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http://dx.doi.org/10.7842/kigas.2017.21.2.32

Advanced Optimization of Reliability Based on Cost Factor and Deploying On-Line Safety Instrumented System Supporting Tool  

Lulu, Addis (Department of Chemical Engineering, Myongji University)
Park, Myeongnam (Department of Chemical Engineering, Myongji University)
Kim, Hyunseung (Department of Chemical Engineering, Myongji University)
Shin, Dongil (Department of Chemical Engineering, Myongji University)
Publication Information
Journal of the Korean Institute of Gas / v.21, no.2, 2017 , pp. 32-40 More about this Journal
Abstract
Safety Instrumented Systems (SIS) have wide application area. They are of vital importance at process plants to detect the onset of hazardous events, for instance, a release of some hazardous material, and for mitigating their consequences to humans, material assets, and the environment. The integrated safety systems, where electrical, electronic, and/or programmable electronic (E/E/PE) devices interact with mechanical, pneumatic, and hydraulic systems are governed by international safety standards like IEC 61508. IEC 61508 organises its requirements according to a Safety Life Cycle (SLC). Fulfilling these requirements following the SLC can be complex without the aid of SIS supporting tools. This paper presents simple SIS support tool which can greatly help the user to implement the design phase of the safety lifecycle. This tool is modelled in the form of Android application which can be integrated with a Web-based data reading and modifying system. This tool can reduce the computation time spent on the design phase of the SLC and reduce the possible errors which can arise in the process. In addition, this paper presents an optimization approach to SISs based on cost measures. The multi-objective genetic algorithm has been used for the optimization to search for the best combinations of solutions without enumeration of all the solution space.
Keywords
Safety Life Cycle; Safety Instrumented Systems; Intelligent Support System; Multi-Objective Genetic Algorithm; Optimization;
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