A Combined AHP-PROMETHEE Approach for Intelligent Risk Prediction of Leak in a Storage Tank

Document Type : Original Research Article

Authors

1 Systems Engineering Research Group, University of Portsmouth, United Kingdom

2 Systems Engineering Research Group Anglesea Building Anglesea Road, University of Portsmouth, United Kingdom

Abstract

This paper describes the use of the Analytic Hierarchy Process (AHP) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method for predicting the risk of leakage in a storage tank. This is the first time AHP and PROMETHEE have been used in this way. Important decisions about day to day operations are continually made in a petroleum environment. Storage tanks in refineries contain large volumes of flammable and hazardous liquids. Decision processes need to evaluate and select alternatives with a higher probability of resulting in a hazard, among many different alternatives. The new model described in this paper will aid decision-makers to predict which tank is likely to develop a leak and determine what criteria (source of risk) could result in a leak. Although the case study deals with a specific risk prediction problem, the combination of AHP and PROMETHEE methods can be applied to other decision problems.

Keywords

Main Subjects

References

  • James, C., and Cheng-Chung, L., “A study of storage tank accidents,” Journal of Loss Prevention in the Process Industries19(1): pp. 51-59, 2009.
  • Ikwan, F., “Reducing energy losses and alleviating risk in petroleum engineering using decision making and alarm systems” Journal of computing in systems and engineering, ISSN 1472-9083: pp. 422-429, 2018.
  • Saaty, T.L., “The analytic hierarchy process—what it is and how it is used,” Mathematical modelling, 9 (3-5): pp. 161-76 (1987).
  • Saaty, T.L., “How to make a decision: the analytic hierarchy process,” Interfaces, 24(6): pp.19-43,1994.
  • Huang, C., Tong, I., Chang, W. and Yeh, C., “A two-phase algorithm for product part change utilizing AHP and PSO,” Expert Systems with Applications: pp 38, 2011. 
  • Opricovic, S., Multicriteria optimization of civil engineering systems (in Serbian) Belgrade: PhD Thesis, Faculty of Civil Engineering: pp. 302, 1998. 
  • Roy, B., “Classement et choixenprésence de points de vue multiples,” RAIRO-Operations Research-Recherche Opérationnelle, (2): pp. 57–75,1968. 
  • Brans, J.P., “Lingenierie de la decision. Elaboration d’instrumentd’aide a la decision. Methode PROMETHEE. Colloqued’Aide a la Decision. Universite Laval, Quebec, Canada; pp.183-213, (1982).
  • Ying, L., Wei, W., BingXin, L. and Xin, Z., “Research on oil spill risk of port tank zone based on a fuzzy comprehensive evaluation,” Aquatic Procedia, (3): pp.216 – 223, 2015.
  • Bing, W., Hong, L. and Hong, Y., “Application of AHP, TOPSIS, and TFNs to plant selection for phytoremediation of petroleum-contaminated soils in shale gas and oil fields,” Journal of Cleaner Production: pp.13-22, 2019.
  • Brans, J. P., Vincke, P. H. and Mareschall, B., “How to select and how to rank projects: The PROMETHEE method,” European Journal of Operational Research, 14: pp. 228-238, 1986.
  • Saaty, T.L., “Decision making with the analytic hierarchy process,” International journal of services sciences, 1(1): pp.83-98, 2008.
  • Saaty, T.L. and Vargas, L.G., “Models, methods, concepts & applications of the analytic hierarchy process,” Springer Science & Business Media, 175(2): pp.1-342, 2012.
  • Ishizaka, A. and Labib, A., “Analytic hierarchy process and expert choice: Benefits and limitations” OR Insight, 22(4), pp.201-220, 2009.
  • Haddad, M. and Sanders, D., “Deep Learning Architecture to Assist with Steering a Powered Wheelchair,” IEEE Transactions on Neural System and Rehabilitation Engineering, Accepted and in Press, 2020.
  • Saaty, T.L., Fundamentals of Decision Making and Priority Theory with the Analytic Hierarchy Process, RWS Publications; 2000.
  • Brans, J.P and Vincke, P.H., “A preference ranking organization method: The PROMETHEE method,” Management Science, 31: pp. 647–65, 1985.
  • Macharis, C., and Springael, J., “PROMETHEE and AHP: The design of operational synergies in multicriteria analysis. Strengthening PROMETHEE with ideas of AHP,” European Journal of Operational Research,153: pp 307-317, 2004.
  • Kasım., B, Sari, T. and Koçdağ, V., “A combined AHP-PROMETHEE approach for project selection and a case study in the Turkish textile industry,” EEuropean Journal of Business and Social Sciences, 5(1): pp. 202 – 216,
  • Ikwan F. et al., Intelligent risk prediction of storage tank leakage using an Ishikawa diagram with probability and impact analysis. In: Arai K., Kapoor S., Bhatia R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, Springer, Cham; vol 1252, 2021.
  • Tongyuan, L., Chao, Wu. and Lixiang, D., “Fishbone diagram and risk matrix analysis method and its application in the safety assessment of natural gas spherical tank,” Journal of Cleaner Production, 174: pp. 296-304, 2017
  • José, L., Carmen, G-C., Cristina, G-G., and Piedad, B., “Risk analysis of a fuel storage terminal using HAZOP and FTA” International Journal of Environmental research and public health, 14(17): pp. 705, 2017.
  • Saaty, T.L., “Decision Making – The Analytical Hierarchy and Network Processes (AHP/ANP),” Journal of Systems Science and Systems Engineering, 13(1): pp. 1 – 35, 2004.
  • Wang, J.J. and Yang, D.L., “Using a hybrid multi-criteria decision aid method for information systems outsourcing,” Computers & Operation Research, 34(12): pp. 3691-3700,
  • Haddad, M. and Sanders, D., “Artificial Neural Network approach for business decision making applied to a corporate relocation problem”. Archives of Business Research,8(6): pp.180-195, 2020.
  • Omoarebun, P., “Disaster risk reduction in petroleum engineering”.  Journal of Computing in Systems Engineering.  ISSN 1472-9083. Pp. 499, 2018.
  • Omoarebun, P., Sanders, D., Haddad, M., Hassan, M., Tewkesbury, G., Giasin, K., “An intelligent monitoring system for crude oil distillation column”. 2020 IEEE. 10th International Conference on Intelligent Systems (IS). 159, 2020.
  • Omoarebun P. et al., Intelligent Monitoring Using Hazard Identification Technique and Multi-sensor Data Fusion for Crude Distillation Column, In Arai K., Kapoor S., Bhatia R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, Springer, Cham; vol 1252, 2021.

 

International Journal of Reliability, Risk and Safety: Theory and Application
Volume 3, Issue 2
December 2020
Pages 55-61

Files

History

  • Receive Date: 13 October 2020
  • Revise Date: 06 November 2020
  • Accept Date: 18 November 2020

Share

How to cite

Statistics