ReviewHeliyon2025
Resilience assessment in process industries: A review of literature.
Review in Heliyon, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Several types of accidents, such as exposure to toxic gases, fires, or explosions, are encountered in process industries which are highly risk systems. To reduce risks and the consequences of disruptive events, resilience is recognized as one of the most important aspects of safety management, and resilience assessment in complex process systems plays an important role. This study examines methods for resilience assessment in process industries, by reviewing the published studies. Given the transient changes in resilience and performance variability due to complexity, it is examined which methods are more commonly applied for quantitative resilience assessments. As a result of the review of published literature, the most commonly used method to assess resilience in process industries is Dynamic Bayesian Network (DBN). DBN may be used for the estimation of uncertainty and probability of resilience in chemical processes. The resilience of complex process systems, which consider some aspects of resilience like absorption, adaptation, and recovery, is addressed and modeled by DBN. This review provides information on the use of quantitative methods to assess the resilience of complex process systems, the estimation of failure probability, the determination of performance variability under complex conditions, and the model of interactions between the components of a complex process system.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.