Dynamic Risk Assessment of Industrial Safety Barriers Using Bayesian Networks: A Predictive Modeling Approach
DOI:
https://doi.org/10.61978/catalyx.v2i3.1341Keywords:
Dynamic Bayesian Network, safety barrier degradation, risk assessment, SIS unavailability, corrosion rate, predictive maintenance, process safetyAbstract
Traditional risk assessment methods in chemical and process industries frequently fail to capture the dynamic nature of barrier degradation and hazard escalation. This study proposes a Dynamic Bayesian Network (DBN) framework integrating temporal indicators, including SIS unavailability and corrosion rate, to enhance the predictive accuracy of real-time risk management systems. The DBN was structured using nodes and dependencies derived from industrial scenarios and reliability parameters, then validated through two simulation cases — reactor runaway and corrosion-driven leak — utilizing real-time inputs of dT/dt and k_cor to dynamically update failure probabilities via MATLAB. Results demonstrate that barrier degradation significantly impacts risk profiles: escalation probability in the reactor runaway scenario increased from 0.10 to 0.45 as SIS unavailability rose, while leak probability reached severe consequences when barrier failure exceeded 60%. Compared to static models, the DBN approach more accurately captured emergent risks over time. The framework supports predictive maintenance, alarm prioritization, and human reliability modeling, establishing DBNs as valuable tools for transitioning toward intelligent, adaptive safety systems in high-risk industries.
References
Amano, K. O. A., Hahn, S.-K., Tschirschwitz, R., Rappsilber, T., & Krause, U. (2022). An Experimental Investigation of Thermal Runaway and Gas Release of NMC Lithium-Ion Pouch Batteries Depending on the State of Charge Level. Batteries, 8(5), 41. https://doi.org/10.3390/batteries8050041 DOI: https://doi.org/10.3390/batteries8050041
Anchan, A., Kalogirou-Baldwin, P., Johnson, R., Kho, D. T., Joseph, W. R., Hucklesby, J., Finlay, G. J., O’Carroll, S. J., Angel, C. E., & Graham, E. S. (2019). Real-Time Measurement of Melanoma Cell-Mediated Human Brain Endothelial Barrier Disruption Using Electric Cell-Substrate Impedance Sensing Technology. Biosensors, 9(2), 56. https://doi.org/10.3390/bios9020056 DOI: https://doi.org/10.3390/bios9020056
Andrews, J., & Fecarotti, C. (2017). System Design and Maintenance Modelling for Safety in Extended Life Operation. Reliability Engineering & System Safety, 163, 95–108. https://doi.org/10.1016/j.ress.2017.01.024 DOI: https://doi.org/10.1016/j.ress.2017.01.024
BahooToroody, A., Abaei, M. M., Arzaghi, E., BahooToroody, F., Carlo, F. D., & Abbassi, R. (2019). Multi-Level Optimization of Maintenance Plan for Natural Gas System Exposed to Deterioration Process. Journal of Hazardous Materials, 362, 412–423. https://doi.org/10.1016/j.jhazmat.2018.09.044 DOI: https://doi.org/10.1016/j.jhazmat.2018.09.044
Behie, S. W., Halim, S. Z., Efaw, B., O’Connor, T., & Quddus, N. (2020). Guidance to Improve the Effectiveness of Process Safety Management Systems in Operating Facilities. Journal of Loss Prevention in the Process Industries, 68, 104257. https://doi.org/10.1016/j.jlp.2020.104257 DOI: https://doi.org/10.1016/j.jlp.2020.104257
Bhandari, J., Arzaghi, E., Abbassi, R., Garaniya, V., & Khan, F. (2016). Dynamic Risk‐based Maintenance for Offshore Processing Facility. Process Safety Progress, 35(4), 399–406. https://doi.org/10.1002/prs.11829 DOI: https://doi.org/10.1002/prs.11829
Cai, B., Liu, Y., & Xie, M. (2017). A Dynamic-Bayesian-Network-Based Fault Diagnosis Methodology Considering Transient and Intermittent Faults. Ieee Transactions on Automation Science and Engineering, 14(1), 276–285. https://doi.org/10.1109/tase.2016.2574875 DOI: https://doi.org/10.1109/TASE.2016.2574875
Chen, Q., Tang, B., Song, J., Jiang, Y., Zhao, X., Ruan, Y., Zhao, F., Wu, G., Chen, T., & He, J. (2022). Dynamic Bayesian Network for Predicting Physiological Changes, Organ Dysfunctions and Mortality Risk in Critical Trauma Patients. BMC Medical Informatics and Decision Making, 22(1). https://doi.org/10.1186/s12911-022-01803-y DOI: https://doi.org/10.1186/s12911-022-01803-y
Fil, A., Henden, L., Newton, S. L., Hoppe, M., & Vallhagen, O. (2024). Disruption Runaway Electron Generation and Mitigation in the Spherical Tokamak for Energy Production (STEP). Nuclear Fusion, 64(10), 106049. https://doi.org/10.1088/1741-4326/ad73e9 DOI: https://doi.org/10.1088/1741-4326/ad73e9
Gillich, E. I., Steinhardt, M., Fedoryshyna, Y., & Jossen, A. (2024). Mechanical Measurement Approach to Characterize Venting Behavior During Thermal Runaway of 18650 Format Lithium-Ion Batteries. Batteries, 10(4), 142. https://doi.org/10.3390/batteries10040142 DOI: https://doi.org/10.3390/batteries10040142
Jepu, I., Widdowson, A., Matthews, G. F., Coad, J. P., Likonen, J., Brezinsek, S., Rubel, M., Pintsuk, G., Petersson, P., Fortuna-Zaleśna, E., Grzonka, J., Poroşnicu, C., Dincă, P., Pompilian, O. G., Butoi, B., Moga, S. G., Silburn, S., Kuksenko, S., Alves, E., … Ratynskaia, S. (2024). Overview of Damage to Beryllium Limiters by Unmitigated Disruptions and Runaway Electrons in the JET Tokamak With Metal Walls. Nuclear Fusion, 64(10), 106047. https://doi.org/10.1088/1741-4326/ad6614 DOI: https://doi.org/10.1088/1741-4326/ad6614
Kabir, S., & Papadopoulos, Y. (2018). A Review of Applications of Fuzzy Sets to Safety and Reliability Engineering. International Journal of Approximate Reasoning, 100, 29–55. https://doi.org/10.1016/j.ijar.2018.05.005 DOI: https://doi.org/10.1016/j.ijar.2018.05.005
Kodikara, S., Ellul, S., & Cao, K. L. (2022). Statistical Challenges in Longitudinal Microbiome Data Analysis. Briefings in Bioinformatics, 23(4). https://doi.org/10.1093/bib/bbac273 DOI: https://doi.org/10.1093/bib/bbac273
Kouhili, Y., Vernières‐Hassimi, L., & Estel, L. (2023). Faults Detection Using Thermal Runaway Criteria on Different Reaction Kinetics. Matec Web of Conferences, 379, 01009. https://doi.org/10.1051/matecconf/202337901009 DOI: https://doi.org/10.1051/matecconf/202337901009
Kyritsakis, A., Veske, M., Eimre, K., Zadin, V., & Djurabekova, F. (2018). Thermal Runaway of Metal Nano-Tips During Intense Electron Emission. Journal of Physics D Applied Physics, 51(22), 225203. https://doi.org/10.1088/1361-6463/aac03b DOI: https://doi.org/10.1088/1361-6463/aac03b
Lewis, A. D., & Groth, K. M. (2020). A Dynamic Bayesian Network Structure for Joint Diagnostics and Prognostics of Complex Engineering Systems. Algorithms, 13(3), 64. https://doi.org/10.3390/a13030064 DOI: https://doi.org/10.3390/a13030064
Lieberth, K., Pavlou, A., Harig, D., Blom, P. W. M., Gkoupidenis, P., & Torricelli, F. (2023). Real‐Time Monitoring of Cellular Barrier Functionality With Dynamic‐Mode Current‐Driven Organic Electrochemical Transistor. Advanced Materials Technologies, 8(10). https://doi.org/10.1002/admt.202201697 DOI: https://doi.org/10.1002/admt.202201697
Liu, H., He, X., Jiao, Y., & Wang, X. (2019). Reliability Assessment of Deflection Limit State of a Simply Supported Bridge Using Vibration Data and Dynamic Bayesian Network Inference. Sensors, 19(4), 837. https://doi.org/10.3390/s19040837 DOI: https://doi.org/10.3390/s19040837
Lu, Y., Lin, R., & Wang, C. (2024). Propose a Practical Plan for the Identification of Safety‐Critical Equipment. Process Safety Progress, 44(1), 39–48. https://doi.org/10.1002/prs.12659 DOI: https://doi.org/10.1002/prs.12659
Luque, J., & Straub, D. (2016). Reliability Analysis and Updating of Deteriorating Systems With Dynamic Bayesian Networks. Structural Safety, 62, 34–46. https://doi.org/10.1016/j.strusafe.2016.03.004 DOI: https://doi.org/10.1016/j.strusafe.2016.03.004
Maduabuchi, E., Ugbebor, J., & Israel, G. O. (2023). Needs and Challenges of Nigeria Petroleum Industry in Assessing Process Safety Cumulative Risk. Journal of Geoscience and Environment Protection, 11(07), 192–204. https://doi.org/10.4236/gep.2023.117013 DOI: https://doi.org/10.4236/gep.2023.117013
Naji, G. M. A., Isha, A. S. N., Mohyaldinn, M. E., Leka, S., Saleem, M. S., Syed Mohamed Nasir Bin Syed Abd Rahman, & Alzoraiki, M. (2021). Impact of Safety Culture on Safety Performance; Mediating Role of Psychosocial Hazard: An Integrated Modelling Approach. International Journal of Environmental Research and Public Health, 18(16), 8568. https://doi.org/10.3390/ijerph18168568 DOI: https://doi.org/10.3390/ijerph18168568
Palencia, O. G., Teixeira, A. P., & Soares, C. G. (2018). Safety of Pipelines Subjected to Deterioration Processes Modeled Through Dynamic Bayesian Networks. Journal of Offshore Mechanics and Arctic Engineering, 141(1). https://doi.org/10.1115/1.4040573 DOI: https://doi.org/10.1115/1.4040573
Ratynskaia, S., Vignitchouk, L., & Tolias, P. (2022). Modelling of Dust Generation, Transport and Remobilization in Full-Metal Fusion Reactors. Plasma Physics and Controlled Fusion, 64(4), 044004. https://doi.org/10.1088/1361-6587/ac4b94 DOI: https://doi.org/10.1088/1361-6587/ac4b94
Sauerhöfer-Rodrigo, F., Díaz, I., Rodríguez, M. I. L., & Pérez, P. (2023). Modelling of Fixed Bed and Slurry Bubble Column Reactors for Fischer–Tropsch Synthesis. Reviews in Chemical Engineering, 40(2), 151–192. https://doi.org/10.1515/revce-2022-0041 DOI: https://doi.org/10.1515/revce-2022-0041
Singh, P., Gulijk, C. v., & Sunderland, N. (2023). Online Process Safety Performance Indicators Using Big Data: How a PSPI Looks Different From a Data Perspective. Safety, 9(3), 62. https://doi.org/10.3390/safety9030062 DOI: https://doi.org/10.3390/safety9030062
Tao, X., Yao, R., Zhao, F., Zhu, L., & Chen, X. (2024). Thermal Risk Assessment of the Complex Nitration Process Through Safety Operating Space Analysis and Optimization. Industrial & Engineering Chemistry Research, 63(29), 12887–12901. https://doi.org/10.1021/acs.iecr.4c01014 DOI: https://doi.org/10.1021/acs.iecr.4c01014
Tran, T.-B., Bastidas‐Arteaga, E., & Aoues, Y. (2020). A Dynamic Bayesian Network Framework for Spatial Deterioration Modelling and Reliability Updating of Timber Structures Subjected to Decay. Engineering Structures, 209, 110301. https://doi.org/10.1016/j.engstruct.2020.110301 DOI: https://doi.org/10.1016/j.engstruct.2020.110301
Uçar, A., Karaköse, M., & Kırımça, N. (2024). Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends. Applied Sciences, 14(2), 898. https://doi.org/10.3390/app14020898 DOI: https://doi.org/10.3390/app14020898
Vito, R. d., Conte, C., & Traina, G. (2022). A Multi-Strain Probiotic Formulation Improves Intestinal Barrier Function by the Modulation of Tight and Adherent Junction Proteins. Cells, 11(16), 2617. https://doi.org/10.3390/cells11162617 DOI: https://doi.org/10.3390/cells11162617
Vries, P. C. d., Gribov, Y., Martin-Solis, R., Mineev, A. B., Sinha, J., Sips, A. C. C., Kiptily, V., & Loarte, A. (2020). Analysis of Runaway Electron Discharge Formation During Joint European Torus Plasma Start-Up. Plasma Physics and Controlled Fusion, 62(12), 125014. https://doi.org/10.1088/1361-6587/abbe34 DOI: https://doi.org/10.1088/1361-6587/abbe34
Wang, Q., Yang, G., Zhu, J., Zheng, T., Su, B., & Zhang, B. (2024). A Mutual Information Based Multifeature Pattern Recognition Criterion for Identification and Optimization of Thermally Safe Operating Conditions for Single Diffusion‐Controlled Reactions Occurring in Isoperibolic Liquid–Liquid Semibatch Reactors. Asia-Pacific Journal of Chemical Engineering, 20(3). https://doi.org/10.1002/apj.3190 DOI: https://doi.org/10.1002/apj.3190
Zarei, E., Azadeh, A., Khakzad, N., Aliabadi, M. M., & Mohammadfam, I. (2017). Dynamic Safety Assessment of Natural Gas Stations Using Bayesian Network. Journal of Hazardous Materials, 321, 830–840. https://doi.org/10.1016/j.jhazmat.2016.09.074 DOI: https://doi.org/10.1016/j.jhazmat.2016.09.074
Zhao, R., Guo, Z., Zhang, R., Deng, C., Xu, J., Dong, W., Hong, Z., Yu, H., Situ, H., Liu, C., & Zhuang, G. (2017). Nasal Epithelial Barrier Disruption by Particulate Matter ≤2.5 Μm via Tight Junction Protein Degradation. Journal of Applied Toxicology, 38(5), 678–687. https://doi.org/10.1002/jat.3573 DOI: https://doi.org/10.1002/jat.3573
Zhu, J., Chen, G., Khan, F., Yang, M., Li, X., Meng, X., & He, R. (2021). A Sequence-Based Method for Dynamic Reliability Assessment of MPD Systems. Process Safety and Environmental Protection, 146, 927–942. https://doi.org/10.1016/j.psep.2020.12.015 DOI: https://doi.org/10.1016/j.psep.2020.12.015
Zhu, R., Li, X., Hu, X., & Hu, D. (2019). Risk Analysis of Chemical Plant Explosion Accidents Based on Bayesian Network. Sustainability, 12(1), 137. https://doi.org/10.3390/su12010137 DOI: https://doi.org/10.3390/su12010137
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