Human reliability assessment for tank overfilling incident utilizing minimized human performance shaping factors

Human reliability assessment for tank overfilling incident utilizing minimized human performance shaping factors


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Abstract. Human errors are identified as significant contributors to process industry accidents. Human reliability analysis (HRA) has been conducted in previous studies to improve human performance in several industrial operations. However, human error predictions are greatly influenced by various performance-shaping factors (PSFs). Research also demonstrates that PSFs are interdependent, which thereby complicates the modeling and analysis. Therefore, this study performs HRA, for a tank overfilling accident scenario that resulted due to human failure. Fewer independent PSFs through careful classification were used to estimate tank overfilling probability resulting from different human-triggered factors. For HRA, this study uses a combination of the Standardized Plant Analysis Risk Human Reliability Analysis (SPAR-H) and Bayesian Belief Network (BBN). The failure probability distributions of individual interconnecting tasks were calculated using SPAR-H, and the probabilistic interdependence of each task to the final tank overfilling scenario was modeled using a BBN. From the current analysis, divergent stream identification is determined as the key to lead tank overfilling with 40% probability. This study concludes that BBN can be reliably employed in the Quantitative Risk Analysis (QRA) framework to examine human factors in industrial failure probability estimation for various other human-related industrial accident scenarios.

Human Reliability Assessment, Quantitative Risk Analysis, Bayesian Networks, Reliability, Safety

Published online 5/20/2023, 10 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: ASHER AHMED Malik, RISZA Rusli, FATIN AFIFAH Binti Mohd Nasir, SALMAN Nazir, RIZAL HARRIS Wong, Human reliability assessment for tank overfilling incident utilizing minimized human performance shaping factors, Materials Research Proceedings, Vol. 29, pp 135-144, 2023


The article was published as article 17 of the book Sustainable Processes and Clean Energy Transition

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

[1] C. Sharma, P. Bhavsar, B. Srinivasan, R. Srinivasan, Eye gaze movement studies of control room operators: A novel approach to improve process safety, Comput. Chem. Eng. 85 (2016) 43–57.
[2] E. Zarei, F. Khan, R. Abbassi, Importance of human reliability in process operation: A critical analysis, Reliab.Eng. Syst. Saf. 211 (2021) 107607.
[3] Directive 2012/18/EU. European Parliament and Council Directive 2012/18/EU of July 2012 on Control of Major-Accident Hazards Involving Dangerous Substances, Amending and Subsequently Repealing Council Directive 96/82/EC., Off. J. Eur. Communities L 197/1, Brussels, 24.7.2012. (n.d.).
[4] M. Musharraf, J. Smith, F. Khan, B. Veitch, S. MacKinnon, Incorporating individual differences in human reliability analysis: An extension to the virtual experimental technique, Saf. Sci. 107 (2018) 216–223.
[5] S.T. Ung, Evaluation of human error contribution to oil tanker collision using fault tree analysis and modified fuzzy Bayesian Network based CREAM, Ocean Eng. 179 (2019) 159–172.
[6] F. Laal, M. Pouyakian, M.J. Jafari, F. Nourai, A.A. Hosseini, A.R. Khanteymoori, Technical, human, and organizational factors affecting failures of firefighting systems (FSs) of atmospheric storage tanks: Providing a risk assessment approach using Fuzzy Bayesian Network (FBN) and content validity indicators, J. Loss Prev.Process Ind. (2020).
[7] W.M.P. Steijn, J.N. Van Kampen, D. Van der Beek, J. Groeneweg, P.H.A.J.M. Van Gelder, An integration of human factors into quantitative risk analysis using Bayesian Belief Networks towards developing a ‘QRA+,’ Saf. Sci. 122 (2020) 104514.
[8] U. Alkhaldi, M, Pathirage, C and Kulatunga, The role of human error in accidents within oil and gas industry in Bahrain, Usir. / (2017) 821–834.
[9] M.H. Wood, L. Fabbri, Challenges and opportunities for assessing global progress in reducing chemical accident risks, Prog. Disaster Sci. 4 (2019) 100044.
[10] R. Oktarinanda, N. Norazahar, Bayesian Analysis for Assessing Risks of Rotating Equipment and Its Financial Loss, IOP Conf. Ser. Mater. Sci. Eng. 808 (2020).
[11] F.A. Alaw, N. Sulaiman, H. Tan, Incorporation of human factors in risk analysis of oil and gas pipeline using Bayesian Network, (2018) 1–9.
[12] P. Swuste, W. Zwaard, J. Groeneweg, F. Guldenmund, Safety professionals in the Netherlands, Saf. Sci. 114 (2019) 79–88.
[13] R. Yang, F. Khan, E.T. Neto, R. Rusli, J. Ji, Could pool fire alone cause a domino effect?, Reliab. Eng. Syst. Saf. 202 (2020) 106976.
[14] A.A. Malik, M.S. Nasif, A.A. Mokhtar, M.Z. Mohd Tohir, Numerical investigation of the effect of weather conditions on the escalation and propagation of fire-induced domino effect, Process Saf. Prog. (2021) 1–13.
[15] S. Mannan, Lee’s Loss Prevention in the Process Industries, 2005.
[16] L. Torres, O.P. Yadav, E. Khan, A review on risk assessment techniques for hydraulic fracturing water and produced water management implemented in onshore unconventional oil and gas production, Sci. Total Environ. 539 (2016) 478–493.
[17] X. Zhen, J.E. Vinnem, C. Peng, Y. Huang, Quantitative risk modelling of maintenance work on major offshore process equipment, J. Loss Prev. Process Ind. 56 (2018) 430–443.
[18] R.D. Calvo Olivares, S.S. Rivera, J.E. Núñez Mc Leod, A novel qualitative prospective methodology to assess human error during accident sequences, Saf. Sci. 103 (2018) 137–152.
[19] W.M.P. Steijn, J. Groeneweg, F.A. van der Beek, J. van Kampen, P. van Gelder, An integration of human factors into quantitative risk analysis: A proof of principle, Saf. Reliab. – Theory Appl. – Proc. 27th Eur. Saf. Reliab. Conf. ESREL 2017. (2017) 321–328.
[20] H. Rozuhan, M. Muhammad, U. Muhammad, Probabilistic risk assessment of o ff shore installation hydrocarbon releases leading to fire and explosion, incorporating system and human reliability analysis, Appl. Ocean Res. 101 (2020) 102282.
[21] J.E. Vinnem, R. Bye, B.A. Gran, T. Kongsvik, O.M. Nyheim, E.H. Okstad, J. Seljelid, J. Vatn, Journal of Loss Prevention in the Process Industries Risk modelling of maintenance work on major process equipment on offshore petroleum installations, J. Loss Prev. Process Ind. 25 (2012) 274–292.
[22] X. Zhen, J. Erik, C. Peng, Y. Huang, Quantitative risk modelling of maintenance work on major offshore process equipment, 4794207768 (1990) 1–27.
[23] B.A. Gran, R. Bye, O.M. Nyheim, E.H. Okstad, J. Seljelid, S. Sklet, J. Vatn, J.E. Vinnem, Journal of Loss Prevention in the Process Industries Evaluation of the Risk OMT model for maintenance work on major offshore process equipment, J. Loss Prev. Process Ind. 25 (2012) 582–593.
[24] M. Hänninen, O.A. Valdez, P. Kujala, Expert Systems with Applications Bayesian network model of maritime safety management, 41 (2014) 7837–7846.
[25] N. Khakzad, F. Khan, P. Amyotte, Safety Scien ce Quantitative risk analysis of offshore drilling operations : A Bayesian approach, Saf. Sci. 57 (2013) 108–117.
[26] N. Khakzad, G. Reniers, Cost-effective allocation of safety measures in chemical plants w.r.t land-use planning, Saf. Sci. 97 (2017) 2–9.
[27] N. Khakzad, G. Reniers, Risk-based design of process plants with regard to domino effects and land use planning, J. Hazard. Mater. 299 (2015).
[28] N. Khakzad, P. Amyotre, F. Khan, Valerio Cozzani, Domino Effect Analysis Using Bayesian Networks, Risk Anal. 33 (2013) 292–306.
[29] N. Khakzad, F. Khan, P. Amyotte, V. Cozzani, Risk Management of Domino Effects Considering Dynamic Consequence Analysis, Risk Anal. 34 (2014) 1128–1138.
[30] N. Khakzad, Modeling wildfire spread in wildland-industrial interfaces using dynamic Bayesian network, Reliab. Eng. Syst. Saf. 189 (2019) 165–176.
[31] N. Khakzad, A Tutorial on Fire Domino Effect Modeling Using Bayesian Networks, Modelling. 2 (2021) 240–258.
[32] J. Park, W. Jung, J. Kim, Inter-relationships between performance shaping factors for human reliability analysis of nuclear power plants, Nucl. Eng. Technol. 52 (2020) 87–100.
[33] R.L. Boring, SPAR-H Step-by-Step Guidance, (2015).
[34] P. Liu, Z. Li, Human Error Data Collection and Comparison with Predictions by SPAR-H, Risk Anal. 34 (2014)1706–1719.
[35] D.I. Gertman, BlackmanH.S., J. Byers, L. Haney, C. Smith, J. Marble, NUREG/CR-6883-The SPAR-H method-US Nuclear regulatory commision, Washington D.C., (2005).
[36] K.M. Groth, L.P. Swiler, Bridging the gap between HRA research and HRA practice: A Bayesian network version of SPAR-H, Reliab. Eng. Syst. Saf. 115 (2013) 33–42.
[37] B. Hallbert, A. Whaley, R. Boring, P. McCabe, Y. Chang, O. of N.R.R. U.S. Nuclear Regulatory Commission, Human Event Repository and Analysis (HERA): The HERA Coding Manual and Quality Assurance (NUREG/CR-6903, Vol. 2), U.S. Nucl. Regul. Comm. Off. Nucl. Regul. Res. Washington, DC 20555-0001. 2 (2007) 133.
[38] M. Mirzaei Aliabadi, R. Esmaeili, I. Mohammadfam, M. Ashrafi, Human Reliability Analysis (HRA) Using Standardized Plant Analysis Risk-Human (SPAR-H) and Bayesian Network (BN) for Pipeline Inspection Gauges (PIG) Operation: A Case Study in a Gas Transmission Plant, Heal. Scope. 8 (2019).
[39] J.R. Van Dorp, S. Kotz, A novel extension of the triangular distribution and its parameter estimation, J. R. Stat. Soc. Ser. D Stat. 51 (2002) 63–79.
[40] W.E. Stein, M.F. Keblis, A new method to simulate the triangular distribution, Math. Comput. Model. 49 (2009) 1143–1147.
[41] P. Weber, G. Medina-Oliva, C. Simon, B. Iung, Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas, Eng. Appl. Artif. Intell. 25 (2012) 671–682.
[42] GeNie Modeler- BayesFusion, (2020).
[43] S.H. Chen, C.A. Pollino, Environmental Modelling & Software Good practice in Bayesian network modelling, Environ. Model. Softw. 37 (2012) 134–145.
[44] G. Song, F. Khan, M. Yang, Integrated risk management of hazardous processing facilities, Process Saf. Prog. 38 (2019) 42–51.