A strategy for the risk-based inspection of pressure safety valves (2009).pdf

ARTIC AR TICLE LE IN PR PRESS ESS

Reliability Engineering and System Safety 94 (2009) 810–818

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Reliability Engineering and System Safety journal homepage: www.elsevier.com/locate/ress

A strategy for the risk-based inspection of pressure safety valves Chi-Hui Chien a, , Chun-Hung Chen a,b, Yuh J. Chao c Ã

a

Department of Mechanical and Electro-Mechanical Engineering. National Sun Yat-Sen University. No. 70, Lien-Hai Road, Kaohsiung 80424, Taiwan Air Products San Fu Co., Ltd., Taipei, Taiwan c Department of Mechanical Engineering, University of South Carolina, 300 Main Street, Columbia, SC 29208, USA

b

a r t i c l e

i n f o

a b s t r a c t

Article history: Received 7 March 2008 Received in revised form 15 August 2008 Accepted 1 September 2008 Available online 17 September 2008 Keywords: Pressure safety valve Aging Aging trend Analysis of variance Semi-quantitative Semi-quantitative risk-based risk-based inspection

The purpose of a pressure safety valve (PSV) is to protect the life and safety of pressure vessels in a pressurized system. If a weakened PSV fails to function properly, a catastrophic event might occur if no other protective means are provided. By utilizing the as-received test data and statistical analysis of the aging conditions of PSVs in lubricant process units, a risk-based inspection (RBI) system was developed in this study. First of all, the characteristics of PSV were discussed from the practical viewpoint of engineering inspection and maintenance. The as-received test data, which shows obvious PSV damage, will be separated from the data used in the following statistical analysis. Then, the relationship between the aging conditions conditions and the correspondin corresponding g PSV parameters parameters was analyzed analyzed by using using the statistical statistical technique— technique—analysis of variance (ANOVA). Finally, a strategy for semi-quantitative RBI is proposed. Also, a definitive estimated inspection interval for every PSV is suggested. The outcome indicated most of the risks result from a few PSVs, for which the corresponding inspection intervals will be shorter than the 2 years in accordance with relative standards and local government regulations. & 2008 Elsevier Ltd. All rights reserved.

1. Introduct Introduction ion

For the past decade, according to the government regulations of the Council of Labor Affairs (CLA) in Taiwan, the jurisdiction inspec inspectio tion n (inspe (inspecti ction on perfor performed med by inspec inspectio tion n agenci agencies es or design designate ated d inspec inspectio tion n agenci agencies es of CLA) CLA) interv interval al for press pressure ure vessels is 2 years except for gas service (3 years) in a pressurized system. system. During During the jurisdicti jurisdiction on inspection inspection period, period, the pressure pressure safety valves (PSVs) in the pressurized system must be tested to ensure these PSVs can perform normally subject to the required criteria criteria based on relative relative code and jurisdicti jurisdiction on requirem requirements ents.. From the safety concerns of the pressurized systems, such a short time-based inspection strategy may be a conservative consideration for all pressure vessels and PSVs. But, from the viewpoint of the risk-based consideration [1–3] [1–3],, inspection intervals for some high-risk PSVs, especially near its repair or retirement stages, may need to be less than 2 years, while the others may warrant longer inspection intervals. In recent years, the CLA in Taiwan has allowed plant operators to extend the jurisdiction jurisdiction inspection interval of pressure vessels vessels to more mor e than than 2 years years (the (the cur curren rentt sho short rtest est inspe inspecti ction on interv interval al allowed allowed under Taiwan Taiwan regulatio regulations), ns), provide provided d there are some systemati systematicc evaluatio evaluations, ns, including including damage damage mechanism mechanism assess assess--

ments [4] [4],, life predictions predictions [5] [5],, and risk-bas risk-based ed inspection inspection (RBI) [6,7] of each pressure vessel in the pressurized system according to the relative API code [8] [8].. One can simply predict the life of pressure vessels using the fitness fitness for service technique in practical engi engine neer erin ing g prac practi tice ce.. Howe Howeve verr, one one cann cannot ot take take the the same same measures when dealing with the PSVs because of the uncertainty of process discharge and condition monitoring. Meanwhile, there is no powerful jurisdiction or code criterion that can be applied system systemati atical cally ly in evalua evaluatin ting g the PSVs PSVs perfor performan mance ce and the inspection inspection intervals. intervals. Therefor Therefore, e, a specific specific inspection inspection and maintenance strategy should be focused on the aging trend of the PSVs during extended periods to support the safety of the pressurized system.

2. Rese Research arch struct structure ure

The research structure of this study can be divided into three sections— sections—characteristic of PSV and data assessment, analysis of varian variance ce (ANO (ANOVA) VA),, and the RBI assess assessme ment. nt. Detail Detailss will will be described in the sections as follows.

2.1. 2.1. Characteristic of PSV and data assessment Ã

Corresponding Corresponding author. author. Tel.: +8867 5254223; fax: +886 +886 7 5254299. E-mail address: chchien@f [email protected] aculty.nsysu.edu.tw su.edu.tw (C.-H. Chien).

0951-8320/$0951-8320/$- see front matter matter doi:10.1016/j.ress.2008.09.002 doi:10.1016/j.ress.2008.09.002

&

2008 Elsevier Ltd. All rights reserved.

From From the the prac practi tica call view viewpo poin intt of engi engine neer erin ing, g, the the agin aging g condition of a PSV can be expressed as a function of specific PSV

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C.-H. Chien et al. / Reliability Engineering and System Safety 94 (2009) 810–818

parameters, which can be written as follows:



TP=SP ¼ f Fluids; Timeservice ;

OP ; Tempop ; Sizeinlet ; Process unit SP



(1) where Fluids is of the category of the internal fluid which can open the PSV disk during process abnormal condition, Timeservice is the time interval of the in-service period, and OP is the operating pressure under normal condition. The value of the inlet static pressure at which there is a measurable lift of the disk or at which discharge of the fluid becomes continuous (as determined by seeing, feeling, or hearing) is SP and it is the inlet gauge pressure at which the pressure relief valve is set to open under service conditions. In this paper, this value is set equal to the cold differential test pressure, or test pressure (TP) in the workshop. Tempop is the operating temperature under normal condition. Sizeinlet the nominal pipe size (NPS) of the PSV at the inlet connection, unless otherwise designated, Process unit is the different service locations of PSVs in this study. Normally, performing the PSV as-received tests in the workshop is the easiest way to evaluate the aging condition of PSV. That is, an increase in value of the ratio of test pressure to set pressure (TP/SP) is a good health indicator of the PSV. In the engineering practices of inspection and maintenance, the PSVs were usually removed from the static equipment and performed the as-received tests before starting the disassembly works of PSVs. Under normal circumferences, without considering the service type and fluid category in each PSV, the test pressure of each PSV should not be greater than 150% or less than 90% of the set pressure under normal operating conditions. Following the inspection and maintenance procedures described in API 576 recommendation practice [9], a thorough damage examination should be performed to each damaged PSV if the required performance does not meet the requirements of jurisdiction or process requests. Meanwhile, the root cause damage should be recorded in a root cause analysis report. In this study, if the asreceived test data, i.e., value of TP/SP, are greater than 1.8 or less than 0.7, then it is assumed that the corresponding PSV is clearly damaged, and the data will be deleted from the data set used in the following statistic analysis. The remaining data set will comply with the purpose of obtaining the aging trend of PSVs and identifying the high-risk population of PSVs. 2.2. Analysis of variance (ANOVA)

The ANOVA method is the easiest way to measure performance between the parameters and the as-received test data of the PSVs. Typically, before performing the ANOVA, the range of each PSV parameter should be divided into several groups according to the Sturges formula [10,11], in order to gain as much information as possible about the nature of aging conditions of the PSVs. After data grouping, ANOVA will be performed between the parameter and the as-received test data of the PSV by applying Microsoft EXCEL data analysis function. Usually, the obtained ANOVA results will show two important values, F - values and the critical value of F -value, F crit. If the obtained ANOVA result shows the F -value of a PSV parameter is below its critical value F crit, then this PSV parameter has less significant influence on aging conditions of the PSVs. If, on the contrary, the showed F - value is greater than the F crit, then significant influence will exist between the as-received test data and the PSV parameter. Therefore, for the purpose of PSV management, the plant operator can identify these parameters, which have significant influence on aging conditions of the PSVs after the ANOVA is executed. After the ANOVA is executed, the

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parameters that significantly influence the aging conditions of the PSVs will be used as the input data for the RBI assessments. 2.3. RBI assessments

RBI uses risk to assess the results of inspection, testing, and monitoring of a PSV. Typically, risk is defined as the product of the likelihood of a failure and its consequence. So, based on this risk definition, the risk value of each PSV in the RBI assessments can be calculated by the following equation: Risk ¼ Likelihood of failure ðLOFÞ Â Consequence of failure ðCOFÞ (2) Recognition of both LOF and COF is essential for a complete RBI assessment program. 2.3.1. Likelihood of failure (LOF) Evidently, there are some parameters influencing the LOF apart from the PSV parameters, which are set according to the required discharge capacity. For instance, the process operating conditions, especially the frequency of process upset, also influence the health condition of PSV. Therefore, based on the PSV parameters and the process operating conditions, the practical assessment of LOF to each PSV should be divided into two separate assessment groups, the likelihood factor ( f likelihood) and generic failure condition factor ( f generic failure condition). Each assessment group is then divided into several items as the sub-likelihood factor Ai (i.e., PSV parameters such as fluid category, service duration, etc.) and the sub-generic failure condition factor Bi (i.e., actual operating conditions such as lifting light or lifting heavy, etc.), as shown in Fig. 1. Meanwhile, because each PSV in the pressurized system is in different services, different corresponding weighting factors for Ai and Bi should be chosen and were defined as W Ai and W Bi , respectively. After the as-received test results were collected and the weighting factors were well defined, the derived f likelihood and f generic failure condition to each PSV can be summarized as follows: f likelihood ¼ SW Ai Ai

(3)

f generic failure condition ¼ SW Bi Bi

(4)

Then the LOF value of each PSV can be calculated as the product of f likelihood and f generic failure condition as follows: LOF ¼ ð f likelihood Þ Â ð f generic failure condition Þ

(5)

2.3.2. Consequence of failure (COF) COF requires huge and complex data such as the number of people injured, costs of adjustments and repairs with the system downtime. A more rigorous analysis would therefore consider the likely and historical demand rate on each PSV. Also, the assessment of COF for PSVs usually requires specific engineering input such as the original design basis, the likely extent of overpressure in case of failure on demand, the flammability and toxicity of the process stream, records of management of change, etc. It may be necessary to call on extra specialist knowledge to support the condition of the pressure vessel being protected and the likely effects of overpressure when assessing the COF. Based on the continuum characteristic of RBI assessments and simplifying the COF analysis of PSVs, one can apply the RBI COF results of the pressure vessels as the input data of the COF of PSV under the assumption that PSV failure will lead to pressure vessels failure. Therefore, the index of toxicity, health, and environmental hazards of pressure vessels will be applied in the COF evaluation of PSV in this study.

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Fig. 1. Research structure of risk-based PSVs inspection.

Following the index provided by the process specialist, the COF for each PSV can be summarized as follows: COF ¼ SC i ¼ Indextoxic þ Indexhealth þ Indexenvironmental

(6)

where C i is the index of toxic/health/environment provided by the process specialist.

3. A case study of a lubricant process train 3.1. System description and data assessment in this case study

In this case study, in the chosen lubricant plant there are seven process units numbered #1200, #1300, #1400, #1500, #1600, #1800, and #1900. A total of 44 kinds of fluids and 252

spring-loaded PSVs were used. Roughly, 60% of the PSVs were used in liquid service, while about 4% and 36% were used in vapor and two phase (liquid and vapor) services, respectively. The corrosion conditions of the PSVs were not severe because no highly corrosive fluids were used. Following the results of the government service extension audit to the pressurized system, a 6-day PSV inspection and adjusting works were performed on the general service vessels during the second year of the system service duration. After the extension of 1-year service, a detailed inspection and maintenance works were performed on all PSVs in the pressurized system within the 30-day turnaround. Under such partial inspection and adjusting works, different PSV service durations of 1-year operation and 3-year operation were obtained. The average tested interval for all PSVs is 1.65 years. The data sets

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Fig. 2. The distributions of the TP/SP ratios of 229 as-received tested results.

corresponding to rust and debris, sticking due to process deposits, and mechanical damages such as spring washers damage to the valve stem, etc. [3] were deleted from the original data sets and the related preventive inspection and maintenance were followed up. The remaining data sets, whose TP/SP values are between 0.7 and 1.8 and will be used in the aging trend analysis, are 229 asreceived tested results as shown in Fig. 2. The flowchart of the assessment of inspection interval of PSVs is shown in Fig. 3. 3.2. Analysis of variation (ANOVA)

To simplify the analysis process, the single-factor ANOVA method is used in this case study under the assumption that no interactions exist between the PSV parameters. Following the analysis flowchart listed in Fig. 3, the single-factor ANOVA was performed between the as-received test data and the PSV parameters. The results of F -value and critical value F crit to each parameter are listed in Table 1. In Table 1, the ANOVA results reveal that there are four PSV parameters—fluid category, service duration, OP/SP, and operating temperature— that show less significant influences on the aging conditions of PSVs. Therefore, with the aid of ANOVA results, one can find that PSV inlet size (represents capacity required in the process train) and different service locations (process units) do have significant influences on the aging conditions of PSVs. Following the flowchart shown in Fig. 3, correlation was performed on the PSV inlet size ( A1) and the different service locations ( A2) to identify the weighting factors, W A1 and W A2 , respectively. The correlated results for the weighting factors are shown in Table 2. 3.3. RBI assessments 3.3.1. Likelihood factor assessment of the LOF It is recalled that an increase in TP/SP may be a good indicator of the health condition of the PSV. So, the bias range of each grouped as-received test data (the maximum TP/SP minus minimum TP/SP in each grouped data) will be the information

relevant to the health condition between each group. The bias range distributions corresponding to the PSV inlet size ( A1) and the different service locations ( A2) are shown in Figs. 4 and 5, respectively. The corresponding linear regression results are also shown in these two figures. Obviously, owing to the lack of sufficient as-received test data provided in this case study, the small R square values of the linear regressions results were expected. However, since the purpose of this study is to establish the scheme of the semi-quantitative RBI system to optimize inspection and maintenance works of PSVs, the effects of small R square values are neglected in this study. By substituting the value of each parameter grouping range into the corresponding linear regression equations shown in Figs. 4 and 5, the values of Ai to each kind of PSV can be obtained and are listed in the second row of Table 3. Then, the score of each sublikelihood factor can be calculated as shown in the last row of Table 3. Thereafter, each PSV can be categorized by using the scores listed in the last row of Table 3 and Eq. (3). For example, suppose there is a PSV with inlet size 200 located in the #1300 process unit, one can obtain the score of A1 as 36.7 ( A1 Â W A1 ) and A2 as 3.0 ( A2 Â W A2 ). As the designated RBI scheme in this case study, the f likelihood of this PSV is equal to 39.7, which is calculated by using Eq. (3).

3.3.2. Generic failure condition factor assessment of the LOF In some situations, the variation degree of lifting light, heavy, or leaking of a PSV can play an important role in the alarm message of the process units outside actual process condition monitoring. Furthermore, in the viewpoint of process specialists, the generic failure conditions of the PSV can accurately reflect the process condition. In this case study, the assessment of generic failure conditions of the PSVs would not apply in the primary RBI assessments because some potential process problems may be hidden in the applied data. However, with the accuracy and precision of the RBI assessments to all PSVs, the generic failure condition factor should be inserted in the RBI reassessment after a thorough discussion and operation treatment of potential process problems.

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Turnaround Start

Remove PSVs from the static equipments

Perform the as-received tests

Record and deletes the data sets corresponding to rust and debris, insect nests, galling parts, sticking due to process deposits, and mechanical damages

Data Grouping Parameters of significant influence

Parameters of nonsignificant influence

ANOVA Grouping and correlation performed between aging data and these parameters

Check high aging trend PSVs data by scatter plot Condition monitoring results and aging model analysis

Verify the service type by checking relative data

Data input (2)

Data input (1)

Likelihood of failure

High-risk population needed to be monitored carefully

Consequence of failure

Risk ranking

Decision of inspection plans and next inspection date

Fig. 3. Flowchart of the assessment of inspection interval of PSVs.

Table 1 ANOVA results of the PSV parameters

PSV parameters

F -value

F crit

Fluid category Service duration OP/SP Operating temperature PSV inlet size Process unit

0.9333 0.0008 1.2687 1.6705 2.7687 3.7792

1.4501 3.8620 1.9807 1.9228 1.9807 2.1396

The generic failure condition may therefore be assumed temporarily to equal the current as-received test condition without distinguishing the degree of TP/SP bias condition of each PSV. That is, the sub-generic failure condition factor value of each PSV, Bi, is equal to TP/SP. The weighting factor of the current asreceived tested data, W Bi , is assumed to be unity.

Table 2 Correlation results and weighting factors contribute to the likelihood factor

Correlation factors

Correlation results

Weighting factors ( W Ai ) (%)

Inlet size of PSVs Process unit

0.1789 0.0128

93.31 6.69

Following the designated scheme of RBI assessment in this case study, the LOF of each PSV was evaluated according to the listed values of Table 3, Eqs. (3)–(5). The ranking results and the distributions are shown in Table 4. 3.3.3. Assessment of the COF For the sake of simplicity, it is assumed that containments are released through the fracture of a pressure vessel due to the failure of a PSV. Therefore, it is reasonable to apply the index of

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the toxicity, health, and environmental hazards from the RBI COF results of the pressure vessels in evaluating the COF of PSV. From the calculation of the index of toxicity, health, and environmental hazards for each PSV by Eq. (6), the COF ranking results and the distributions are listed in Table 5.

100%

Table 4 Ranking results for LOF category

LOF category

PSV quantities located in each LOF category

LOF ( f likelihood ) Â ( f generic failure condition )

PSV percentage located in each LOF category (%)

1

1 0

LOFp3.6 3.6oLOFp12.4

0.44

2

8 16

12.4oLOFp21.2 21.2oLOFp30.0

10.48

3

25 123

30.0oLOFp38.8 38.8oLOFp47.6

64.63

4

50 2

47.6oLOFp56.4 56.4oLOFp65.1

22.71

5

2 2

65.1oLOFp73.9 73.9oLOFp82.7

1.75

90% 80% n i m x a m P S / P T

70% 60% 50% 40% 30%

y = -0.0624x + 0.5185 R2 = 0.3328

20% 10% 0% 0

1

2

3

4

5

6

7

8

9

Inlet size (inches) Table 5 Ranking results for COF category

Fig. 4. The bias distributions of the TP/SP ratios vs. inlet sizes.

100%

COF category

PSV quantities located in each COF category

COF (SC i)

PSV percentage located in each COF category (%)

1

109 18

COFp0.5 0.5oCOFp1.6

55.46

2

27 5

1.6oCOFp2.7 2.7oCOFp3.7

13.97

3

8 15


10.04

4

46 0


20.09

5

0 1


0.44

90% 80% n i m x a m P S / P T

70% 60% y = -0.0004x + 0.9647 R2 = 0.1411

50% 40% 30% 20% 10% 0% 1100

1200

1300

1400

1500

1600

1700

1800

1900

2000

Process Unit No. Fig. 5. The bias distributions of the TP/SP ratios vs. different process units.

Table 3 Category list of sub-likelihood factor

Approach step

TP/SP max.–min. (note) ( Ai) (%) W Ai (from Table 2) (%) Value of Ai Â W Ai in each group range (%) Sub-likelihood factor score

Significantly influential operating parameters Inlet size of PSVs ( A1) (unit: inch) 0.5

0.75

1

1.5

2

3

4

6

8

49 93.31 45.5 45.5

47

46

42

39

33

27

14

2

44.0 44.0

42.6 42.6

39.6 39.6

36.7 36.7

30.9 30.9

25.1 25.1

13.4 13.4

1.8 1.8

Process unit ( A2)

TP/SP max.–min. (note) ( Ai) (%) W Ai (from Table 2) (%) Value of Ai Â W Ai in each group range (%) Sub-likelihood factor score

1200

1300

1400

1500

1600

1800

1900

48 6.69 3.24 3.2

44

40

36

32

24

20

2.98 3.0

Note: linear regression results for the PSV parameters are listed as follows: TP/SP max.–min. ( A1 value) ¼ À0.0624(inlet size of PSV)+0.5185. TP/SP max.–min. ( A2 value) ¼ À0.0004(process unit)+0.9647.

2.71 2.7

2.44 2.4

2.17 2.2

1.64 1.6

1.37 1.4

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3.3.4. Risk calculation of PSVs The risk value of each PSV in the lubricant plant was calculated by following the designated RBI scheme. The risk categories were ranked from 1 to 5 based on the risk value of each PSV. In this case study, PSVs with lower risk values are assigned a lower rank. PSVs belonging to the high-risk category will be the critical population and the inspection intervals for those should be reduced. The RBI assessment results for all PSVs and their corresponding distributions are shown in Table 6 and Fig. 6, respectively. Obviously, it can be seen from Table 6 and Fig. 6 that most of the risks result from a few PSVs that belong to the higher-risk categories. The inspection intervals of the higher-risk PSVs should be considerably shorter than the 2 years in accordance with the current local government regulations. So, from the viewpoint of risk management, inspection intervals for all categories of PSVs should be estimated and established during the service extension.

PSVs in the pressurized system were properly inspected and adjusted according to the required set pressure during the turnaround period. The ratio of TP/SP for each PSV should equal unity when the resetting work is finished. As the in-service time passes, the TP/SP value of each PSV may differ from unity, either increasing or decreasing, based on the operating characteristic during in-service hours. In this case study, two sets of service time frame data (1-year operation and 3-year operation) were obtained from the process units. The ANOVA result shows the time frame is not a significantly influential parameter on the aging condition of the PSV. However, by focusing on the system integrity of the RBI scheme, it requires further statistical analysis on the service time frame data to optimize the inspection interval of PSVs. Fig. 7 shows the aging models of both 1-year-operating and 3-year-operating PSVs by plotting the cumulative density function of PSV quantity against the as-received test data. In Fig. 7, one can

3.4. Inspection interval suggestion X <= 0.894 5.0%

1

The last and the most essential step in the proposed RBI scheme is the determination of the next inspection date to mitigate against the high risk potential of PSVs. Following inspection and maintenance procedures, it is assumed that all

Table 6 RBI assessment results for all PSVs

Risk category

1

PSV quantities located in each risk category 4 120

Risk value

Riskp7.2 7.2oRiskp43.8

2

15 23

43.8oRiskp80.3 80.3oRiskp116.8

3

5 14

116.8oRiskp153.4 153.4oRiskp189.9

4

5

y t i t n y t a i s u n Q e D V e S v P i r t o a f l u n m o i u t C c n u F

16.59

8.30

13 24

189.9oRiskp226.5 226.5oRiskp263.0

16.16

7 4

263.0oRiskp299.5 299.5oRiskp336.1

4.80

0.8 0.6 0.4 0.2

1-Year Operation

0

PSV percentage located in each risk category (%) 54.15

X <= 1.184 95.0%

0.6

0.8

1

1.2

1.4

1.6

1.8

TP/SP X <= 0.873 5.0%

1 y t i t n y t a i s u n Q e D V e S v P i r t o a f l u n m o i u t C c n u F

X <= 1.244 95.0%

0.8 0.6 0.4 0.2

3-Years Operation

0 0.6

0.8

1

1.2

1.4

1.6

1.8

TP/SP Fig. 7. The cumulative density function between 1-year and 3-year-operated PSVs.

Fig. 6. RBI assessment results for all PSVs.

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2.2

y = 1.0421e0.0659x R2 = 0.9865

2 1.8 1.6 1.4

y = 1.0073e 0.0092x R2 = 0.9784

P 1.2 S / P 1 T

0.8

y = 0.9718e-0.0405x R2 = 0.9828

0.6 0.4 0.2 0 0

2

4

6

8

10

12

Inspection Interval Fig. 8. Exponential curve fitting to estimate the next inspection date of PSV.

Table 7 Suggestion for the inspection interval of each category

4. Conclusion

Risk category

PSV percentage located in each risk category (%)

Suggestion for inspection interval in each category (months)

1 2 3 4 5

54.15 16.59 8.3 16.16 4.80

60 48 36 24 12

see that the mean value of each data set does not change significantly with the time frame. However, based on 95% confidence level, the upper value of TP/SP for 1-year-operation and 3-year-operation PSVs equals 1.184 and 1.244, respectively. It shows a slight aging trend exists as the time frame increases. So, in order to conservatively establish the next inspection date based on the risk of the probability of failure on demand, the suggested strategy for the plant operator is to trace and watch the PSVs with high failure potential, which are near the upper value of 95% confidence level when the PSV service duration is extended. Suppose the acceptable risk of the plant operator is 5% on the probability of failure on demand and the aging model is an exponential function of the service time frame with a conservative estimation. Then, by using the 1-year-operation data and the 3-year-operation data, one can plot the estimated fitted curve of the PSV aging trend against the in-service time frame based on the 95% confidence level as shown in Fig. 8. Within the 95% confidence interval, if the ratio of TP/SP of the PSVs in the pressurized system would not increase beyond 1.5 during the extended service duration, the inspection interval will be located at 5 years. That is, except for damaged PSVs, the service duration of PSVs used in the process units will last 60 months till the next inspection date in this case study. By referring to the RBI ranking results, it is suggested that the difference in inspection interval between any two adjacent category ranks is 12 months, the inspection interval of each category can be obtained and is listed in Table 7. From Table 7, one finds that the inspection intervals of about 5% of total assessed PSVs should be shorter than the current inspection intervals required by the Taiwan regulations. If the plant operator follows the jurisdiction regulation of 2-year inspection intervals, only 21% of total assessed PSVs should follow up the inspection and maintenance during service extension.

In this study, a semi-quantitative RBI methodology is established which shows a plan, do, correct, and action (PDCA) loop in the inspection/ maintenance strategies of PSVs as shown in Fig. 3. The PDCA loop outlined in this paper not only considers the inspection/maintenance conditions of PSVs, but also includes process upset in the condition assessments. The presented methodology was successfully performed on the PSVs in a lubricant plant during the extended service period and resulted in a high level of confidence both in the safety of the pressurized system and in the fulfillment of regulations administrated by the CLA in Taiwan. The ANOVA results show that service time frame is not a significantly influential parameter for the PSV aging condition, which makes it difficult to estimate the next inspection date of PSVs. However, it is shown that the proposed RBI scheme can provide the estimation of the next inspection date of each PSV based on two sets of time frame data under the assumption of an exponential aging model for PSVs and a 95% confidence level. A semi-quantitative RBI scheme is established and a suggested inspection interval for each PSV is obtained in this study. However, reassessing the primary RBI results is necessary if the current operating mode is changed since the aging model will also change then. Furthermore, the small R square value of linear regression results can be improved through the long-term observation of system characteristics. Also, although the conservative evaluation of maximum service duration is 5 years, which complies with the API 510 code in this study, a thorough feedback of site examination and evaluation during the next turnaround should be followed up to document the experience, which may indicate a longer inspection and maintenance interval are still acceptable.

Acknowledgments

The data studied in this paper are partially provided by the auditors of Department of Occupational Safety Office, Labor Affairs Bureau, Kaohsiung City Government, Taiwan. The authors would like to thank the chief inspector M. S. Ho and his colleagues for their suggestions, supports, and for providing the as-received test data of the process units.

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[5] Fitness for service. American Petroleum Institute, API Recommended Practice 579-1. June 2007. [6] Risk-based inspections. American Petroleum Institute, API Recommended Practice 580. May 2002. [7] Risk-based inspection base resource document. American Petroleum Institute, API Recommended Practice 581. May 2000. [8] Maintenance inspection, rating, repair, and alteration. American Petroleum Institute, API 510 Pressure Vessel Inspection Code. August 2003. [9] Inspection of pressure-relieving devices. American Petroleum Institute, API Recommended Practice 576. December 2000. [10] Lewis EE. Introduction to reliability engineering. New York: Wiley; 1987. [11] Sturges HA. The choice of a class interval. J Am Stat Assoc 1926;21:65–6.

A strategy for the risk-based inspection of pressure safety valves (2009).pdf

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