A risk assessment of a gas pressure reduction station system with confidence for dealing with imprecisions and unknowns

Abstract

Process systems are sensitive and vital industrial facilities. Disturbances in their performance may cause harm to the environment,humans,or significant economic damage. In risk assessment of chemical process industries , the available data, information, and knowledge are typically rare, limited, and often unrealistic. This issue poses a challenge to conducting a credible quantitative risk assessment and effects the robustness of the results. To address these challenges, this work proposes a methodology based on the Dempster-Shafer theory of evidence as the reasoning framework. It incorporates risk identification, analysis, and mitigation phases to ensure a thorough analysis of risks and the integration of proactive risk reduction strategies. The approach aims to model the worst-case hazard scenario and assess associated risks using various methods such as FMECA, Bow-Tie, Credal Network, and Dempster-Shafer theory. The proposed approach models imprecision and data ambiguity using intervals and associated belief mass. This extension provides a basis for addressing the fundamental problem of prior ignorance about the distribution of the observed data, which is prevalent in data mining applications. A new approach is proposed that utilizes Belief and Plausibility curves, similar to a Cumulative Distribution Function, to propagate uncertainty, enhance criticality discrimination, and determine cumulated belief measures. This approach is applied in analyzing the failure modes identified in FMECA and is further extended through the credal network for comprehensive risk assessment. Results show how to express irrelevant and independent judgments, and how to work out with inferences in credal networks. This issue is often overlooked, but if properly addressed it represents the key to ultimately drawing reliable conclusions and fully utilizing the system's available data. A case study of the City Gate Station system was used to verify the application potential of the proposed approach.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 7 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.