- Hoffmann Souza, M. L., da Costa, C. A., de Oliveira Ramos, G., & da Rosa Righi, R. (2020). A survey on decision-making based on system reliability in the context of Industry 4.0. Journal of Manufacturing Systems, 56, 133–156.
https://doi.org/10.1016/J.JMSY.2020.05.016
- Buncefield Major Incident Investigation Board (BMIIB) (2005). The Buncefield Incident: The Final Report of the Major Incident Investigation Board. HSE Books.
www.Buncefieldinvestigation.Gov.Uk.
- Ikwan, F. (2018). Reducing energy losses and alleviating risk in petroleum engineering using decision making and alarm systems. J. Comput. Syst. Eng, 422–429.
- Omoarebun, P., Sanders, D., Ikwan, F., Hassan, M., Haddad, M., Thabet, M., Piner, J. and Shah, A., 2020. Intelligent Monitoring Using Hazard Identification Technique and Multi-Sensor Data Fusion for Crude Distillation Column. Advances in Intelligent Systems and Computing, 730-741.
- Omoarebun P., Sanders D., Ikwan F., Haddad M., Tewkesbury G., Hassan M. (2022) A Neuro-Fuzzy Model for Fault Detection, Prediction and Analysis for a Petroleum Refinery. In: Arai K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 296. Springer, Cham 866-876. https://doi.org/10.1007/978-3-030-82199-9_59
- Al-Fattah, S. M., & Startzman, R. A. (2001). Neural Network Approach Predicts US Natural Gas Production. Proceedings of SPE Production and Operations Symposium.
https://doi.org/10.2523/67260-MS
- Garcia, A., & Mohaghegh, S. D. (2004). Forecasting US Natural Gas Production into year 2020: a comparative study. All Days.
https://doi.org/10.2118/91413-MS.
- Fletcher, A., & Davis, J. P. (2002, 8 October). Decision-Making with Incomplete Evidence. All Days. https://doi.org/10.2118/77914-MS
- Balch, R. S., Hart, D. M., Weiss, W. W., & Broadhead, R. F. (2002, 13 April). Regional Data Analysis to Better Predict Drilling Success: Brushy Canyon Formation, Delaware Basin, New Mexico. All Days. https://doi.org/10.2118/75145-MS.
- Bhushan, V., & Hopkinson, S. C. (2002, 29 October). A Novel Approach to Identify Reservoir Analogues. All Days. https://doi.org/10.2118/78338-MS
- Finol, J., Romero, C., & Romero, P. (2002, September 29). An Intelligent Identification Method of Fuzzy Models and Its Applications to Inversion of NMR Logging Data. All Days.
https://doi.org/10.2118/77605-MS
- Alimonti, C., & Falcone, G. (2002, September 29). Knowledge Discovery in Databases and Multiphase Flow Metering: The Integration of Statistics, Data Mining, Neural Networks, Fuzzy Logic, and Ad Hoc Flow Measurements Towards Well Monitoring and Diagnosis. All Days. https://doi.org/10.2118/77407-MS
- Weiss, W. W., Balch, R. S., & Stubbs, B. A. (2002, 13 April). How Artificial Intelligence Methods Can Forecast Oil Production. All Days. https://doi.org/10.2118/75143-MS
- Ikwan, F., Sanders, D., & Hassan, M. (2021). Safety evaluation of leak in a storage tank using fault tree analysis and risk matrix analysis. Journal of Loss Prevention in the Process Industries, 73.
- Ikwan, F., Sanders, D., & Haddad, M. (2020). A Combined AHP-PROMETHEE Approach for Intelligent Risk Prediction of Leak in a Storage Tank. International Journal of Reliability, Risk and Safety: Theory and Application, 3(2), 55–61. http://www.ijrrs.com/article_119488.html
- Ikwan, F., Sanders, D., Haddad, M., Hassan, M., Omoarebun, P., Thabet, M., Tewkesbury, G., & Vuksanovic, B. (2021). Intelligent Risk Prediction of Storage Tank Leakage Using an Ishikawa Diagram with Probability and Impact Analysis. In Intelligent Systems and Applications. IntelliSys 2020 (Vol. 1252). Springer. https://doi.org/10.1007/978-3-030-55190-2_45
- Ahmadi, O., Mortazavi, S. B., & Mahabadi, H. A. (2020). Review of Atmospheric Storage Tank Fire Scenarios: Costs and Causes. Journal of Failure Analysis and Prevention, 20(2). https://doi.org/10.1007/s11668-020-00846-5.
- José, F.-B., González-Cruz, M., González-Gaya, C., & Baixauli-Pérez, M. (2017). Risk Analysis of a Fuel Storage Terminal Using HAZOP and FTA. International Journal of Environmental Research and Public Health, 14(7).
https://doi.org/10.3390/ijerph14070705
- Luo, T., Wu, C., & Duan, L. (2018). Fishbone diagram and risk matrix analysis method and its application in safety assessment of natural gas spherical tank. Journal of Cleaner Production, 174. https://doi.org/10.1016/j.jclepro.2017.10.334
- Wang, D., Zhang, P., & Chen, L. (2013). Fuzzy fault tree analysis for fire and explosion of crude oil tanks. Journal of Loss Prevention in the Process Industries, 26(6), 1390–1398. https://doi.org/10.1016/J.JLP.2013.08.022
- Buncefield Major Incident Investigation Board (BMIIB) (2005). The Buncefield Incident: the Final Report of the Major Incident Investigation Board . HSE Books.
www.Buncefieldinvestigation.Gov.Uk.
- Painting, A. (2014). Intelligent monitoring system to predict catastrophic incidents. University of Portsmouth.
- Charles, Z. (2021). Real Statistics Using Excel. Https://Www.Real-Statistics.Com/Time-Series-Analysis/Basic-Time-Series-Forecasting/Holt-Winters-Method/.
- Kadaifci, C., Asan, U., Serdarasan, S., & Arican, U. (2019). A new rule-based integrated decision making approach to container transshipment terminal selection. Maritime Policy & Management, 46(2).
https://doi.org/10.1080/03088839.2018.1489149.
- Jang, R., & Gulley, N. (1995). Matlab Fuzzy Logic Toolbox (Vol. 1). The MathWorks, Inc.
- Lyon, B. K., & Popov, G. (2020). Managing Risk Through Layers of Control. Professional Safety, 65(04), 25–35
- S. Chemical Safety and Hazard Investigation Board (USCSB). (2005). BP Texas City Refinery Explosion. https://www.csb.gov/bp-america-refinery-explosion/
- S. Chemical Safety and Hazard Investigation Board (USCSB). (2011). Caribbean Petroleum Corporation (CAPECO).
https://www.csb.gov/caribbean-petroleum-refining-tank-explosion-and-fire/