uncertainty aspects in process safety safety...

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Uncertainty aspects in process Uncertainty aspects in process safetysafety analysis analysis

A.S. Markowski*,M.S. Mannan**,A.Bigoszewska* and D. Siuta*

*Process and Ecological Safety DivisionFaculty of Process and Environmental Engineering

Technical University of Lodz, Poland

**Mary Kay O’Connor Process Safety CenterDepartment of Chemical Engineering

Texas A&M University, College Station-TX

MKOPSC MKOPSC SymosiumSymosium 20082008College College StationStation, TX, USA, TX, USA

Mary Kay O’Connor Process Safety Center TAMU

Process and EcologicalSafety Division TU Lodz

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MotivationMotivation

1. to disccuss issue of uncertainty in traditional the Process Safety Analysis (PSA)

2. to propose the general method to handle a PSA taking into account the uncertainty

3. to demonstrate the application of fuzzy logic in PSA, e.g. in consequence analysis

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UncertaintyUncertainty

Uncertainty is a term used in different ways in a number of fields.

Uncertainty applies to predictions of future events or to the unknown. It is essentially the absence of information, information that may or not be obtainable.

All engineering calculations are affected by uncertainties

In terms of PSA, uncertainty means the possibility of predicting wrong risk index.

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Dealing with uncertaintiesDealing with uncertainties ((threethree waysways) )

• Neglecting uncertainties may lead to faulty decision bases for dimensioning components and, hence, to components which are too weak.

• Safety factors (expert opinion) which may lead to an insufficient design, overdesign etc.

• By modelling which may essentially reduce the uncertainities

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Advantages of accounting for Advantages of accounting for uncertaintiesuncertainties

• The information base used becomes broader.

• The meaning of safety factors becomes evident, safety reserves are made explicit.

• The credibility of the results is increased.

• Indications of areas are given where models and data should be refined.

• If the quality of the different input data for treating a problem differs, this fact is propagated through the calculations and reflected in the final result.

Uncertainties approaches Uncertainties approaches

Physical variables(objective)

Statisticalapproaches

Uncertaintiesin PHA

Lack of knowledge(subjective)

Variation inthe results

Expertknowledge

Classicalmethods

Probabilisticmethods

Best estimate Probabilitydistribution

Typeof uncertainty

Uncertaintymeasure

Uncertaintyapproaches

Uncertaintytechniques

Uncertaintyrepresentation

Probabilitytheory

Possibilitytheory

Rule-basedsystem

Fuzzy setstheory

Sensitivityanalysis

Expertsystem

Fuzzy logicsystem

Sensitivityanalysis

Certaintyfactor

Fuzzyset

Correlationcoefficients

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Representation of Representation of uncertainityuncertainity results and their interpretationresults and their interpretation

•

Risks are normally characterized by indicating the frequency of occurrence of an accident and the corresponding severity of consequence.

•

It is useful to indicate the uncertainty associated with the final result.

•

A distinction is made between:– risk index (for semi quantitative methods)- group risk– individual risk,depending on method used for RA

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Type of uncertainty

Characteristic Approaches Methods

Objective (aleatory)

Physical variability

Probabilistic Statistic Sensitivity

Subjective(epistemic)

Lack of knowledge

Possibility theory

Fuzzy sets

UncertaintyUncertainty –– sourcessources andand approachesapproaches

All

types

od uncertainties

occur

in

PSA, especially

subjective

type

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For PSA it is convinient to distinguish the following types:

• completeness uncertainty• modeling uncertainty• parameter uncertainty

UncertaintyUncertainty

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Steps of PSA:

• hazard identification• consequence

assessment• frequency• risk evaluation

PSPSA A frameworkframework ((traditionaltraditional))

Hazardidentification

Representativeaccident scenarioselection (RAS)

Accident scenariologic structure

Frequencyof RAS

Severity ofconsequences

Risk estimationand assessment

Risk analysis(semi-quantitative or quntitative)

Hazard analysis(quntitative)

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Hazard identification

1st step 1st step ofof PSAPSA

Main goal Main toolTypes of uncertainty

Completeness Modelling Parameter

Identification of accident scenario

HAZOPPHAFTAETA

Incomplete identification of all accident scenarios as well errors in screening of hazards

Interaction between different contributors and variables in accident scenario models

Imprecision or vagueness in characteristic properties of contributors and variables

Completeness

uncert.-

main

component

of

uncertainity

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Consequence assessment



Heath, property and environmental losses

Consequence models

Incorrectness in identification of all types of the consequences as well as of all interactions among consequences

Complexity phenomena and imprecision of source terms, dispersion and physical effects

Inadequacy or vagueness in values for model variable

2nd step 2nd step ofof PSAPSA

Modelling

uncert.-

main

component

of

uncertainity 12

Risk evaluation

3rd step 3rd step ofof PSAPSA



Risk index or risk level

QRALOPA

Limited depth of assumptions in: external conditions, number of accident outcome cases and incorrectness in interpretation of results

Inadequacy in selection of appropriate risk measures as well as of risk acceptance criteria

Lack of real time data for weather conditions and population, for real failure rates and human errors

Parameter

uncert.-

main

component

of

uncertainity

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Fuzzy logic for process safetyFuzzy logic for process safety

• PSA is a complex problem as characterized by the presence of diffrent types of uncertainty contained in the variables, models and assumptions. Such a complex system is difficult to precise analysis.

• Where no precise analysis and ambiguity or vaguiness take place the fuzzy set analysis can help.

• PSA can be treated as a fuzzy concept because plant safety cannot be strictly classified as a safe or unsafe (because of the existence of inherent hazards); therefore level of safety may belong simultaneously to safe state category and to unsafe state category with some memberships; this can only be realized by fuzzy sets.

FuzzyFuzzy set set

Membership of “state”to fuzzy set A(x)

Precislydeterminedboundary

Fuzzyboundary

“Safe state”Classical set

“State” partialy belongs to set

“Safe state”Fuzzy set

“Unsafe state”

“Unsafe state”

}));(,{( XxxxA A

]1,0[: XA

μ

(x) is membership function describing degree of belonging for x in A

where:

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Fuzzy set

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Fuzzy logicFuzzy logic system (FLS)system (FLS)

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ApllicationApllication for PSA (for PSA (aassumptionsssumptions for for fPSAfPSA

• A fuzzy logic system is applied to all elements of the PSA

• All linguistic variables (frequency, severity and risk) are represented by fuzzy logic sets (fuzzification), defined in their own universe of discourse

• Output fuzzy frequency (F) is calculated on the basis of „bow-tie model”

• Output fuzzy severity of consequences (S) is assessed using an expert opinien or applying fuzzy arithmetics to the consequence models (parameter method)

• Output fuzzy risk index (R) is assessed using FLS where knowledge of rules is provided by a risk matrix

• Fuzzy Risk Correction Index (RCI) is used to take into account the uncertainties involved in identification of RAS

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FuzzyFuzzy PSA (PSA (fPSAfPSA))

PHA & RAS

for F(RAS)

Traditionalpart

Fuzzypart

Bow-tie model for RAS

FLS FLS

for R (RAS)O

FLS

for S(RAS)

Risk indexR(RAS)

FLS FLS

1 2

3

4 5

Fuzzy risk surface

FUZZY RISK MATRIX CLASSICAL RISK MATRIX

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Consequence analysis

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FLS for consequence analysis

Two

methods

may

be used:1. simplified method based on the categorization of

the severity of consequences into separate categories; further process applies assigning of fuzzy set for that category

of release

(fuzzification) and this is input data for fuzzy

risk matrix,

2. parameter method used for particular consequence model, e.g. BLEVE model

and

the

apllication

of

fuzzy

arithmetic

on the

model. 22

FLS for consequence analysis –parametr method

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FLS for BLEVE –an example

Important

output

data: distance

(radius) to hazardous

radiation

level24

BLEVE calculation

][kW/m T FE = I 2viewp

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BLEVE - an example

-

600 m3 tank with LPG with the help of PHAST program

-three threshold values for thermal radiation

- 4, 12.5, 37.5 kW/m2

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Fuzzification of sensitive parameters -fuzzy sets

273 283

243 3130

0,5

1

240 260 280 300 320T [K]

Mem

bers

hip

func

tion

0,5 0,6

0,90,10

0,5

1

0 0,5 1

Mem

bers

hip

func

tion

70 80

300

0,5

1

0 20 40 60 80 100 120X [%]

Mem

bers

hip

func

tion

Ambient temperature

Filling degree Air humidity

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BLEVE results

Range of distance for different radiation levels 28

BLEVE resultsType of analysis Range of distance to radiation level [m]

4 kW/m2 12.5 kW/m2 37.5 kW/m2

Non –

fuzzy 876 506 283

Fuzzy 793 467 264

Overprediction

of

hazardous

zone

distance

by about

10 % 29

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ConclusionsConclusions•

Process Safety Analysis (PSA), representing numbers of uncertainties those may lead to important overlooks in the risk assessment

of the process plants.

• One of the promising methods for reduction of the uncertainties in process safety assessment is fuzzy logic, which allows to apply

imprecise and approximate data that are typically met in PSA to receive

a quite precise output

results.

• The fuzzy PSA model is presented where

FLS is

built

–in the

particular

components

of

RA.

• Preliminary

tests

indicated

that

fuzzy

PSA can

produce

more

precise

results

concerning

both

elements

of

risk

(frequency

and severity

of

consequences).

• It

is

also

possible

to include

the effect of the quality of PSA

on

overall risk index

assessment

by means

of

fuzzy

Risk

Correction Index

uncertainty aspects in process safety safety...

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