Genetic Algorithm Optimization Model for Determining the Probability of Failure on Demand of the Safety Instrumented System

Source:
By:Ahmed H. Aburawwash, Moustafa Mohammed Eissa, Azza F. Barakat, Hossam M. Hafez

DOI: https://doi.org/10.30564/ese.v1i2.994

Abstract:

A more accurate determination for the Probability of Failure on Demand (PFD) of the Safety Instrumented System (SIS) contributes to more SIS realiability, thereby ensuring more safety and lower cost. IEC 61508 and ISA TR.84.02 provide the PFD detemination formulas. However, these formulas suffer from an uncertaity issue due to the inclusion of uncertainty sources, which, including high redundant systems architectures, cannot be assessed, have perfect proof test assumption, and are neglegted in partial stroke testing (PST) of impact on the system PFD. On the other hand, determining the values of PFD variables to achieve the target risk reduction involves daunting efforts and consumes time. This paper proposes a new approach for system PFD determination and PFD variables optimization that contributes to reduce the uncertainty problem. A higher redundant system can be assessed by generalizing the PFD formula into KooN architecture without neglecting the diagnostic coverage factor (DC) and common cause failures (CCF). In order to simulate the proof test effectiveness, the Proof Test Coverage (PTC) factor has been incorporated into the formula. Additionally, the system PFD value has been improved by incorporating PST for the final control element into the formula. The new developed formula is modelled using the Genetic Algorithm (GA) artificial technique. The GA model saves time and effort to examine system PFD and estimate near optimal values for PFD variables. The proposed model has been applicated on SIS design for crude oil test separator using MATLAB. The comparison between the proposed model and PFD formulas provided by IEC 61508 and ISA TR.84.02 showed that the proposed GA model can assess any system structure and simulate industrial reality. Furthermore, the cost and associated implementation testing activities are reduced.

References:

[1] Health and Safety Executive, The Flixborough disaster: report of the court of inquiry, HMSO, ISBN 0113610750, 1975. [2] F. Lees, Hazard identification, assessment and control, Loss Prevention in the Process Industries, 3:3, Butterworth Heinemann, ISBN 0 7506 1547 8, 1996. [3] W. Cullen, The public inquiry into the piper alpha disaster, HMSO, London, 1990. [4] IEC Standard 61508 Standard, Edition 2, Functional safety of electrical / electronic / programmable electronic safety related systems, 2010. [5] IEC 61511 Standard, Edition 2, Functional safety-safety instrumented systems for the process industry sector, 2010. [6] ISA-TR.84.00.02, Application of safety instrumented systems for the process control industry”, Instrumentation Society of America, (ISA), 2002. [7] H. Jin, M. Lundteigen, and M. Rausand, Uncertainty assessment of reliability estimates for safety-instrumented systems, Proc IMechE Part O, J Risk and Reliability, Ó IMechE, 2012, 226(6): 646–655. [8] L. Oliveira and R. Abramovitch, Extension of ISA TR84.00.02 PFD equations to KooN architectures, Reliability Engineering and System Safety, 2010, 95 (7): 707–715. [9] M. Abdelrhafour, N. Bajaj and S. Boil, Proof test procedure effectiveness on safety instrumented systems, 2013. [10] J. Börcsök, P. Holub, M. Schwarz et al. Probability of failure on demand for systems with partial stroke test, International Journal of Mathematical Models and Methods in Applied Science, 2007, 1(4). [11] R. Freeman and A. Summers, Evaluation of uncertainty in safety integrity level calculations, American Institute of Chemical Engineers Process Safety Progress, 2016, 35(4). [12] Sh. Wu, L. Laibin, M. Lundteigen, W. Zheng. Reliability assessment for final elements of SISs with time dependent failures. Journal of Loss Prevention in the Process Industries, 2018, 51. [13] H. EL-Sayed. Will a partial valve stroke testing lead to a higher SIL?. Hazardex, Conference and Exhibition, Runcorn, Cheshire UK, 2016. [14] H. Khan. Generalizing PFD formulas of IEC 61508 for KooN configurations. ISA Transactions, 2016, 55: 168-174. [15] L. Oliveira and R. Abramovitch, Extension of ISA TR84.00.02 PFD equations to KooN architectures. Reliability Engineering and System Safety, 2010, 95 (7): 707–715. [16] M. Chebila, and F. Innal, Generalized analytical expressions for safety instrumented systems performance measures Pfd avg and pfh. Journal of Loss Prevention in the Process Industries, 2015, 34:167–176. [17] W. Martins, Methods for determining pfd/sil for workover control systems with short test-intervals and imperfect testing. 2014. [18] E. Naresh Ocheni, Impact of partial and imperfect testing on reliability assessment of safety instrumented systems, 2015. [19] T. Gabriel, A Hildebrandt, and U menck. PFD calculation considering Imperfect Proof Tests. Chemical Engineering transactions, 2016, 48. [20] L. Stewart. Are your safety instrumented systems proof tests effective?. EXIDA-Valve Magazine, 2017. [21] D. Hermawanto. Genetic algorithm for solving simple mathematical equality problem. Book, Chapter 2, Budapest, 2013. [22] M. Rausand, Reliability of safety-critical systems: theory and applications, Wiley, Hoboken, NJ. 2014. [23] M. Rausand and A. Hsyland, System reliability theory, models, statistical methods, and application, Second Edition, 2004. [24] H. Jin and M. Rausand. Reliability of safety-instrumented systems subject to partial testing and common-cause failures. Reliability Engineering & System Safety, 2014, 121: 146–151. [25] M. Lundteigen, M. Rausand, Common cause failures in safety instrumented systems on oil and gas installations: implementing defense measures through function testing. Journal of Loss Prevention in the Process Industries, 2007, 20: 218–229. [26] W. Mechri, C Simon, and K. Othman. Uncertainty analysis of common cause failure in safety instrumented systems. Proc. IMechE, part O: J. Risk and Reliability, 2010, 225. [27] D. Fournier. How critical is proof test coverage?: functional safety clarified. Canadian Process Equipment & Control News, 2009. [28] E. Naresh Ocheni, Impact of partial and imperfect testing on reliability assessment of safety instrumented systems, 2015. [29] F. Brissaud, A. Barros, and C. Bérenguer. Probability of failure on demand of safety systems: impact of partial test distribution. Journal of Risk and Reliability, 1748006X12448142, 2012. [30] F.Innal, Y. Liu, M. Lundteigen, et al.. Pfdavg and pfh formulas for sis subject to partial and full periodic tests. Reliability Engineering & System Safety, 2015. [31] S. Sachdeva, Imperfect testing and its influence on availability of safety instrumented systems, 2015. [32] W. Martins. Methods for determining pfd/sil for workover control systems with short test-intervals and imperfect testing, 2014. [33] A. E. Summers and B. Zachary. Partial stroke testing of block valves. Control Engineering, 2006, 47(12): 87–89. [34] M. Lundteigen and M. Rausand. Partial stroke testing of process shutdown valves: How to determine the test coverage. Journal of Loss Prevention in the Process Industries, 2008: 579–588. [35] M. Lundteigen and M. Rausand. The effect of partial stroke testing on the reliability of safety valves. Reliability and Societal Safety, Proceedings of the European Safety and Reliability Conference, 2007. [36] Mansoura Petroleum Company, Inst.-Doc No. 4607-815-10-RT-002. Engineering works for west khelala plant, hazop report, 2016. [37] Mansoura Petroleum Company, Inst.-Doc No. 4607-815-KCB-001Rev.0, C&E. [38] Mansoura Petroleum Company, Inst.-Doc No. 4607-815-KKB-01/2/3/4/5/6-Rev.1, P&IDs.