ISSN 2086-5953
2.2 Application of RGEC model
Case study has been done from TTF data until 9000 hours operation of ship oil system. Using
of the reliability growth equation based on failure types choices. Using ‗
and ‘ parameters follow the four methods, they are Weibull [9], Crow [2],
Crow [3], and RGEC. Test problem :
1. TTF Application of the MDM
The MDM emerged from TTF. If the comparation
between the
differentiation of
maintenance with the maintenance duration is a bit, hazard
will be happenend. Hazard was happenend on the Purifier of the 1230 operating hours after the
10 hours purifier replacement. It was simplified by 123010, and the 3612 operating hours after 2-
hours replacement, simplified 3612 2, Discharge Filter 1150 8, Supply Pump 6625 25, 3313 13,
and main engine 1319 19.
If there is the same maintenance schedule for different component or
subsystem, so maintenance combining to reduce cost or
harmonisation can be emerged. Maintenance
combining to reduce cost has occured to the Purifier-Filter-Main Engine at 70 operating hours,
Discharge Filter and Supply Pump of the 1142 hours, Transfer Pump and Discharge Filter of the
780 hours, and Filter with main engine at the 1000 hours.
In the certain maintenance duration block, the differentiation between maintenance is almost the
same value, so the using of reliability growth equation was differed by value of
and in the block. The results show that the maintenance
duration between blocks not have the same value and the duration tends to become longer, and
emerged procedure and technology innovation. For example main engine at the operation of 138 hours,
1000 hours, and 2656 hours. The three condition show innovation process. These matters were
shown from the next maintenance duration become longer than before, the average before is 34.5 hours
until 138 hours operational become 95.8 hours in average until 1000 hours, 327.4 hours in average
for the 1000 hours duration until 2656 hours, and 898.4 hours in average with 2656 hours until 9000
hours.
Transfer Pump and Filter are never in a hazard condition. These are becaused that hazard of
the Transfer Pump or Filter of oil system not caused the system stops in operation. Combining
maintenance scheduling of some component and subsystem or harmonisation could be happened at
the 1142 hours operation running of Supply Pump with the Discharge Filter, and 1000 hours operation
for Filter with main engine. Preliminary study is only received from TTF data and it still cannot
shown
component effectivity
influenced or
subsystem at the system reliability.
Table 1. TTF data of ship oil System
Component N
Time to Repair operation hours
Supply Pump 28
100, 205, 315, 435, 540, 830, 1142, 1460, 1750, 2050, 2400, 2660, 3000,
3013
, 3300, 3640, 3970, 4475, 4950, 5520,
6080, 6600,
6625
, 7000, 7580, 8100, 8575, 9000
Purifier 46
30, 70, 120, 175, 225, 274, 330, 400, 476, 530, 615, 695, 760, 830, 905, 976, 1050,
1135, 1220,
1230
, 1459, 1575, 1700, 1835, 1975, 2100, 2230, 2550, 2900,
3240, 3610,
3612
, 4105, 4623, 5000, 5353, 5670, 6000, 6340, 6675, 6900,
7200, 7459, 8019, 8567, 9000. Transfer
Pump 15
350, 780, 1142, 1560, 2000, 2550, 3000, 3550, 4010, 4600, 5200, 5710, 6180,
7100, 8100 9000 Disc. Filter
12
350, 780, 1142,
1150
, 2000, 3000, 4010, 5200, 6180, 7000, 8012, 9000
Filter 45
30, 70, 115, 165, 217, 176, 345, 412, 465, 514, 573, 632, 695, 755, 820, 890, 943,
1000, 1105, 1214, 1315, 1410, 1504, 1630, 1746, 1875, 2100, 2357, 2602,
2848, 3105, 3326, 3540, 3770, 4340, 4800, 5200, 5710, 6180, 6580, 7000,
7450, 8010, 8550, 9000 Main Engine
27
30, 70, 100, 138, 225, 330, 432, 530, 600, 700, 806, 897, 1000, 1300,
1319
, 1634, 2010, 2325, 2656, 3345, 4074, 4895,
5750,
5805
, 6983, 8050, 9000
2. The advantages in using RGEC
Reliabitity growth of ship oil system is applied follows the configuration of Figure 2. The
example of reliability growth curve for Supply pump and Transfer Pump with the four alternatives
calculation was configured at the figure 3 and 4. Both figures inform that the reliability of EGEC is
the realistic one.
Oil system has two cycles, they are oil filled cycle into Service tank, and oil used cycle at the
main engine. Oil in the Stora ge Oil is pumped out by Transfer Pump into the Service Tank. In the
certain time oil volume in the Service Tank decreases and the Transfer Pump re-pump oil out of
the Storage Oil fill into the Service Tank. This pumping system just only used one Transfer Pump,
besides paralel connection Transfer Pump to guarante pumping operation if one of the pumps
failured. In the second cycle, the filter distills oil for Main Engine. Used oil of Main Engine was
received by three Discharge Filter to be filtered and re-circulated.
ISSN 2086-5953
Table 2 Failure Definition and Component or Subsystem Number
Sub system or component
Failure type
Sum
A D
Storage Oil SS Service Tank SS
=1 =1
1 1
E G
Supply Pump SS Motor Induk SS
Bc BC
2 1
B H
Transfer Pump SS Discharge Filter K
Bd Bd
2 3
C F
Purifier K Filter K
A A
2 1
Figure 2. Configuration of ship oil system
3 RESULT AND DISCUSSION
The reliability of each component and subsystem consist of : R
SP
= Pump Supply Reliability
, R
P
= Purifier Reliability for 1 and 2, R
TP
= Pump Transfer Reliability for 1 and 2, R
DF
= Discharge Reliability
for 1, 2, and 3, R
F
= Filter Reliability
, and R
MI
= Main Engine Reliability. R
MI
= Main Engine Reliability. This notation is used to declare system reliability equation based on the
equation 20. The reliability of Storage Oil and Service Tank
were assumpted e equal to one. Ship Oil system configuration equation
becomes,
x t
R t
R t
R t
R x
t R
t R
t R
x t
R t
R t
R
TP TP
TP TP
P p
P MI
SP
2 1
2 1
2 2
1 1
.
3
. 2
. 1
3 .
2 3
. 1
2 1
2 1
t R
t R
t R
t R
t R
t R
t R
t R
t R
t R
t R
DF DF
DF DF
DF DF
DF DF
DF DF
DF
2 1
2 1
t R
t R
t R
t R
F F
F F
Reliability growth prediction of ship oil system can be resulted from accumulative of
reliability from impact calculation: MDM and reliabilitly
of non
technical determination
impulsive system and the pure caused of routine maintenance. Reliability of ship oil system shows
that the EGRC equation gives the biggest reliability with reliability level 0.15 for 25 years operation at
the end of life time system. This value is under reliability level of Indonesia, 66.67, and the mean
value requirement is 80. The mean value of its reliability is 64.3.
Figure 3. Reliability Growth of Supply Pump up to 9000 hours operatio time
Figure 4. Reliability Growth of Transfer Pump
4 CONCLUSION
The RGEC model using MDM have been established in this study. Model and numerical
result on reliability growth prediction of ship oil system can inspire the other system model. The
interpretation of
reliability becomes
communication among of the experties even among states. We need the same perception in the method
and procedure to ensure satisfaction.
REFERENCES
[1] Bieman J M, Malaiya Y K 2002 Software Reliability Growth With Test Coverage.
A B
B C
C D
E E
F G
H H
H
20
ISSN 2086-5953 Proceeding of Transaction on Reliability,
IEEE 51: 420-426. [2] Crow L H 2004 An Extended Reliability
Growth Model for Managing and Assessing Corrective Actions. Proceeding of Annual
Reliability and Maintanability Symposium 4: 1-8.
[3] Crow L H 2008, Practical Methods for Analyzing the Reliability of Repairable
Systems, Reliability edge Hc – Reliasoft.doc.,
5: 124-132. [4] David D D, and Mary G P 2009 A new
Reliability Assessment Technique for Aging Electronic Systems. Paper from Reliability
Analysis Center RAC – Illinois Institute of
Technology, p.18. [5] Kibrio, S A M S 1989 An Overview of The
Framework for
Technology-Based Development,
Economic and
Sosial Commission for Asia and The Fasific-United
Nation, Bangalore, India. [6] Nelson W 1982 Applied Life Data Analysis,
John Wiley and Sons, Inc.-New York. [7] Pertamina
Shipping 2007
Planned Maintenance System Manual. Direktorat
Pemasaran dan Niaga Perkapalan, PT. Pertamina Persero, Jakarta.
[8] Rasmussen, M and H Moen 1996 The Role of Information Technology for Reduction of
Maintenance Cost. Proceeding of The Institute of Marine Engineering Norwegian University
of Science and Technology, Trondheimm 6: 85-94.
[9] Weibull W 1970 A Statistical Distribution Function of Wide Applicability. Journal of
Applied Mechanics 18: 293-297.
83 ISSN 2086-5953
REDUCTION AND REFORMATION OF
POLYBROMINATED DIPHENYL ETHERS
PBDEs DURING THE HEATING PROCESS FOR NON-WASHING AND WASHING ASHES
Aullya Ardhini Artha, Chi-Hsuan, Chen, Wen-Jhy Lee
1
, Lin-Chi Wang, Guo-Ping Chang-Chien
2 1
Department of Environmental Engineering, National Cheng Kung University No.1, University Road, Tainan City 701, Taiwan R.O.C.
2
Department of Chemical and Materials Engineering, Cheng Shiu University Kaohsiung 833, Taiwan, Republic of China
Email: wjleemail.ncku.edu.tw
ABSTRACT
Polybrominated diphenyl ethers PBDEs flame retardants are persistent organic pollutants
that have been found globally in the environment. Since they are not chemically incorporated with
polymers, PBDEs are easily exposed to the atmosphere during their production, used, and
disposed. Over the past decade, a mounting body of data has shown that PBDEs are prone to undergo
long-range atmospheric transport to regions where they were never used. Persistent aromatic bromine,
chlorine and mixed chlorine-bromine compounds were being analyzed from fly ashes to explore the
impact of brominated flame retardants BFR on their reduction and reformation. Polybrominated
diphenyl ethers PBDE were the most abundant original BFRs found, that have allowed them to be
used, successfully, as flame retardants in a wide range of materials. Due to its stability, PBDEs have
been found at varying levels in the environment. Products containing PBDEs will sooner or later be
treated by municipal solid waste incinerators MSWIs or metal recycling plants [1]. Many
countries consider waste-to-energy incineration as a mainstream strategy for municipal solid waste
management. Unlike fly ashes, bottom ashes BA are usually considered as non-hazardous materials.
The research analyze about reduction and reformation of PBDEs for non-washing and
washing ashes during heating process. The applying temperature is from 50
o
C to 1450
o
C. PBDEs content from fly ash remain the same from 50
o
C to 1000
o
C, but it change after 1000
o
C. PBDEs content from fly ash, has unstable concentration from 1100
o
C to 1350
o
C and it is caused by de novo synthesis. Highest value occur at temperature 1200
o
C and its about 33137 pgg.
Keywords: Polybrominated diphenyl ethers, fly ash.
1 INTRODUCTION
PBDEs are used only for flame retardant purposes. The rationale for using brominated
compounds as flame retardants is based on the ability of halogen atoms, generated from the
thermal decomposition of the bromoorganic compound, to chemically reduce and retard the
development of fire [1].
The behavior of PBDEs in the process of manufacturing brominated flame retardants has
recently attracted attention and PBDEs during the combustion of products containing brominated
flame retardants in municipal solid waste MSW incinerator also the same. There are many cases of
PBDDFs being detected in flue gas and fly ashes from municipal solid waste incinerators, but fewer
studies of the behavior of PBDDFs from laboratory thermolysis of BFR products have been reported.
Concerns about polybrominated dibenzo-p- dioxins and dibenzofurans PBDDFs have also
increased, because PBDDF formation occurs during either processing PBDE-containing plastics
or when incinerating waste which contains BFRs [4].
Presence of halogens Br, Cl in various products such as electronic devices or plastics are
well known to produce toxicologically due to thermal degradation products under thermal stress
conditions combustion, accidental fires, thermal recycling processes. Furthermore, considering
product safety and environmental properties of products, manufactures and distributors of flame
retarded products are focusing on replacement of halogenated flame retardants by halogen-free
materials. Persistent aromatic bromine, chlorine and mixed chlorine-bromine compounds were analyzed
from fly ashes to explore the impact of brominated flame retardants BFR on their reduction and
reformation.
ISSN 2086-5953
2 MODEL, ANALYSIS, DESIGN,
AND IMPLEMENTATION
2.1 Laboratory Melting System
Kategori