Xinhong Li

Bio: Xinhong Li is an academic researcher from Xi'an University of Architecture and Technology. The author has contributed to research in topics: Subsea & Computer science. The author has an hindex of 11, co-authored 34 publications receiving 466 citations. Previous affiliations of Xinhong Li include Dalian University of Technology & China University of Petroleum.

Developing a dynamic model for risk analysis under uncertainty: Case of third-party damage on subsea pipelines

Abstract: Third-party damage is an important factor leading to subsea pipelines failure, and risk analysis an efficient approach to mitigate and control such events. However, available crisp probabilities for input events are usually limited, missing or unknown, which introduces data uncertainty. Furthermore, conventional risk analysis methods are known to have a static structure, which introduces model uncertainty. This paper presents a dynamic model for risk analysis under uncertainty and illustrates it by a case of third-party damage on subsea pipelines. Proposed model makes use of fuzzy set theory and evidence theory to handle data uncertainty, and utilizes Bayesian network (BN) to address model uncertainty. Primary accident scenario is developed by the FT-ESD approach, and it is transformed into BN to circumvent model uncertainty by relaxing the limitations of conventional methods. Expert elicitation is integrated into fuzzy set theory and evidence theory to obtain the crisp probabilities of input events in BN. Based on the model, a robust probability reasoning is conducted, through which the most probable factors contributing to the occurrence of unexpected consequence are identified. As new observations become available, potential accident probabilities are updated over time to produce a dynamic risk profile. The case study demonstrates the applicability and effectiveness of the model, which indicates that it is an alternative approach for risk analysis in the process industries under uncertainty.

Probabilistic analysis of natural gas pipeline network accident based on Bayesian network

Abstract: Natural gas pipeline network (NGPN) accident is a kind of catastrophic disaster as the hazard of natural gas may present a large-scale extension in NGPN that can easily result in cascading accidents. In this paper, the Bayesian network (BN) was employed to probabilistically analyze natural gas pipeline network accidents. On the basis of case-studies of typical NGPN accidents, eleven BN nodes were proposed to represent the evolution process of natural gas pipeline network accidents from failure causes to consequences. The conditional probabilities of every BN node were determined by expert knowledge with weighted treatments by the Dempster-Shafer evidence theory. Through giving evidences of some BN nodes with certain state values, the probabilities of evolution stages and consequences of the natural gas pipeline network accident can be estimated. The results indicate that the combination of Bayesian network and Dempster-Shafer evidence theory is an alternative method for evaluating NGPN accident, and the proposed framework can provide a more realistic consequence analysis since it could consider the conditional dependency in the evolution process of the NGPN accident. This study could be helpful for emergency response decision-making and loss prevention.