cng {bnRep} | R Documentation |
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.