Accident Modeling and Analysis in Process Industriesprosesguvenligi.org/assets/24-october-2015/f-a... · Accident Modeling and Analysis in Process Industries Faisal Khan Centre for

SAFETY AND RISK ENGINEERING GROUPWWW.ENGR.MUN.CA/RESEARCH/SREG

Accident Modeling and Analysis in

Process Industries

Faisal Khan

Centre for Risk, Integrity & Safety Engineering (CRISE)

Faculty of Engineering & Applied Science

Memorial University, St John’s, NL, Canada


Outline

• Accident

• Accident Modelling Approaches

• SHIP Methodology

• Dynamic Risk

• Case Studies

• Conclusion


Introduction

• Recent Process Accidents and losses

• On January 23, 2010, release of highly toxic phosgene, exposing an

operator leading to death at the DuPont facility in Belle, West.

• On April 20, 2010, a sudden explosion and fire occurred on the

BP/Transocean Deepwater Horizon oil rig. The accident resulted in the

deaths of 11 workers and caused a massive oil spill into the Gulf of

Mexico.

• On July 22, 2010, an explosion and fire killed two workers at the

Horsehead Holding Company zinc recycling facility located in Monaca, PA.

The facility recycles and purifies zinc through a high temperature

distillation process

• On January 10, 2012, blowout in KS Endeavour (Nigeria) killing two

personnel, fire and spill continued for 46 days.

• And list goes on...

Source: www.csb.gov


Introduction

• Are these Accidents Preventable?

Yes! Most of the times.

• How?

Knowing their occurrence early

(likelihood) and taking appropriate safety

measure

Predictive Accident Modeling

(Occurrence Likelihood)


An Accident

• Event or activity that is:

Unwanted

Uncertain

Uncontrollable

An accident in process facility caused by

process malfunction is termed as Process

Accident


Accident Concept

Good

Bad

What we see?

What we measure/monitor

What we must Model/Predict


Process Accident

Initiation

Propagation

Termination


Accident Process Concept

Safe (Normal) state

Near Miss

Mishap

Incident

Accident

COUSES


Accident Pyramid

Catastrophic Accident (0)

Accident (1)

Incident (5)

Mishap (10)

Near miss (100)

Frequency increasing

Consequence

increasing


Accident Modeling Approaches

• Accident Models ReviewedDomino Keltz Ren’s HOF model

Loss causation Swiss cheese Kujath’s Model

FRAM Daryl's model STAMP

• Observation:

Focus on occupational accidents, and the models focusing on process hazards have been scant

Unable to present a holistic picture of system safety, and are not capable of accommodating modeling of multiple causal factors.

Descriptive models, not predictive models

Not adopted comprehensive quantification (no updating mechanism to reduce the uncertainty)


Proposed Approach & Model


Layers of Protection

System control

Critical Alarms

Safety instrumented

system

Passive Protection Measures

Active safety and effect

Mitigating Measures


SHIPP Methodology-System Hazard

Identification,

Prediction and

Prevention Methodology


Process Accident Model

Progression

Layers of protection

Initiation Termination


Incre

asin

g

Incre

asin

g

Occurrence

frequency

Consequence



Accident Risk Model

Unwanted Event

Basic

event

Causes

Outcome

Basic

event

Basic

event

Outcome

Outcome

Accident Risk Modeling using “Bow-tie”

diagram

Proactive Controls Reactive Controls Consequences

Fault Tree Event Tree

Accident

Risk

Safer

Accident

Risk


Risk

Conceptual Design

• Risk

FEED • Risk

Detailed design

• Risk

Risk

Time

Risk= F{s(c, f)}


■ Limitations:

1. Unable to capture the dynamic behavior of the process operation

2. Unable to update the quantitative results

3. Unable to take account of early into account

4. Carry significant uncertainty of quantitative estimation

5. No predictive capabilities

6. Utilize for risk assessment in early stage of process life cycle (design stage not in operational, or modification stages)

Dynamic Risk Assessment will overcome these

drawbacks

Current Risk Assessment Approach


Dynamic Risk

Conceptual Design • Risk

FEED • Risk

Detailed design

• Risk

Installation • Risk

Operation • Risk

Dynamic

Risk

Time

Dynamic Risk= F{s(c, f),t}


Picture cursey: Rob Rutenbar CS@Illinois

Symptoms of Accident- Accident Precursors

Rare Event

Grey Swan

Unpredictable

Event

Black Swan

Regular Failure

Statistics

White Swan


Step 2-1

Step 1

Step 2-2

Step 2-3

Step 3

Step 3-2

Step 3-1

Step 223

Operational Risk Assessment


Updating Mechanism

Prior probabilities

FTA)(

ixP

Likelihood probabilities

Accident precursor data

)/(i

xdataP

Bayesian Inference

)()/(

)()/(

ii

ii

xPxdataP

xPxdataP

Posterior probabilities

)/( dataxPi


Application of Operational Risk Assessment Methods


The accident modeling and dynamic risk assessment approach has been applied many case studies, few examples are:

1. Processing facility – BP Texas City Refinery Accident

2. LNG Facility – Liquefaction Unit

Applications of ORA


■ Background Information■ On March 23, 2005, a series of explosions and fires at BP’s

Texas City refinery killed 15 people and injured another 180, alarmed the community, and resulted in financial losses exceeding $1.5 billion

■ There had been a number of previous events in ISOM involving hydrocarbon leaks, vapor releases, and fires

■ BP Incident investigation observed two major incidents occurred just a few weeks prior to the March 23 fatal event:

• February 2005 hydrocarbons leak

• March 2005 fire

BP Texas city Refinery Accident


Overpressure in splitter (~63 psig) have opened the

overhead relief valves to feed directly into unit F-20

(Knockout drum with stack)

This resulted in vaporsand liquid emerging ~20ft above the top of thestack ‘like a geyser’ andrunning down andpooling around the baseof F-20)

BP Texas city Refinery Accident


■ Step 1: Scenario identificationThree possible accident scenario states are identified.

Process upset (A), Process Shutdown (B) and Fluid release (C)

■ Step 2: Prior function calculation


Part of the event tree of ISOM unitTotal events are 190

Prior end-state probabilities are estimated based on prior failure probability of safety barrier


■ Step 3: Formation of Likelihood function

■ Likelihood function is formulated based on accident precursor data■ Based on conjugate property, Likelihood function is taken as binomial

distribution

k

kiki

SBi

iik xxcP ,, 1)1()()(


■ Step 4: Risk Estimation and Perdition

Years Probability of Release

Linear Hazard Model

Probability of Release

Poisson Process

Discrete Cumulative Discrete Cumulative

1 0.0003 3.00×10-4 8.00×10-4 8.00×10-4

2 0.0003 6.00×10-4 8.00×10-4 1.60×10-3

3 0.0003 9.00×10-4 8.00×10-4 2.40×10-3

4 0.0011 2.00×10-3 1.60×10-3 4.00×10-3

5 0.0035 5.50×10-3 3.99×10-3 7.99×10-3

6 0.0043 9.80×10-3 4.79×10-3 1.28×10-2

7 0.0051 1.49×10-2 5.58×10-3 1.84×10-2

11 0.0083 2.32×10-2 8.76×10-3 2.71×10-2

2004

12 0.0091 3.23×10-2 9.55×10-3 3.67×10-22005 Predictive results based on 2004


Accident Modeling and Dynamic risk

estimation of Liquefaction unit


Acid gas

removal unit

Natural

gas

HP C3

Dehydration

unit

Mercury

removal unit

HP C3 MP C3

Heavy gas

removal unit

LP C3

Fractionation

unit

Condensate

storage

HCHE

Compressor

HP C3LP C3 MP C3

End flash

unitLNG expander

LNG storage

Fuel gas expander

Fuel gas

Upstream Processing

Purification

Liquefied and Sub-cooled

Downstream and

Storage


Accident Scenario Analysis

No Date Scenarios Severity Level

1 04.Jan 09 Steam hammering in the low pressure steam line caused a

valve stem cover for a gear operated gate valve to loosen

and fall approximately 15 m to the ground

Near miss

2 12.Jan 09 Upper master valve did not close as required during train

three depressurization

Safe

3 13.Jan 09 Inadvertent flaring due to wrong opening of pressure

control valve on flare line

Near miss

4 14.Jan 09 Gland leak from level control valve when open flame job

was in progress inside low pressure knock-out-drum

Incident

5 15.Jan 09 Inadvertent flaring due to wrong opening of pressure

control valve on flare line

Near miss

6 19.Jan 09 Flame noticed from main combustion chamber of sulphur

recovery unit top side

Mishap


Release

Prevention

Barrier

(RPB)

Dispersion

Prevention

Barrier

(DPB)

Ignition

Prevention

Barrier

(IPB)

Escalation

Prevention

Barrier

(EPB)

Safe

Near miss

Mishap

Incident

Accident

Deviation

from safe

state

Fail

Success

Success

Success

Success

Fail

Fail

Fail



FT Results

• FT are constructed using the proposed generic fault tree models

• The failure of barriers is assumed independent and mutually

exclusive

Safety Barrier (xi) Failure Probability p(xi)

Release Prevention Barrier (RPB) 0.0527

Dispersion Prevention Barrier (DPB) 0.0616

Ignition Prevention Barrier (IPB) 0.1060

Escalation Prevention Barrier (EPB) 0.0271

It is observed that

estimated results

show significant

agreement to real

plant data.


Event Tree Analysis

k

kiki

SBj

iik xxcp ,, 1)1()(

RPB

SB1

DPB

SB2

EPB

SB4

IPB

SB3Consequences

C1 - Safe

C2 – Near miss

C3 - Mishap

C4 - Incident

C5 - Accident

X1

X4

X3

X2

Deviation

from safe

mode

The prior probability of

consequence of severity level (

=1, 2, 3, 4, 5), denoted by , is

given as;

Consequences (ck) Occurrence Probability p(ck)

C1(Safe) 9.4×10-1

C2(Near Miss) 4.9×10-2

C3 (Mishap) 2.9×10-3

C4(Incident) 3.3×10-4

C5(Accident) 9.3×10-6

Severity

Pro

bab

ility


Prediction• The number of abnormal event In the first ten month of year 2009 has been

estimated using the results of HAZOP study

• Based on these data, λp can be estimated

• The mean value of the number of events is estimated as 22. This implies

that the average number of events predicted in the eleventh month is 22.


Updated Probability of Abnormal Event

Month C1(Safe) C2(Near miss) C3 (Mishap) C4 (Incident) C5 (Accident)

1 9.27×10-1 6.90×10-2 3.20×10-3 1.90×10-4 0

2 9.14×10-1 8.30×10-2 2.60×10-3 8.00×10-5 0

3 9.09×10-1 8.80×10-2 2.60×10-3 1.00×10-4 0

4 8.64×10-1 1.32×10-1 3.80×10-3 2.80×10-4 7.68×10-7

5 8.51×10-1 1.44×10-1 4.00×10-3 2.70×10-4 6.24×10-7

6 8.50×10-1 1.46×10-1 3.90×10-3 2.70×10-4 5.69×10-7

7 8.54×10-1 1.42×10-1 3.70×10-3 2.90×10-4 1.14×10-6

8 8.55×10-1 1.41×10-1 3.80×10-3 2.80×10-4 1.03×10-6

9 8.51×10-1 1.45×10-1 3.80×10-3 2.70×10-4 9.42×10-7

10 8.50×10-1 1.45×10-1 4.00×10-3 3.00×10-4 9.21×10-7


Conclusions• The SHIPP methodology help identifying process hazards,

evaluate them, and model probable accident scenarios.

• It provides precise information of how system is degrading with

time and help to predict potential accidents

• It helps to increase the overall safety and performance of the

system by applying preventive measures with the knowledge of

realistic prediction.

• The dynamic risk assessment and management help to identify

process risk early and invite to take appropriate safety action

• It has dynamic learning abilities that is effective in preventing

accidents and enhancing the overall safety performance of the

system

• Source-to-source uncertainty may be modelled using Bayesian

analysis


References related to

presentation

• Al-Shanini, A. Ahmad, A., Khan, F. (2014). Accident modeling and safety measure design of a

hydrogen station. International Journal of Hydrogen Energy, 39(35), 20362-20370.

• Rathnayaka, S., Khan, F., Amayotte, P. (2013). Accident modeling and risk assessment framework

for safety critical decision-making: application to deepwater drilling operation. Proceedings of the

Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 227(1), 86–105.

• Rathnayaka, S., Khan, F., Amyotte, P. (2012). Accident modeling approach for safety assessment in

an LNG processing facility. Journal of Loss Prevention in the Process Industries, 25(2), 414–423.

• Rathnayaka, S., Khan, F., Amyotte, P. (2011). SHIPP methodology: Predictive accident modeling

approach, Part I: methodology and model description. Process Safety and Environmental Protection,

89(3), 151-164.

• Rathnayaka, S., Khan, F., Amyotte, P. (2011). SHIPP methodology: Predictive accident modeling

approach, Part II: validation with case study. Process Safety and Environmental Protection, 89(2),

75-88.

• Kujath, M. F., Amyotte, P., and Khan, F. (2010). A Conceptual offshore oil and gas process accident

model. Journal of Loss Prevention in the Process Industries, 23 (2). 323-330.

• Attwood, D., Khan, F. and Veitch, B. (2006). Occupational accident models-where have we been and

where are we going?, Journal of Loss Prevention in the process industries, 19(6), 664-682.

• Attwood, D., Khan, F. and Veitch, B. (2006). Offshore oil and gas occupational accidents-What is

important?, Journal of Loss Prevention in the Process Industries, 19(5), 386-398.

• Attwood, D., Khan, F. and Veitch, B. (2006). Can we predict process accident frequency?, Process

Safety and Environmental Protection, 84(3B), 208-221.


THANK YOU FOR YOUR ATTENTION!!!!!!!!

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Accident Modeling and Analysis in Process Industriesprosesguvenligi.org/assets/24-october-2015/f-a... · Accident Modeling and Analysis in Process Industries Faisal Khan Centre for