory, a complex network model of the chemical process is constructed; then, three cascading failure models are constructed using a combination of linear and non‐linear load capacity models and initial load and initial residual capacity redistribution strategies; and finally, the nodes with the maximum node degree are deliberately attacked to analyze the robustness of the chemical process system in response to cascading failure. The case study shows that the proposed models are valid and feasible, and the robustness of the chemical process system is enhanced as the load and capacity parameters are increased. By reasonably setting the initial load and adjusting the model parameters, the robustness can be effectively improved, providing a theoretical reference for improving the robustness of the actual chemical process system in response to cascading failure.

In order to better characterize the accident propagation mechanism in chemical industry park (CIP), a method combining mixed degree decomposition (MDD) algorithm with accident propagation probability is proposed to measure the accident propagation ability of nodes in the accident chain network of CIP in this paper. First, the accident chain network model of CIP is established by using complex network theory and the nodes in the network model are layered according to the accident propagation ability based on the MDD algorithm. Second, the accident propagation probability value of each node in the network is defined and calculated according to the characteristic factors of the node itself. Finally, the result of MDD algorithm is combined with the accident propagation probability value by using Euclidean distance formula, the nodes with strong accident propagation ability in the accident chain network of CIP are determined. The case analysis shows that the method has higher discrimination, can effectively measure the accident propagation ability of nodes in the accident chain network of CIP, and better identify the nodes with strong propagation ability.

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