Evolving Principle Based Fuzzy Inherently Safer Design Index (FISDI) for ISD Assessment, Case Study for Acetic Acid Production Plant
AbstractInherently Safer Design (ISD) is served as an important and crucial step for Industrial Safety Management Systems. It is simpler, cheaper, and more efficient to eliminate and/or reduce inherent hazards. However, uncertainty, relativity, ambiguousness and quality/quantity transformations disrupt the implementation of ISD. As advantages of fuzzy reasoning, naming problems can be resolved in order to have a justified and sophisticated decision making about Inherently Safer Design Assessment. Accordingly in this paper, ISD four principles: 1.Elimination/Substitution, 2.Minimization, 3.Moderation and 4.Simplification enter the Fuzzy Mamdani system: Fuzzy ISD Index (FISDI) to accomplish Fuzzy Inherently Safer Design Assessment. Inputs and output of the FISDI range from 0 to 100 and are categorized in 5 triangular membership functions. The proposed FISDI is applied for acetic acid production unit. The unit is divided into 7 zones, the 4 principle based checklist is provided for each zone and the FISDI is computed for each zone, then the total FISDI is computed for the unit. The results show that the minimum, maximum and total FISDIs equal to 29, 72 and 45.1 correspondingly. The total plant FISDI data is compared to the classic ISDI. The cross validation accomplished via CFtool in MatLab presents the mean slope of 0.7181 and mean R2=0.7885 which is a justified curve fitting within the scope of the study philosophy_70% of the ISD. The FISDI mainly underestimates the aggregative ISDI. It is noted that the most conformed and the least conformed zone cross validations are determined as Zone 4 and Zone 7 respectively.
Kletz TA, What You Don't Have, Can't Leak', Chem Ind 1978; 287–292.
Kletz TA, Learning from Accidents, 3rd ed. Butterworth-Heinemann, Oxford, ISBN 0-7506-0692-2, 2001, p. 129
Kletz TA, Plant Design for Safety – A User-Friendly Approach, Hemisphere, New York, USA, 1991.
Khan FI, Amyotte PR, How to make inherent safety practice a reality. Can J Chem Eng 2003; 81: 2–16. http://dx.doi.org/10.1002/cjce.5450810101
Center for Chemical Process Safety, Inherently Safer Chemical Processes: A Life Cycle Approach. Wiley, 2010. New York.
Center for Chemical Process Safety, Guidelines for Hazard Evaluation Procedures, 3rd ed. John Wiley & Sons, 2011. New York.
Abidin MZ, Rusli R, Azmi MS, Khan FI, Three-Stage ISD Matrix (TIM) Tool to Review the Impact of Inherently Safer Design Implementation, Process Saf Environ Prot 2016; 99: 30–42. http://dx.doi.org/10.1016/j.psep.2015.10.006
Luyben WL, Hendershot DC, Dynamic disadvantages of intensification in inherently safer process design. Ind Eng Chem Res 2004; 43: 384–396. http://dx.doi.org/10.1021/ ie030266p
Hendershot DC, Inherently safer design: An overview of key elements. Prof Saf 2011; 48–54.
Hendershot DC, An overview of inherently safer design. Process Saf Prog 2006; 25, 98–107. http://dx.doi.org/10.1002/prs.10121
Hendershot DC, Conflicts and decisions in the search for inherently safer process options. Process Saf Prog 1995; 14: 52–56. http://dx.doi.org/10.1002/prs.680140109
Rathnayaka S, Khan F, Amyotte P, Risk-based process plant design considering inherent safety, Saf Sci 2014; 70: 438-464.
Shariff AM, Leong CT, Zaini D, Using process stream index (PSI) to assess inherent safety level during preliminary design stage, Saf Sci 2012; 50: 1098-1103.
Moore DA, Hazzan M, Rose M, Heller D, Hendershot DC, Dowell AM, Advances in inherent safety guidance. Process Saf Prog 2008; 27: 115–120.
Rusli R, Shariff AM, Qualitative Assessment for Inherently Safer Design (QAISD) at preliminary design stage. J Loss Prevent Proc 2010; 23: 157–165.
Ross T, Fuzzy Logic with Engineering Applications. John Wiley & Sons. 2004.
Zadeh L A, Fuzzy sets. Information Control 1967; 8: 338-353.
Mo-Yuen C, Methodologies of Using Neural Network and Fuzzy Logic Technologies for Motor Incipient Fault Detection. World Scientific, Singapore, 1997.
Zadeh LA, Fuzzy sets as a basis for theory of possibility. Fuzzy Sets and Systems 1978; 1: 3-28.
Ocampo W, Ferré N Domingo J, Schuhmacher M, Assessing water quality in rivers with fuzzy inference systems: a case study. Environ Int 2006; 32: 733-742.
Soler V, Lógica difusa aplicada a conjuntos imbalanceados: aplicación a la detección del síndrome de Down. PhD thesis, Departament de Microelectrònica i Sistemes Electrònics, Universitat Autònoma de Barcelona, 2007.
Sarkheil H, Rahbari S, Development of case historical logical air quality indices via fuzzy mathematics (Mamdani and Takagi–Sugeno systems), a case study for Shahre Rey Town. Environ Earth Sci 2016; 75: 1-13. doi:10.1007/s12665-016-6131-2
Gentile M, Rogers WJ, Mannan MS, Development of a Fuzzy Logic-Based Inherent Safety Index. Process Saf Environ Prot 2003; 81(6):444-456. http://dx.doi.org/10.1205/095758203770866610
Wang Q, Wang H, Qi Z, An application of nonlinear fuzzy analytic hierarchy process in safety evaluation of coal mine, Saf Sci 2016; 86: 78-87.