uncertainties and errors of qra

8/6/2019 Uncertainties and Errors of QRA

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UNCERTAINTIES AND ERRORS

OF QRA Laura Bruce, Ifiok Etukudo, Widya Siswanto, Ugoeze

Emmanuella Uzor and Shobha Venkataswamy.



T YPES OF UNCERTAINTIES IN QRA

Parameter Uncertainty: When the values of the

parameters used in the models are not accurately

known. It might be stochastic or epistemic

uncertainty.

Model Uncertainty: Any model, conceptual or

mathematical, will inevitably be a simplification of

the reality it is designed to represent

Completeness Uncertainty: Originates from the

fact that not all contributions to risk are addressed

in QRA models.



UNCERTAINTIES INTRODUCED AT THE

DIFFERENT STAGES OF QRA



THE IDENTIFICATION STAGE

Produce a comprehensive list of possible initiating events

To identify priorities between them and make decisions on which of them are to be analysed further

Major uncertainty: Completeness.

Have all major hazards and/ or possible accident

scenarios been identified? Methods for structured identification (to facilitate

completeness): HAZOP, what-if analysis, FMEA, etc.



THE IDENTIFICATION STAGE (CONT·D)

Example of accidents with their coverage in standard QRA methodologies.

Accident Mechanism Included in

Standards

QRA

Methodologies?

Models

Available?

Texas City

2005

K O drum

overflow,

spraying vent

N Y

Tosco Avon

1997

Hydrocracker

reactor

runaway

N Y

Buncefield2005

Overflow,

splashing flow,

UVCE

N Y

CAI, Danvers 2006

Confined VCE N Y (Adapted from Taylor, 2010)



FREQUENCY ESTIM ATION



FREQUENCY ESTIM ATION (CONT·D)

The two main methods of likelihood and frequency

estimation are:

Historical Data: Questions raised on accuracy and

applicability. Data may be inaccurate, incomplete or

inappropriate. Issue on parameter uncertainty.

E.g.: Leak frequency data for North Sea to elsewhere.

Fault and event tree analysis: Questions raised on

completeness, simplification and parameter uncertainty in the model.

E.g.: Omission of significant failure mechanisms can

lead to erroneous results.



FREQUENCY ESTIM ATION (CONT:)

Major sources of uncertainty in frequency

estimation:

- failure frequency;

- leak frequency;- ignition probability; and

- explosion probability.



CONSEQUENCE ESTIM ATION

The consequence estimation scheme involves

three steps:

y Accident scenario analysis Identification of initiating events.

Generation of accident scenarios for each initiating events.

Quantification of accident scenarios.

y Identification and classification of losses

y Estimation of losses



CONSEQUENCE ESTIM ATION (CONT·D)

Conducted using physical and effect models.

y Physical models: flash fire, pool fire, BLEVEs,

etc.

y Effect models: explosion effects, heat radiation, toxic effects, etc.

Major uncertainty:

y Model uncertainty in physical and effect models

y Parameter uncertainty, such as leak size.




Scenario:

Piping connecting Separator A with Gas Compressor experience 30 mm leak (9 in piping with 110 m length).

Operating pressure is 30 bar and operating temperature

is 27o

C.

Model uncertainty and parameter uncertainty was identified in the consequence estimation.

Leak

Diameter

(mm)

Cd Full Analysis Spouge

Difference

(%)Leak

(kg/s)

Flame

length (m)

Leak

(kg/s)

Flame

length (m)

in flame

length

30 0.8 2.87 28.51 2.7 27.8 2.50%

25 0.8 1.99 24.55 1.875 23.94 2.50%

30 0.7 2.51 26.99 2.7 27.8 2.99%




With the same scenario, if SD V is used as

safeguard, the consequence estimation presented

below.Time (minutes) Leak (kg/s) Flame length (m)

0 2.87 28.51

5 3.25E-04 0.7

10 3.60E-08 0.02

Uncertainty relates to the assumption made on the condition of the safeguard, such as full closure (air-tight) condition of the valve, time response, etc.



ESTIM ATION OF RISK

The final step in the QRA is to generate the

actual risk measure.

Risk is product of the probability of a certain

outcome with the consequence of that particular outcome.

Risk indices represented by:

y Individual Risk Per Annum (IRPA);

y Probable Loss of life (PLL);

y Temporary Refuge Impairment Frequency (TRIF);

and

y Societal Risk (F-N Curve).



ESTIM ATION OF RISK (CONT·D)

Major uncertainty:

y Assumption and simplification made in order to

decrease the complexity of the analysis

Example: assumptions on distribution of ignition

sources, population distributions, etc.

y Relative importance of risk



CONCLUSION

Identification of the contributors to the overall

uncertainty is important to improve the quality

of the QRA

Whenever necessary, increased investment indata collection or model development could

significantly reduce uncertainty.

However, uncertainty in inherent statistical

variability in failure rate data can not be

removed.



REFERENCES

Abrahamsson, Marcus. Uncertainty in

Quantitative Risk Analysis ² Characterization

and Methods of Treatment. 2002.

Lees, Frank P. L

oss Preven

tion

in

the ProcessI ndustries, 3 rd Edition. 2004.

Taylor, J.R. Accuracy in Quantitative Risk

Assessment? 13th International Symposium on

Loss Prevention. 2010.

Borysiewicz, M.J, et. al. Quantitative Risk

Assessment (QRA).



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uncertainties and errors of qra

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