applied

sciences
Article
Availability Estimation of Air Compression and
Nitrogen Generation Systems in LNG-FPSO
Depending on Design Stages
Youngkyun Seo 1 , Jung-Yeul Jung 2, * , Seongjong Han 1 and Kwangu Kang 1
1 Offshore Industries R&BD Center, Korea Research Institute of Ships & Ocean Engineering (KRISO),
Geoje 53201, Korea; ykseo@kriso.re.kr (Y.S.); sjhan@kriso.re.kr (S.H.); kgkang@kriso.re.kr (K.K.)
2 Maritime Safety and Environmental Research Division, Korea Research Institute of Ships & Ocean
Engineering (KRISO), Daejeon 34103, Korea
* Correspondence: jungjy73@kriso.re.kr




Received: 21 October 2020; Accepted: 27 November 2020; Published: 3 December 2020


Abstract: This study estimated availability of an air compression system and a nitrogen generation
system in liquefied natural gas—floating production storage and offloading unit (LNG-FPSO) with
different design stages to investigate the gap between the availability at the early design stage and that
at the late design stage. Although availability estimation in the early design stage is more important
than the late design stage, it is difficult to estimate the availability accurately in the early design stage.
The design stage was divided into three depending on the design progress. Monte Carlo simulation
technique was employed for the availability estimation. The results of the availability estimation
showed that there was 0.434% difference between the early and late design stages. This meant that
the availability in the early design stage was underestimated due to limited information. A sensitivity
analysis was performed to investigate critical factors affecting the results. The investigated factors
were failure rate, repair time, redundant equipment, and modified preventive maintenance schedule.
The most critical factor was redundant equipment. It increased 0.486% availability.

Keywords: air compression system; nitrogen generation system; utility module; availability;
sensitivity analysis

1. Introduction
Various factors are considered in system design, such as efficiency, costs, safety, and environmental
effect. Availability is also one of the important issues in the system design. The definition
of the availability from BS4778-3.1 (British standards, quality vocabulary, availability, reliability,
and maintainability terms.) Guide to concepts and related definitions is the ability of an item under
the combined aspects of reliability, maintainability, and maintenance support to perform its required
function at a specified instant or for a specified period [1]. The availability indicates that how much a
system approaches ideal operation without production loss caused by equipment failures or undesired
external events. Availability estimation is frequently performed in the oil and gas, chemical, and power
plant industries to find the optimum design option, to predict the production level, and to evaluate
maintenance and operating policies.
Many previous studies conducted the availability estimation for various systems to improve their
designs. Basker and Martin [2] estimated the availability of production and electrical systems using the
developed numerical method. They considered failure and repair rates following the non-exponential
distribution. Keller and Stipho [3] conducted the availability estimation for two similar chlorine
production plants which were located in different environmental conditions (Iraq and Switzerland).

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Appl. Sci. 2020, 10, 8657 2 of 16

They employed the concept of “delayed time” to take into account the additional time required to
reach full production rate. Bosman [4] estimated the availability of a natural gas compressor plant to
investigate its unavailability. Since the plant had no backup systems, the unavailability estimation
was crucial. They concluded that the availability analysis provided useful information to determine
the optimum number of spares. Aven [5] indicated the methodologies for the availability estimation
of oil/gas production and transport systems. He described not only an analytical approach but also
a simulation method for the availability estimation. Khan and Kabir [6] conducted the availability
estimation for an ammonia plant using both analytical and simulation method. They concluded that the
performance of the plant could be improved by changing the overhaul strategy and plant configuration.
Hajeeh and Chaudhuri [7] analyzed the availability of a reverse osmosis (RO) plant for producing
potable water from seawater through desalination. They employed failure mode effect analysis (FMEA)
and fault tree analysis (FTA) techniques to investigate the downtime pattern and failure. Zio et al. [8]
assessed the availability of an offshore installation using Monte Carlo simulation. Marquez et al. [9]
suggested a general approach for the reliability and availability assessment of complex systems by
employing Monte Carlo simulation. They validated the proposed approach by performing a case study
for cogeneration plants. Michelassi and Monaci [10] estimated the availability of a gas re-injection plant
for the oil and gas production. They utilized reliability block diagram (RBD) techniques in conjunction
with Monte Carlo simulation. They also considered the leak because the plant should be stopped when
the leak was detected. Chang et al. [11] estimated the availability of conventional and novel propulsion
systems with a BOG handling system of an LNG carrier. They estimated the availability depending on
the required function to prevent rough evaluation: design propulsion load, emergency propulsion load,
and BOG utilization. Görkemli and Ulusoy [12] suggested a new modeling approach for predicting the
availability of a production system. They considered not only machine failures but also the material
supply, management, and set-up in the proposed method. They also investigated the uncertainties
caused by the various production environment using a fuzzy Bayesian method. Seo et al. [13] predicted
the availability of CO2 liquefaction processes for a ship-based carbon capture and storage (CCS)
chain and they converted the availability to unavailability cost to calculate the life-cycle cost (LCC).
Seo et al. [14] estimated the availability of LNG fuel gas supply systems to evaluate economics of them.
They concluded that one of the significant factors was mechanical devices. Gowid et al. [15] reviewed
the studies related with the profitability, reliability, and condition based monitoring of liquefied natural
gas-floating production storage and offloading unit (LNG-PFSO). They assumed that the efficiency of
LNG-PFOS depends on LNG liquefaction process type, system reliability, and maintenance approach,
and reviewed the paper at theses points. They concluded that the literature was not sufficient to
improve efficiency of LNG-FPSO. Hwang et al. [16] developed the condition-based maintenance system
to perform proactive maintenance in advance to avoid the abnormal states. They addressed the system
architecture, main components, diagnostics, and prognostic methods of the system.
The methodologies for the availability estimation has been improved to increase its accuracy and
to apply to various systems. Precise availability estimation is important because it directly influences
the owner’s decision. The availability estimation is performed several times depending on the design
stages (conceptual design, basic design, and detailed design stages). In the early design stage, the results
of availability estimation are effective for design improvement, but it is hard to estimate it precisely
due to the limited data. On the contrary, accurate availability estimation is possible at the end of the
design stage using sufficient data, but it accompanies high costs for the system modification. Therefore,
it is an important to estimate the availability in the early design stage accurately. Although many
studies improved the methods to increase their accuracy, there was little effort to practically estimate
the availability in the early design stage
The purpose of this study is to investigate the availability gap between in the early and late design
stages by estimating it with the design stages to find practical manner of availability estimation in
the early design stage. The structure of this study is as follows. The target systems are described.
Appl. Sci. 2020, 10, x FOR PEER REVIEW 3 of 17
Appl. Sci. 2020, 10, 8657 3 of 16
described. In Section 3, methodologies for the availability estimation are discussed. The results of the
availability estimation and the sensitivity analysis are indicated in Section 4. Finally, the conclusions
In Section 3, methodologies for the availability estimation are discussed. The results of the availability
are presented.
estimation and the sensitivity analysis are indicated in Section 4. Finally, the conclusions are presented.
2. Description of Target System
2. Description of Target System
In this study, two systems in LNG-FPSO is selected as a target for the availability estimation.
In this study, two systems in LNG-FPSO is selected as a target for the availability estimation.
These are air compression and nitrogen generation systems in LNG-FPSO. LNG-FPSO is a huge
These are air compression and nitrogen generation systems in LNG-FPSO. LNG-FPSO is a huge facility
facility for LNG production in offshore, and its concern has been increased because of the growing
for LNG production in offshore, and its concern has been increased because of the growing demand
demand for LNG. LNG-FPSO is a floating unit for production, processing, storage, and offloading of
for LNG. LNG-FPSO is a floating unit for production, processing, storage, and offloading of LNG
LNG in remote offshore gas fields. Conventionally, the natural gas in an offshore field is transported
in remote offshore gas fields. Conventionally, the natural gas in an offshore field is transported by
by pipeline to onshore for processing. LNG-FPSO does not require the pipeline because it processes the
pipeline to onshore for processing. LNG-FPSO does not require the pipeline because it processes
natural gas itself in offshore. It is specialized for small scale gas field. Topside modules of the LNG-
the natural gas itself in offshore. It is specialized for small scale gas field. Topside modules of the
FPSO can be categorized into two: a processing module and a utility module. The processing module
LNG-FPSO can be categorized into two: a processing module and a utility module. The processing
handles the primary hydrocarbon, whereas the utility module deals with utilities including energy,
module handles the primary hydrocarbon, whereas the utility module deals with utilities including
water, air, and diesel oil. The utility module provides utilities to the processing system for safe and
energy, water, air, and diesel oil. The utility module provides utilities to the processing system for
stable operation. Some failure of the utility module can be critical because safety systems for
safe and stable operation. Some failure of the utility module can be critical because safety systems for
preventing an accident are operated by the utility module.
preventing an accident are operated by the utility module.
The topside of LNG-FPSO can be divided into ten modules as shown in Figure 1. A produced feed
The topside of LNG-FPSO can be divided into ten modules as shown in Figure 1. A produced feed
gas come up through a turret, and it is transported to an inlet facility module. Slug in the feed gas is
gas come up through a turret, and it is transported to an inlet facility module. Slug in the feed gas is
removed by a slug catcher, and liquid is separated by a separator. CO2, Hg, and H2O in the feed gas is
removed by a slug catcher, and liquid is separated by a separator. CO2 , Hg, and H2 O in the feed gas is
removed in a pre-treatment module. The treated natural gas is liquefied by a liquefaction module, and
removed in a pre-treatment module. The treated natural gas is liquefied by a liquefaction module, and a
a refrigeration module supplies the refrigerant to the liquefaction module. The heavier components
refrigeration module supplies the refrigerant to the liquefaction module. The heavier components than
than methane like ethane, butane, and propane are separated by a fractionation module. Some amounts
methane like ethane, butane, and propane are separated by a fractionation module. Some amounts of
of natural gas are transferred to a fuel gas compression system, and it is utilized for power generation.
natural gas are transferred to a fuel gas compression system, and it is utilized for power generation.
The liquefied natural gas is stored in storage tanks with LPG and condensate. A condensate stabilizer
The liquefied natural gas is stored in storage tanks with LPG and condensate. A condensate stabilizer
module separates the relatively light components for safe operation. Condensate is mainly composed
module separates the relatively light components for safe operation. Condensate is mainly composed
of propane, butane, pentane, and heavier hydrocarbon. When condensate contains light components
of propane, butane, pentane, and heavier hydrocarbon. When condensate contains light components
like methane and ethane, it can be vaporized and increase the pressure of a storage tank during storage.
like methane and ethane, it can be vaporized and increase the pressure of a storage tank during storage.
These light components should be separated before storage. A blowdown module treats combustion
These light components should be separated before storage. A blowdown module treats combustion
fluids in emergency situations. The utility module supplies various utilities to other modules for the
fluids in emergency situations. The utility module supplies various utilities to other modules for
operation.
the operation.

Figure 1. Topside modules of liquefied natural gas—floating production storage and offloading unit
Figure 1. Topside modules of liquefied natural gas—floating production storage and offloading unit
(LNG-FPSO).
(LNG-FPSO).

In this study, air compression and nitrogen generation systems are analyzed in the utility module
In this study, air compression and nitrogen generation systems are analyzed in the utility
because those are important systems for stable and safe operation. A general utility module contains
module because those are important systems for stable and safe operation. A general utility module
Appl. Sci. 2020, 10, x FOR PEER REVIEW 4 of 17

contains an instrument and service air system, a nitrogen generation system, a cooling water system,
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a seawater system, a hot oil system, a portable water system, a produced and wastewater system, and
a diesel oil system. The instrument and service air system compresses the air up to approximately 10
contains an instrument and service air system, a nitrogen generation system, a cooling water system,
an instrument
bar for the usage andthe
of service air system, a nitrogen
instrument generation system, a coolingsystem
water system, a seawater
a seawater system, a hot oil system,and others.water
a portable The nitrogen
system, generation
a produced and wastewater supplies
system,nitrogen
and to
system,
the customers. a hot oil system, a portable water system, a produced and wastewater system, and athe
diesel
a diesel oil system. The instrument and service air system compresses the air up to approximatelytopside
The cooling water system is used to provide the cooling medium for all of 10
oil system. The instrument and service air system compresses the air up to approximately 10 bar
modules. The sea water system provides the seawater to various systems.
bar for the usage of the instrument and others. The nitrogen generation system supplies nitrogen to The hot oil system increases
for the usage of the instrument and others. The nitrogen generation system supplies nitrogen to the
the temperature
the customers.ofThe thecooling
oil within
waterasystem
specified range.
is used It utilizes
to provide waste medium
the cooling heat from forflue
all ofgas
theusing
topsidewaste
customers. The cooling water system is used to provide the cooling medium for all of the topside
modules.
heat modules.
recoveryThe The sea
units water system provides the seawater to various systems.
installed in a power generation system. The portable water system distributesThe hot oil system increases
sea water system provides the seawater to various systems. The hot oil system increases
waterthetotemperature
topside of the oil and
eyewash within a specified
safety shower, range. andItItutilizes
hot andwastecoldheat fromfor
water fluepersonal
gas usingusage.
waste
the temperature of the oil within a specified range. utilizes waste heat from flue gas using waste The
heat
produced recovery units
and wastewater installed
systemin a power generation system. The portable water system distributes
heat recovery units installed in a removes the oil insystem.
power generation the produced
The portable water from
water topside
system separators.
distributes
water
The diesel to topside
oiltopside
system eyewash
distributes and safety
the diesel shower,
oiland and
to customers hot and cold water for personal usage.it. The
water to eyewash and safety shower, hot and coldby transferring
water for personal and purifying
usage. The produced
produced and wastewater system removes the oil in the produced water from topside separators.
Figure
and 2 indicates
wastewater systemtheremoves
air compression
the oil in theand nitrogen
produced watergeneration
from topside systems. The The
separators. systems
diesel mainly
oil
The diesel oil system distributes the diesel oil to customers by transferring and purifying it.
system
consist distributes
of three piecesthe diesel oil to customers
of equipment; by transferring
an air compressor, an airand purifying
dryer, and it.
a nitrogen generator. Air is
Figure 2 indicates the air compression and nitrogen generation systems. The systems mainly
compressed Figure 2 indicates the air compression and nitrogen generation systems. inThe systems mainlyair is
consist of by threethepieces
air compressor,
of equipment;and an airthen the small
compressor, anamount
air dryer,of water
and a nitrogen thegenerator.
compressed Air is
consist
dehydrated of three
by the pieces of
air dryer. equipment;
The dry air an air compressor, an air dryer, and a nitrogen generator. Air is
compressed by the air compressor, andisthensentthe to asmall
customer
amount requiring
of waterthe in instrument
the compressed air and
air isto the
compressed by the air compressor, and then the small amount of water in the compressed air is
nitrogen generator.
dehydrated by the Theair nitrogen
dryer. Thegenerator separates
dry air is sent the nitrogen
to a customer requiringfromthethe dry air. air and to the
instrument
dehydrated by the air dryer. The dry air is sent to a customer requiring the instrument air and to the
nitrogen generator. The nitrogen generator separates the nitrogen from the dry air.
nitrogen generator. The nitrogen generator separates the nitrogen from the dry air.

Figure 2. Block diagram of air and nitrogen systems.

Figure
Figure2.2.Block
Blockdiagram of air
diagram of airand
andnitrogen
nitrogen systems.
systems.
Figures 3–5 show process diagrams with different design stages. Figure 3 is a process flow diagram
Figures
(PFD) of3–5
Figures show
the3–5
airshow process
compressiondiagrams
process diagrams with
with different
and nitrogen different design
design
generation stages.
stages.
systems. Figure
Figure
PFD 3 is3ais
shows a process
process
the mainflow flow diagram
diagram
equipment
(PFD)
(PFD)inof of
the
the the air compression
air compression
system. and nitrogen
and nitrogen
The preliminary generation
process generation systems.
systems.
and instrument PFD shows
PFD(P&ID)
diagram the
showsisthemain equipment
main equipment
indicated in
in Figures thein
4 the
system.
system. 5. The
andThe P&ID preliminary process
includesprocess
preliminary not only and
and instrument
theinstrument diagram
main equipment
diagram but(P&ID) is is
indicated
also piping,
(P&ID) in Figures
instrumentation,
indicated 4 and
in Figures and 5.control
4 andP&ID
5. P&ID
includes
includes not not
devices. In only
onlythis the main
thestudy,
mainthe equipment but also
also piping,
piping information
equipment but is not instrumentation,
piping, contained because
instrumentation, andand
it control devices.
is unnecessary
control In this
for
devices. the
In this
study, the piping information
availability estimation. is not contained because it is unnecessary for the availability estimation.
study, the piping information is not contained because it is unnecessary for the availability estimation.
2nd stage Instrument Air
Supply
2nd stage Instrument Air
st Supply
1 stage
Nitrogen supply
1st stage
Inter-cooler
1st stage Nitrogen supply
Air
st
Inter-cooler Heater Membrane
1 stage
Air
Separator I Heater Membrane

Motor Separator I
Compressor Buffer Tank
(2 Stages)
Motor Compressor 2nd stage Dryer A Dryer B Buffer Tank
(2 Stages) After-cooler (Drying) (Reactivating)
2nd stage Dryer A Dryer B
After-cooler (Drying) (Reactivating)
Separator II

Figure 3. Process
Separator IIflow diagram (PFD) of air and nitrogen system.

Figure 3. 3.
Figure Process flow
Process flowdiagram
diagram(PFD) of air
(PFD) of airand
andnitrogen
nitrogen system.
system.
Appl. Sci. 2020, 10, 8657 5 of 16
Appl. Sci. 2020, 10, x FOR PEER REVIEW 5 of 17

Figure 4. The preliminary process and instrument diagram (P&ID) of air and nitrogen system.

The design stage considered in this study are three. The first stage is PFD and the second stage
is the preliminary P&ID. The third stage is preliminary P&ID with the information on preventive
maintenance.
1. Stage I—PFD
2. Stage II—Preliminary P&ID
3. Stage III—Preliminary
Figure
Figure4.4.The P&ID
Thepreliminary
preliminary + Information
process
process and on Preventive
and instrument Maintenance
diagram (P&ID) of air and nitrogen system.

The design stage considered in this study are three. The first stage is PFD and the second stage
is the preliminary P&ID. The third stage is preliminary P&ID with the information on preventive
maintenance.
1. Stage I—PFD
2. Stage II—Preliminary P&ID
3. Stage III—Preliminary P&ID + Information on Preventive Maintenance

Figure 5. P&ID of air and nitrogen system with information on preventive maintenance.

The design stage considered in this study are three. The first stage is PFD and the second stage is the
preliminary P&ID. The third stage is preliminary P&ID with the information on preventive maintenance.
Appl. Sci. 2020, 10, 8657 6 of 16

1. Stage I—PFD
2. Stage II—Preliminary P&ID
Appl. Sci. 2020, 10, x FOR PEER REVIEW 6 of 17
3. Stage III—Preliminary P&ID + Information on Preventive Maintenance
3. Methodology
3. Methodology
Several methods are available for the availability estimation: reliability block diagram (RBD),
Several methods are available for the availability estimation: reliability block diagram (RBD),
Markov model, and Monte Carlo simulation [17,18]. The former two are an analytical approach
Markov model, and Monte Carlo simulation [17,18]. The former two are an analytical approach
whereas the latter one is a simulation approach. The analytical approach calculates the availability
whereas the latter one is a simulation approach. The analytical approach calculates the availability
using mathematical equations, while the simulation technique estimates it by generating scenarios.
using mathematical equations, while the simulation technique estimates it by generating scenarios.
When the system is complex, the analytical approaches like RBD and Markov model are unrealistic.
When the system is complex, the analytical approaches like RBD and Markov model are unrealistic.
They are additionally difficult to apply to the system, which has nonconstant failure and repair rates.
They are additionally difficult to apply to the system, which has nonconstant failure and repair
However, the Monte Carlo simulation approach can handle inconstant failure/repair rates and multi-
rates. However, the Monte Carlo simulation approach can handle inconstant failure/repair rates and
state systems. One of the drawbacks of the Monte Carlo simulation is the long simulation time, but it
multi-state systems. One of the drawbacks of the Monte Carlo simulation is the long simulation time,
can be overcome by the advanced simulation techniques. In this study, Monte Carlo simulation is
but it can be overcome by the advanced simulation techniques. In this study, Monte Carlo simulation
employed for the availability estimation.
is employed for the availability estimation.
Figure 6 shows the procedure for the availability estimation using Monte Carlo Simulation. First
Figure 6 shows the procedure for the availability estimation using Monte Carlo Simulation. First of
of all, the target system is analyzed, and then the reliability block diagram is drawn for the modeling
all, the target system is analyzed, and then the reliability block diagram is drawn for the modeling of the
of the system. The data for reliability and maintainability is collected from the data sources. The
system. The data for reliability and maintainability is collected from the data sources. The availability
availability of the target system is estimated using the Monte Carlo Simulation. The followings are
of the target system is estimated using the Monte Carlo Simulation. The followings are the details of
the details of each step.
each step.

Figure 6. Procedure for availability estimation using Monte Carlo simulation.

Figure 6. Procedure for availability estimation using Monte Carlo simulation.
3.1. STEP 1 System Analysis
3.1. STEP 1 System Analysis
First, the information required for the availability estimation is gathered, and the system is
First,The
analyzed. the boundary
information
andrequired
a level of for the availability
the system estimation in
analysis determined is this
gathered, andgiven
step. The the operating
system is
analyzed. and
conditions The assumptions
boundary andfor athelevel of the system
availability analysis
estimation determined
are determined. in this
Those step.the
include The given
lifespan
operating
of conditions
the system, numberand assumptionsdistribution
of simulations, for the availability
functionestimation
of failure, are determined.
distribution Thoseofinclude
function repair
the lifespan
time, of therate,
unit of failure system, number
and unit of simulations,
of repair time. Table 1distribution
tabulates the function of failure, distribution
information.
function of repair time, unit of failure rate, and unit of repair time. Table 1 tabulates the information.
3.2. STEP 2 Determination of Reliability Block Diagram (RBD)
RBD is a block structureTable
to show1. Operation conditions
success logic and assumption.
of a system. The blocks represent equipment or
components of the system to fulfill
Items a specified function. Success path can be visually verified so that it
Lifespan 20 years
Number of simulations 250
Distribution function of failure Exponential
Distribution function of repair time Constant
Unit of failure rate Number of failure/106 h
Unit of repair time Hours
Appl. Sci. 2020, 10, 8657 7 of 16

can be easily understood. In this step, the RBD of the system is determined based on Step 10 s results.
The followings indicate the RBD with the different design development states.

Table 1. Operation conditions and assumption.

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Items
Unit Lifespan
of repair time Hours
20 years
Number of simulations 250
3.2. STEP 2 Determination
Distribution of Reliability
function of Block Diagram (RBD)
failure Exponential
Distribution
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10, x FOR PEER REVIEW of repair time Constant 7 of 17
RBD is a block structure to show success logic of a system. The blocks represent equipment or
Unit of failure rate Number of failure/106 h
components
3.2. STEP of2the system tooffulfill
Determination a specified
Reliability function.
Block Diagram (RBD) Success path can be visually verified so that
Unit of repair time Hours
it can be easily understood. In this step, the RBD of the system is determined based on Step 1′s results.
RBD is a block structure to show success logic of a system. The blocks represent equipment or
The followings
components indicate the RBD
of the system withathe
to fulfill different
specified design
function. development
Success states.
path can be visually verified so that
3.2.1. RBDitat Stage I (PFD Stage)
can be easily understood. In this step, the RBD of the system is determined based on Step 1′s results.
3.2.1. RBD at Stage Iindicate
(PFD Stage)
FigureThe followings
7 shows the RBD atthe RBD with the different design development states.
Stage I. It is divided into three parts as shown in Figure 6: air compression,
Figure
air dryer, and 7 shows
nitrogen the RBD parts.
generation at Stage TheI. configuration
It is divided into three
of the parts as shown
air compressor partinis 3Figure
× 50%.6: Itair
means
3.2.1. RBD at Stage I (PFD Stage)
compression,
that three compressors air dryer, and nitrogen
are installed, generation
and the capacity parts. The configuration
of eachthreecompressor of the
is 50%.air compressor
Two part
compressors are
is 3 × 50%.Figure
It means 7 shows the RBD
that three at Stage I. are
compressors It isinstalled,
divided into parts as of
and the capacity shown
each in Figure 6: air
compressor is 50%.
in operation, and one compressor is on standby for a failure of the operating
compression, air dryer, and nitrogen generation parts. The configuration of the air compressor part compressors. The air
Two compressors are in operation, and one compressor is on standby for a failure of the operating
dryer partishas 2 × 100%
3 × 50%. It means configuration. One air
that three compressors are dryer is and
installed, redundancy.
the capacity ofIneach
the compressor
nitrogen generation
is 50%. part,
compressors. The air dryer part has 2 × 100% configuration. One air dryer is redundancy. In the
the membrane Two compressors
has the 4part,are in
× 33% operation, and one compressor is on standby for a failure of the operating
nitrogen generation
compressors. the configuration.
The air dryer membrane
part has Three
has 2 × 100% the 4 ×membranes
33% One
configuration.
are operated,
configuration. and the remaining
Three membranes
air dryer is redundancy. In the are
membrane
operated,stands
nitrogen
by for
and generation a
the remaining failure.
part,membrane
the membrane stands
hasby thefor
4 a× failure.
33% configuration. Three membranes are
operated, and the remaining membrane stands by for a failure.

Figure
7. 7.Reliability
Reliabilityblock
block diagram atatStage I (PFD stage).
FigureFigure 7. Reliability blockdiagram Stage
diagram at Stage I (PFD
I (PFD stage).stage).

3.2.2.3.2.2.
RBDRBD at Stage
3.2.2. atIIStage
at Stage
RBD (Preliminary
II (Preliminary P&ID)
P&ID)
II (Preliminary P&ID)
Figures
Figures 8–10
Figures
8–10 indicate
8–10
indicate thethe
indicate RBD
the
RBD RBD atatStage
at Stage Stage IIII (for
II (for (for
thethe preliminaryP&ID
thepreliminary
preliminary P&ID
P&ID stage).
stage). Figures
Figures
stage). 8–10 8–10
Figures 8–10show
show show the
RBD the theair
for RBD
the RBD
for forair
the thecompression,
air compression,
compression, airair
air dryer, dryer,nitrogen
dryer,
and andnitrogen
and nitrogen generation
generation
generation parts, respectively.
parts,
parts, respectively.
respectively.

Figure 8. Reliability block diagram at Stage II (preliminary P&ID stage)—Air compression part.
Figure 8. Reliability block diagram at Stage II (preliminary P&ID stage)—Air compression part.
Appl. Sci. 2020, 10, x FOR PEER REVIEW 8 of 17

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Sci. 2020, 10, x FOR PEER REVIEW 8 of 178 of 16

Figure 9. Reliability block diagram at Stage II (preliminary P&ID stage)—Air dryer part.
Figure 9. Reliability block diagram at Stage II (preliminary P&ID stage)—Air dryer part.
Figure 9. Reliability block diagram at Stage II (preliminary P&ID stage)—Air dryer part.

Figure 10. Reliability

Figure block
10. Reliability diagram
block diagramatatStage
StageIIII (preliminary P&IDstage)—Nitrogen
(preliminary P&ID stage)—Nitrogen generation
generation part.part.
Figure 10. Reliability block diagram at Stage II (preliminary P&ID stage)—Nitrogen generation part.
3.2.3. RBD at Stage III (Preliminary P&ID + Information on Preventive Maintenance)
3.2.3. RBD at Stage III (Preliminary P&ID + Information on Preventive Maintenance)
3.2.3.RBD
RBDRBD
atatStage
Stage IIIIIIis
at Stage (Preliminary
almost
III is almost P&ID
identical
identical + Information
with
with that
that for onIIIIPreventive
stage
for stage excepting
excepting theMaintenance)
the additional
additional information
information on on
preventive maintenance.
preventive maintenance. One block for
Oneidentical the
block forwithpreventive
the preventive maintenance
maintenance is added
is added for stage III.
RBD at Stage III is almost that for stage II excepting the for stage III.information on
additional
preventive
3.3. maintenance.
Step 3 Data Collection One block for the preventive maintenance is added for stage III.
The reliability and maintenance data are required for the availability estimation. Since the results
of the availability estimation are significantly influenced by the reliability and maintenance data,
they are important. Reliability data is linked to the failure rate. The maintenance data is associated with
the corrective maintenance time (repair time) and the preventive maintenance time. When the failure
occurs, the corrective maintenance is conducted to a system. Preventive maintenance is performed on
the basis of maintenance policies and strategies. The data can be categorized into three depending
Appl. Sci. 2020, 10, 8657 9 of 16

on the kinds of sources: Open data (from open books and reports), vendor data, and in-house data.
This study uses the OREDA (Offshore and onshore reliability data) and vendor data. OREDA is
offshore and onshore reliability data handbook sponsored by oil and gas companies. It is considered
a unique data source in the offshore industry. OREDA is employed in this study because it is the
most suitable for it [19,20]. Vendor data is taken from a manufacturer of air compression and nitrogen
generation systems. Table 2 indicates the reliability and maintenance data employed in this study.

Table 2. Reliability and maintainability data for air and nitrogen system.

Failure Rate Active Repair Time

Items Source
Lower Mean Upper Mean Max.
Compressor OREDA 2009 - 140.84 779.34 15 98
Electric motor OREDA 2015 0.87 7.52 19.63 16 25
Heat exchanger OREDA 2015 0.28 64.94 243.9 28 96
Separator OREDA 2015 0.34 73.49 271.39 6.4 12
Dryer OREDA 2015 18.32 29.22 44.37 6.2 11
Heater OREDA 2015 244.7 349.24 484.57 14 84
Membrane OREDA 2015 18.32 29.22 44.37 6.2 11
Filter * OREDA 2015 - 4.67 - 1.105 -
Relief valve OREDA 2015 0.04 2.07 6.41 6.9 13
Check valve OREDA 2015 0.01 2.47 9.24 2 2
Ball valve (Utilities) OREDA 2015 2.73 11.65 25.65 5 6
Ball valve (Condensate processing) OREDA 2015 12.08 72.09 226.9 27 39
Control valve OREDA 2015 0.99 19.8 93.96 9 9
Gate valve OREDA 2015 0.04 3.74 12.37 20 72
Control logic unit OREDA 2015 0.08 17.4 64.59 - -
Pressure input device OREDA 2015 0.01 1.09 3.73 7 12
Trap Vender - 16.31 22.83 2 -
* This data is regenerated using the component data in Offshore and onshore reliability data (OREDA) 2015.

Table 3 indicates the information on the preventive maintenance. The preventive maintenance
is conducted to prevent unexpected future failure. It is classified into four categories: age-based,
clock-based, condition-based, and opportunity maintenance [18]. In the age-based maintenance,
the preventive maintenance is performed at the defined age of the system (e.g., the number of
take-offs/landings for an airplane). The clock-based maintenance is carried out at specified calendar
time so that it is scheduled by administers. In the condition-based maintenance, the preventive
maintenance is initiated by measuring condition variables. The opportunity maintenance is carried
out when the system is stopped by the other failure. In this study, the clock-based maintenance is
taken into account for the preventive maintenance, and the data is collected from the vendor of the air
compression and nitrogen generation systems.

Table 3. Preventive maintenance information on air compression and nitrogen generation systems.

Periodic Maintenance Time

Equipment
(month) (hour)
Component replacement 1 6 0.5
Air compressor Component replacement 2 24 3
Main maintenance 36 72
Component replacement 1 6 0.5
Air dryer Component replacement 2 24 1
Main maintenance 36 24
Component replacement 1 6 0.5
Nitrogen generator Component replacement 2 24 1
Main maintenance 36 24
 (month) (hour)
Component replacement 1 6 0.5
Air compressor Component replacement 2 24 3
Main maintenance 36 72
Appl. Sci. 2020, 10, 8657
Component replacement 1 6 0.5 10 of 16
Air dryer Component replacement 2 24 1
Main maintenance 36 24
3.4. Step 4 Monte Carlo Simulation Component replacement 1 6 0.5
Monte Nitrogen generator is employed
Carlo simulation Component replacement
to estimate 2 24 Figure 11 shows1the flowchart
the availability.
Main maintenance
of the Monte Carlo simulation [21]. First of all, components, their 36states, and their24
configuration
are defined. Moreover, the next transition time for each component is estimated by the random
3.4. Step 4 Monte Carlo Simulation
number generation. The transition time is the time when the phase of a component in the system
is changed
Montefrom normal
Carlo to failure.
simulation In this step,
is employed the generated
to estimate random Figure
the availability. number 11isshows
converted into a
the flowchart
value of Monte
of the time using
Carloasimulation
conversion[21].
method
First at
of aall,
cumulative distribution
components, function.
their states, and theirFigure 12 showsare
configuration
how the generated
defined. Moreover, random number
the next is transferred
transition time for each to component
the value ofistime by theby
estimated conversion
the randommethod.
number
The cumulative distribution function for the exponential distribution is indicated in Equation
generation. The transition time is the time when the phase of a component in the system is changed (1).
from normal to failure. In this step, the generated random number is converted into a value of time
using a conversion method at a cumulative ) = 1 − e−λx function. Figure 12 shows how the generated
F(xdistribution (1)
random number is transferred to the value of time by the conversion method. The cumulative
where λ is the failure rate, and x is a value of time.
distribution function for the exponential distribution is indicated in Equation (1).
F(x) =
R = 𝐹(𝑥) − e𝑒−λx
= 11 − −𝜆𝑥 (1)
(2)
where λ is the failure rate, and x is a value of time.
where R is the random number between 0 and 1. R* is a generated random number between 0 and 1.

Figure 11. Procedure for availability estimation using Monte Carlo simulation [21].
Figure 11. Procedure for availability estimation using Monte Carlo simulation [21].
The shortest transition time is found among all of the predicted times, and then the system time
is changed to the shortest transition time. If the time is shorter than the mission time, the transition
times for all component are estimated again. The mission time is total operation time required to
the system like lifespan. When the time is longer than the mission time, the system’s availability
is calculated. This process is just one simulation. If the number of simulations is lower than the
desired number of simulations, the next simulation is repeatedly performed. The desired number of
simulations is determined as referring the convergence of results. When a result converges sufficiently,
the number of simulations is selected as the desired number of simulations. The desired number of
simulations is determined as setting a sufficiently high number of simulations or determining the
Appl. Sci. 2020, 10, 8657 11 of 16

number of simulations after the initial simulation. When the number of simulations is the same as
the desired number of simulations, the average system availability is calculated finally. The average
Appl. Sci. 2020, 10, x FOR PEER REVIEW 11 of 17
system availability is the result after the last simulation, while the system availability is the result of
each simulation.

Figure 12. Conversion method to transfer random number to value of time at cumulative distribution.
Figure 12. Conversion method to transfer random number to value of time at cumulative distribution.
The predicted time from the generated random number is shown in Equation (3).

𝑅 = 𝐹(𝑥) = 11 − 𝑒 −𝜆𝑥 (2)

x = F−1 (R) = − ln(1 − R) (3)
λ
where R is the random number between 0 and 1. R* is a generated random number between 0 and 1.
The predicted
4. Results time from the generated random number is shown in Equation (3).
and Discussion
1
4.1. Availability 𝑥 = 𝐹 −1 (𝑅) = − ln(1 − 𝑅) (3)
𝜆
Figure 13 shows
The shortest the availability
transition of the air
time is found compression
among all of theand nitrogen
predicted generation
times, and then systems depending
the system time
on the design stages. The availability decreased with the increment of the design
is changed to the shortest transition time. If the time is shorter than the mission time, the transition stages because the
system in the late design stage was more complex than that in the early
times for all component are estimated again. The mission time is total operation time required to thedesign stage. A complex
system
system haslikemore factors
lifespan. decreasing
When the availability
the time is longer than of the
thesystem thantime,
mission a simple system. The
the system’s availability
availability is
is decreased by 0.331% when the design stage was changed from Stage
calculated. This process is just one simulation. If the number of simulations is lower than the I (PFD) to Stage II (P&ID).
desired
This
numbermeant of that the instrument
simulations, the nextsystem occupies is
simulation 0.331% of the system’s
repeatedly performed.availability.
The desiredWhennumber
the design of
stage was transferred from Stage II (P&ID) to Stage III, the availability
simulations is determined as referring the convergence of results. When a result converges was decreased by 0.103%.
The preventive
sufficiently, the maintenance influencedisabout
number of simulations selected0.103%
as theofdesired
the availability.
number of The availabilityThe
simulations. difference
desired
between Stage I and Stage III was 0.434%. It showed that the availability
number of simulations is determined as setting a sufficiently high number of simulations in the early design stage
or
was underestimated compared to the late design stage. The unavailability
determining the number of simulations after the initial simulation. When the number of simulations (1—availability) in the late
design stageas(0.972%)
is the same the desired is approximately 1.8 times the
number of simulations, severe than system
average that in availability
the early design stage (0.535%).
is calculated finally.
We
Thecan predict
average that the
system unavailability
availability is theestimated
result after in the
the late
last design stagewhile
simulation, is 1.8 times serious
the system than that in
availability is
the
the early
resultdesign
of each stage. The availability difference between early and late design stages can be dissimilar
simulation.
with the target system. However, this result provides meaningful information to guess the actual
availability
4. Results and in the early design stage.
Discussion

4.2. Component Criticality

4.1. Availability
Figure
Figure 14 13 shows
shows thethecomponent
availabilitycriticality depending
of the air on the
compression anddesign stages.
nitrogen The component
generation systems
criticality shows the important component of the availability, and it is the ratio
depending on the design stages. The availability decreased with the increment of the design of the component’s
stages
failure time to the system failure time. The most crucial component at Stage
because the system in the late design stage was more complex than that in the early design stage. A I was the heater,
which
complex accounted
system has formore
90.3%factors
criticality. The heater
decreasing and ball valve
the availability of the(condensate)
system than were critical
a simple in Stage
system. The
II
availability is decreased by 0.331% when the design stage was changed from Stage I (PFD) todesign
and Stage III. The heater and ball valve (condensate) had 50.7% and 20.0% criticality at the Stage
Stage III, respectively.
II (P&ID). This meant that The the
preventive maintenance
instrument occupied
system occupies aboutof10%
0.331% the on the criticality
system’s at Stage
availability. WhenIII.
The most critical component was the heater regardless of the design stages. The availability
the design stage was transferred from Stage II (P&ID) to Stage III, the availability was decreased by of the
system
0.103%.can Thebepreventive
significantly increased as
maintenance installingabout
influenced redundant
0.103%heaters.
of theThe results ofThe
availability. the component
availability
difference between Stage I and Stage III was 0.434%. It showed that the availability in the early design
stage was underestimated compared to the late design stage. The unavailability (1—availability) in
the late design stage (0.972%) is approximately 1.8 times severe than that in the early design stage
(0.535%). We can predict that the unavailability estimated in the late design stage is 1.8 times serious
than that in the early design stage. The availability difference between early and late design stages
Appl. Sci. Appl. 10,2020,
2020,Sci. 865710, x FOR PEER REVIEW 12 12 of 16
of 17

can be dissimilar with the target system. However, this result provides meaningful information to
criticality analysis
guess guide
the actual a designer
availability in or
theaearly
decision maker
design stage.to select additional components to installed to
increase availability.

Figure 13. Availability depending on the stages.

4.2. Component Criticality

Figure 14 shows the component criticality depending on the design stages. The component
criticality shows the important component of the availability, and it is the ratio of the component’s
failure time to the system failure time. The most crucial component at Stage I was the heater, which
accounted for 90.3% criticality. The heater and ball valve (condensate) were critical in Stage II and
Stage III. The heater and ball valve (condensate) had 50.7% and 20.0% criticality at the design Stage
III, respectively. The preventive maintenance occupied about 10% on the criticality at Stage III. The
most critical component was the heater regardless of the design stages. The availability of the system
can be significantly increased as installing redundant heaters. The results of the component criticality
analysis guide a designer or a decision maker to select additional components to installed to increase
availability. Figure 13. Availability depending on the stages.
Figure 13. Availability depending on the stages.

4.2. Component Criticality

Figure 14 shows the component criticality depending on the design stages. The component
criticality shows the important component of the availability, and it is the ratio of the component’s
failure time to the system failure time. The most crucial component at Stage I was the heater, which
accounted for 90.3% criticality. The heater and ball valve (condensate) were critical in Stage II and
Stage III. The heater and ball valve (condensate) had 50.7% and 20.0% criticality at the design Stage
III, respectively. The preventive maintenance occupied about 10% on the criticality at Stage III. The
most critical component was the heater regardless of the design stages. The availability of the system
can be significantly increased as installing redundant heaters. The results of the component criticality
analysis guide a designer or a decision maker to select additional components to installed to increase
Figure 14. Component criticality depending on the stages.
availability. Figure 14. Component criticality depending on the stages.
4.3. Sensitivity Analysis
4.3. Sensitivity Analysis
This study performed the sensitivity analysis to investigate the factors affecting the results. It is
important This study performed
to analyze the sensitivity
the correlation betweenanalysis
the factorsto investigate
and the resultsthe factors affecting
because the results.
the results can be It is
important to analyze the correlation between the factors and the results
changed depending on the variation of the factors. In this study, four factors are investigated for because the results can be
changed depending
the sensitivity analysis: on the variation
failure of the
rate, repair factors.
time, In this study,
redundant equipment, four factors are investigated
and modified preventive for the
sensitivity analysis: failure rate, repair time, redundant equipment,
maintenance schedule. The reliability data used in this study are mainly from OREDA, and its mean and modified preventive
valuemaintenance schedule.
is utilized. The valuesThecan reliability
be differentdata used in this
depending study
on the areconditions.
target mainly from OREDA,
OREDA and itsthe
predicts mean
value
failure rateiswith
utilized. The valuesinterval.
90% confidence can be different depending
The confidence on describes
interval the targetthe conditions.
amount of OREDA predicts
uncertainty
Figure 14. Component criticality depending on the stages.
associated with a sample of a population. The sensitivity analysis was performed for the lower and
upper limits of the failure
4.3. Sensitivity rates. The repair times utilized in this study were also mostly from OREDA.
Analysis
The employed active repair time considers only the time when actual repair work is being done. It does
This study performed the sensitivity analysis to investigate the factors affecting the results. It is
not contain time to shut down the unit, issue the work order, wait for spare parts, start-up after repair.
important to analyze the correlation between the factors and the results because the results can be
Some variation exists between
changed depending on the the active
variation of repair timeInand
the factors. this the actual
study, downtime.
four factors (The reason
are investigated why
for the
OREDAsensitivity
only considers
analysis: failure rate, repair time, redundant equipment, and modified preventive to
the active repair time is that the required time for the preparation and return
the normal operation are different
maintenance schedule. depending
The reliability dataon theinlocation
used this studyof the installation.)
are mainly The additional
from OREDA, repair
and its mean
time is taken
value isinto account.
utilized. TheThe availabilities
values with depending
can be different and without on redundant equipment
the target conditions. are estimated
OREDA predicts to
examinethe itsfailure ratethe
effect on with 90% confidence
availability. interval.
Finally, The confidence
the availability interval with
is calculated describes the amount
different of
preventive
maintenance schedules.
 from OREDA. The employed active repair time considers only the time when actual repair work is
being done. It does not contain time to shut down the unit, issue the work order, wait for spare parts,
start-up after repair. Some variation exists between the active repair time and the actual downtime.
(The reason why OREDA only considers the active repair time is that the required time for the
preparation and return to the normal operation are different depending on the location of the
installation.)
Appl. Sci. 2020, 10, 8657 The additional repair time is taken into account. The availabilities with and without13 of 16
redundant equipment are estimated to examine its effect on the availability. Finally, the availability
is calculated with different preventive maintenance schedules.
4.3.1. Lower amd Upper Failure Rates
4.3.1. Lower amd Upper Failure Rates
Figure 15 indicates the availability depending on the design states with different failure rates: lower,
Figure 15 indicates the availability depending on the design states with different failure rates:
mean, and upper
lower, failure
mean, and rates.
upper As therates.
failure failure
As rate was increased
the failure from lower
rate was increased fromtolower
upper, the availability
to upper, the
was decreased. In the
availability wascase of lower
decreased. In and mean
the case failure
of lower rates,
and meanthe availability
failure rates, thewas slightlywas
availability decreased
slightly with
the design stages. with
decreased In contrast,
the designthe availability
stages. In contrast,was significantly
the availability wasreduced in the
significantly caseinofthe
reduced upper
case offailure
rate. When upper
thefailure
design rate. When
stage wasthechanged
design stage
from was changed
Stage fromtoStage
I (PFD) Stage I (PFD) to Stage
II (P&ID), II availability
the (P&ID), the was
availability was dramatically decreased in the case of upper failure rate. This indicated that the
dramatically decreased in the case of upper failure rate. This indicated that the instrument devices
instrument devices gave a critical impact on the availability. The availabilities are 99.506% (lower)
gave a critical impact on the availability. The availabilities are 99.506% (lower) and 97.819% (upper) at
and 97.819% (upper) at Stage III. The upper means that the result is derived using upper failure rate
Stage III. in
The upper
Table means
2, and that isthe
the lower theresult is derived
reverse. This meant using
that upper
the mostfailure rateavailability
optimistic in Table 2,isand the lower
99.506%
is the reverse.
and theThis
mostmeant thatavailability
pessimistic the most isoptimistic
97.819%. availability is 99.506% and the most pessimistic
availability is 97.819%.

Figure 15. Availability

Figure with
15. Availability withdifferent failurerate.
different failure rate.

4.3.2. Additional RepairRepair

4.3.2. Additional Time Time
Figure 16Figure
shows the availability
16 shows depending
the availability depending ononthe
thedesign stageswith
design stages withthethe additional
additional repairrepair
time. time.
Three additional
Three additional repair repair
times times assumed
assumed in this
in this studyare
study are1,
1, 3,
3, and
and55hhtotoinvestigate the impact
investigate of the of the
the impact
delayed
delayed repair repair
time. Thetime. The availability
availability decreased decreased
with thewith the increment
increment of thetime.
of the repair repairWhen
time. additional
When 1,
additional 1, 3, and 5 h were considered at Stage III, the availabilities were 98.969%, 98.823%, and
3, and 5 h Appl.
wereSci.considered
2020, 10, x FORatPEER
Stage III, the availabilities were 98.969%, 98.823%, and 98.701%, respectively.
REVIEW 14 of 17
98.701%, respectively. This result presented that one additional hour in the repair time decreased the
This result presented that
availability by 0.065%.
one additional hour in the repair time decreased the availability by 0.065%.

16. Availability
FigureFigure with additional repair time.
16. Availability with additional repair time.

4.3.3. Installation of Redundant Heater

Figure 17 presents the availability depending on the design stages with the installation of the
redundant heater. As mentioned in Section 4.1, the most critical component in the availability was
the heater regardless of the design stages. The availability was estimated depending on the
Appl. Sci. 2020, 10, 8657 14 of 16
Figure 16. Availability with additional repair time.

4.3.3. Installation
4.3.3. Installation of Redundantof Redundant
HeaterHeater
Figure 17 presents the availability depending on the design stages with the installation of the
Figure 17 presents the availability depending on the design stages with the installation of the
redundant heater. As mentioned in Section 4.1, the most critical component in the availability was
redundant heater. As mentioned
the heater regardless ofinthe Section
design 4.1, the The
stages. most critical component
availability in depending
was estimated the availability
on thewas the
heater regardless of the design stages. The availability was estimated depending on the installation
installation of the redundant heater or not. The availability was considerably increased when the
redundant heater is installed. The availability is 99.028% without the redundant
of the redundant heater or not. The availability was considerably increased when the redundant heater at Stage III,
whereas it is 99.514% with the redundant heater. That is, the redundant heater increased the
heater is installed. The availability is 99.028% without the redundant heater at Stage III, whereas it is
availability by 0.486%. Although 0.486% availability seems to be low, it is not a negligible value in
99.514% withthe the redundant
system heater. That is, the redundant heater increased the availability by 0.486%.
(LNG-FPSO).
Although 0.486% availability seems to be low, it is not a negligible value in the system (LNG-FPSO).

Figure 17. Availability with installation of redundant heater.

Figure
Appl. Sci. 2020, 10, x FOR PEER 17. Availability with installation of redundant heater.
REVIEW 15 of 17

4.3.4. Modified Preventive Maintenance Schedule

4.3.4. Modified Preventive Maintenance Schedule
Figure 18 shows
Figure 18the availability
shows depending
the availability depending ononthe
thedesign stages
design stages withwith the modified
the modified preventive
preventive
maintenance. maintenance. As mentioned
As mentioned in Section
in Section 3, the
3, the preventive maintenance
preventive maintenance is conducted to prevent
is conducted the
to prevent the
critical failures. There are various activities for the preventive maintenance as indicated in Table 3.
critical failures. There are various activities for the preventive maintenance as indicated in Table 3.
These activities are individually conducted depending on their inherent periodic. When the activities
These activities
haveare individually
different schedule,conducted depending
some activities on their
can be merged to inherent
increase the periodic. When
availability. the activities
Although
have different schedule, preventive
simultaneous some activities can beincreases
maintenance mergedthetoavailability,
increase the availability.
it requires Although
new engineers simultaneous
to conduct
the activities atincreases
preventive maintenance the same time.
the Since all components
availability, have the
it requires newsame preventive
engineers tomaintenance
conduct theschedule,
activities at the
different schedules were assumed in the modified schedule. The result showed that the availability
same time. Since all components have the same preventive maintenance schedule, different schedules
was decreased by 0.076% through the modified preventive maintenance schedule. Since the
were assumed in the modified
preventive schedule.
maintenance was not The result showed
considered at Stages that
I andthe
II, availability
the values atwasthosedecreased
stages were by 0.076%
through theunchanged.
modified preventive maintenance schedule. Since the preventive maintenance was not
considered at Stages I and II, the values at those stages were unchanged.

Availability
Figure 18.Figure withwith
18. Availability modified preventive
modified maintenance
preventive maintenance schedule.
schedule.

5. Conclusions
This study estimated the availability of air and nitrogen systems depending on the design stages
to analyze the gap between early and late design stages. Three design stages were considered: Stages
I–III. Stage I was the process flow diagram (PFD) stage and Stage II was the piping and instrument
Appl. Sci. 2020, 10, 8657 15 of 16

5. Conclusions
This study estimated the availability of air and nitrogen systems depending on the design stages to
analyze the gap between early and late design stages. Three design stages were considered: Stages I–III.
Stage I was the process flow diagram (PFD) stage and Stage II was the piping and instrument diagram
(P&ID) stage. In Stage III, the preventive maintenance was additionally considered comparing to Stage
II. The Monte Carlo simulation approach was employed for the availability estimation. The results
presented that the availabilities were decreased with the design progress. It is obvious because the
system was more complex with the design development. The availability difference between Stage
I and Stage II was 0.331%, and that was 0.103% between Stage II and Stage III. These indicated that
the instrument system and the preventive maintenance occupied 0.331% and 0.103%, respectively.
This result also presented that the availability in the early design stage (Stage I) was underestimated
compared to the late design stage (Stage III). The unavailability at the late design stage was 1.8 times
higher than the early design stage. We could guess the availability at the late design stage using the
result at the initial design stage. The most critical component in the air and nitrogen systems was the
heater regardless of design stages. The sensitivity analysis was conducted to analyze the key factors on
the results. The most crucial factor was the redundant equipment. When the redundant heater was
installed, the availability was increased by 0.486% at Stage III. The factors for the modified maintenance
schedule and additional repair time (1 h) were not significant in the system compared to other factors.
Since this study investigated only two systems (air and nitrogen systems) among lots of systems in
LNG-FPSO, future studies are required for the whole system (LNG-FPSO). Although this study did
not consider the whole system (LNG-FPSO), this gives the important guide to progress the next step
for the accurate availability estimation in the early design stage.

Author Contributions: Conceptualization, J.-Y.J.; methodology, Y.S.; formal analysis, S.H.; validation, K.K.;
writing—original draft preparation, Y.S.; writing—review and editing, J.-Y.J. All authors have read and agreed to
the published version of the manuscript.
Funding: This research was supported by a grant from Endowment Project of “Technology development of
material handling and risk management for operation and maintenance service of offshore plant (PES3470)” and
“Development of Evaluation Model for Hydrogen Offshore Supply Chain and Test Technologies for Hydrogen
Equipment (PES3510)” funded by Korea Research Institute of Ships and Ocean Engineering.
Conflicts of Interest: The authors declare no conflict of interest.

References
1. British Standards Institution (BSI). BS 4778-3.1:1991 Quality Vocabulary: Part 3: Availability, Reliability and
Maintainability Terms, Section 3.1 Guide to Concepts and Related Definitions; BSI: London, UK, 1991.
2. Basker, B.; Martin, P. Availability prediction by using a method of simulation. Microelectron. Reliab. 1977,
16, 135–141. [CrossRef]
3. Keller, A.; Stipho, N. Availability studies of two similar chlorine production plant. Reliab. Eng. 1981,
2, 271–281. [CrossRef]
4. Bosman, M. Availability analysis of a natural gas compressor plant. Reliab. Eng. 1985, 11, 13–26. [CrossRef]
5. Aven, T. Availability evaluation of oil/gas production and transportation systems. Reliab. Eng. 1987,
18, 35–44. [CrossRef]
6. Khan, M.R.; Kabir, A.Z. Availability simulation of an ammonia plant. Reliab. Eng. Syst. Saf. 1995,
48, 217–227. [CrossRef]
7. Hajeeh, M.; Chaudhuri, D. Reliability and availability assessment of reverse osmosis. Desalination 2000,
130, 185–192. [CrossRef]
8. Zio, E.; Baraldi, P.; Patelli, E. Assessment of the availability of an o shore installation by Monte Carlo
simulation. Int. J. Press. Vessels Pip. 2006, 83, 312–320. [CrossRef]
9. Crespo, A.; Heguedas, A.S.; Iung, B. Monte Carlo-based assessment of system availability. A case study for
cogeneration plants. Reliab. Eng. Syst. Saf. 2005, 88, 273–289. [CrossRef]
Appl. Sci. 2020, 10, 8657 16 of 16

10. Michelassi, C.; Monaci, G. Availability assessment of a raw gas re-injection plant for the production
of oil and gas. In Proceedings of the 16th IMEKO TC4 International Symposium Exploring New
Frontiers of Instrumentation and Methods for Electrical and Electronic Measurements, Florence, Italy,
22–24 September 2008; pp. 777–783.
11. Chang, D.; Rhee, T.; Nam, K.; Chang, K.; Lee, D.; Jeong, S. A study on availability and safety of new
propulsion systems for LNG carriers. Reliab. Eng. Syst. Saf. 2008, 93, 1877–1885. [CrossRef]
12. Görkemli, L.; Ulusoy, S.K. Fuzzy Bayesian reliability and availability analysis of production systems.
Comput. Ind. Eng. 2010, 59, 690–696. [CrossRef]
13. Seo, Y.; You, H.; Lee, S.; Huh, C.; Chang, D. Evaluation of CO2 liquefaction processes for ship-based carbon
capture and storage (CCS) in terms of life cycle cost (LCC) considering availability. Int. J. Greenh. Gas Control.
2015, 35, 1–12. [CrossRef]
14. Seo, S.; Chu, B.; Noh, Y.; Jang, W.; Lee, S.; Seo, Y.; Chang, D. An economic evaluation of operating expenditures
for LNG fuel gas supply systems onboard ocean-going ships considering availability. Ships Offshore Struct.
2014, 11, 213–223. [CrossRef]
15. Gowid, S.; Dixon, R.; Ghani, S. Profitability, reliability and condition based monitoring of LNG floating
platforms: A review. J. Nat. Gas Sci. Eng. 2015, 27, 1495–1511. [CrossRef]
16. Hwang, H.; Lee, J.-H.; Hwang, J.; Jun, H.-B. A study of the development of a condition-based maintenance
system for an LNG FPSO. Ocean Eng. 2018, 164, 604–615. [CrossRef]
17. Verma, A.K.; Srividya, A.; Karanki, D.R. Reliability and Safety Engineering; Springer: London, UK, 2010;
ISBN 978-1-84996-231-5.
18. Rausand, M.; Høyland, A. System Reliability Theory: Models, Statistical Methods, and Applications, 2nd ed.;
John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2004; ISBN 0-7803-3274-1.
19. Oreda Project. OREDA: Participants Offshore Reliability Data Hand-Book, 5th ed.; DNV: Oslo, Norway, 2009;
ISBN 8214027055.
20. Oreda Project. OREDA: Participants Offshore and Onshore Reliability Data Handbook, 6th ed.; DNV GL: Oslo,
Norway, 2015.
21. Seo, Y.; Han, S.; Kang, K.; Noh, H.-J.; Park, S.; Jung, J.-Y.; Chang, D.; Čepin, M.; Briš, R. Availability estimation
of utility module in offshore plant depending on system configuration. In Advances and Trends in Engineering
Sciences and Technologies II; Al Ali, M., Platko, P., Eds.; CRC Press: London, UK, 2017; pp. 2195–2202.

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