cng: cng Bayesian Network

In bnRep: A Repository of Bayesian Networks from the Academic Literature

cng Bayesian Network

Description

Quantitative risk estimation of CNG station by using fuzzy bayesian networks and consequence modeling.

Format

A discrete Bayesian network for risk assessment in compressed natural gas (CNG) stations. The probabilities were given within the referenced paper. The vertices are:

X1

Not up-to-date technology (T, F);

X2

Lack of maintenance (T, F);

X3

Unsafe equipment (T, F);

X4

Type of ignition material (T, F);

X5

The nature of the chemical substance (T, F);

X6

Inspection defect in wear detection (T, F);

X7

Improper use of the equipment (T, F);

X8

Leakage (T, F);

X9

High temperature (T, F);

X10

Low temperature (T, F);

X11

Horizontal wind speed (T, F);

X12

Vertical wind speed (T, F);

X13

Environmental stability and instability (T, F);

X14

Sunny hours (T, F);

X15

Relative humidity and evaporation rate (T, F);

X16

Lighting (T, F);

X17

Landslide (T, F);

X18

Flood (T, F);

X19

Earthquake (T, F);

X20

Land settlement (T, F);

X21

Deliberate vandalism (T, F);

X22

Incidents related to the missile site (T, F);

X23

Military attack (T, F);

X24

Explosion of other equipment (T, F);

X25

Deliberate error in the execution of the recipe (T, F);

X26

Accidental collision valves (T, F);

X27

Failure to issue a work permit (T, F);

X28

Artificial lighting (T, F);

X29

Natural lighting (T, F);

X30

Lack of cost (T, F);

X31

Requirements for conducting training classes by managers (T, F);

X32

Fatigue (T, F);

X33

Shift work (T, F);

X34

Stress - internal causes) (T, F);

X35

Stress - external causes (T, F);

X36

Not having enough experience and skills (T, F);

X37

Hearing loss - non-occupational causes (T, F);

X38

Hearing loss - occupational causes (T, F);

X39

Failure to notify the control room in time (T, F);

X40

Fear of explosion and fire by operator (T, F);

X41

Operator performance - temperature and humidity (T, F);

X42

Chemical pollutants - particles (T, F);

X43

Chemical pollutants - gas and steam (T, F);

X44

Solid waste (T, F);

X45

Liquid waste (T, F);

X46

Adjacent commercial use (T, F);

X47

Adjacent residential use (T, F);

X48

Adjacent industrial use (T, F);

X49

Land uses changes (T, F);

X50

Room metering - measurement of changes (T, F);

X51

Room metering - operator error (T, F);

X52

Lack of standard dryer quality (T, F);

X53

Disturbance in the electricity flow of the dryer (T, F);

X54

Fire dryer heaters (T, F);

X55

Leakage of tank (T, F);

X56

Adjacent tanks (T, F);

X57

Dispenser leakage and damage (T, F);

X58

Disregarding dispenser safety signs (T, F);

X59

Dispenser malfunction (T, F);

X60

Improper management performance (T, F);

AdjacentLandUses

(T, F);

AnticipatedEvents

(T, F);

ChemicalContaminants

(T, F);

ClimateChanges

(T, F);

Dispenser

(T, F);

Dryer

(T, F);

EnvironmentChanges

(T, F);

Exhaustion

(T, F);

FailureToInspectAndOperateEquipment

(T, F);

FortuitousEvents

(T, F);

HearingLoss

(T, F);

HumanReasons

(T, F);

ImproperOperatorPerformance

(T, F);

InadequateTraining

(T, F);

LeakOfCNG

(T, F);

Lighting

(T, F);

MilitaryIncidents

(T, F);

NaturalDisasters

(T, F);

ProcessProblems

(T, F);

RoomMetering

(T, F);

Storage

(T, F);

Stress

(T, F);

TankStructure

(T, F);

Temperature

(T, F);

Wastes

(T, F);

WindSpeed

(T, F);

Value

An object of class bn.fit. Refer to the documentation of bnlearn for details.

References

Abbasi Kharajou, B., Ahmadi, H., Rafiei, M., & Moradi Hanifi, S. (2024). Quantitative risk estimation of CNG station by using fuzzy bayesian networks and consequence modeling. Scientific Reports, 14(1), 4266.

bnRep documentation built on April 12, 2025, 1:13 a.m.