Uncertainty quantification in dynamic system risk assessment: a new approach with randomness and fuzzy theory

Abstract

International audienceQuantifying uncertainty during risk analysis has become an important part of effective decision-making and health risk assessment. However, most risk assessment studies struggle with uncertainty analysis and yet uncertainty with respect to model parameter values is of primary importance. Capturing uncertainty in risk assessment is vital in order to perform a sound risk analysis. In this paper, an approach to uncertainty analysis based on the fuzzy set theory and the Monte Carlo simulation is proposed. The question then arises as to how these two modes of representation of uncertainty can be combined for the purpose of estimating risk. The proposed method is applied to a propylene oxide polymerisation reactor. It takes into account both stochastic and epistemic uncertainties in the risk calculation. This study explores areas where random and fuzzy logic models may be applied to improve risk assessment in industrial plants with a dynamic system (change over time). It discusses the methodology and the process involved when using random and fuzzy logic systems for risk management

Modeling of variability and uncertainty in human health risk assessment

“…fuzzy and random), for carrying out risk assessment. To address this issue different effort have been made by various researchers for joint propagation of variability and uncertainty in the same computation of risk viz., Flage et al [6] , discussed probabilistic and Possibilistic treatment of epistemic uncertainties, Dutta and Ali [7] studied fuzzy focal elements in Dempster-Shafer theory of evidence: case study in risk analysis, Ali et al [8] discussed modeling uncertainty in risk assessment using Double Monte Carlo method, Dutta and Ali [9] proposed a hybrid method to deal with aleatory and epistemic uncertainty in risk assessment, Pedroni et al [10] , [11] studied propagation of aleatory and epistemic uncertainties, Arunraj et al [12] proposed an integrated approach with fuzzy set theory and Monte Carlo simulation for uncertainty modeling in risk assessment, Pastoor et al [13] studied roadmap for human health risk assessment in 21st century, Farako et al [24] , [25] studied risk assessment for Salmonella in tree nuts, Salmonella in low-water activity foods and Salmonella in low-moisture foods, Zwietering [14] studied uncertainty modeling for risk assessment and risk management for safe foods, Rębiasz et al [15] studied joint Treatment of Imprecision and Randomness in the Appraisal of the Effectiveness and Risk of Investment Projects, Alyami et al [16] studied advanced uncertainty modeling for container port risk analysis, studied uncertainty handling in safety instrumented systems according to IEC and new proposal based on coupling Monte Carlo analysis and fuzzy sets, Abdo and Flaus [17] proposed a new approach with randomness and fuzzy theory for uncertainty quantification in dynamic system risk assessment, Zhang et al [18] discussed risk assessment of shallow groundwater contamination under irrigation and fertilization conditions. However, in all their efforts it is observed that representation of epistemic uncertainty is of Type-I fuzzy set.…”

Sensitivity analysis for justification of utilising special purpose machine tools in the presence of uncertain parameters

“…Rönnberg Sjödin, Frishammar, and Eriksson (2016) believed that uncertainty is one of the key challenges at the early stages of projects which can have large consequences in project performance. Furthermore, when the behaviour of a system is described by a mathematical model a poor decision may be made due to uncertainty can occur in the parameters of the model (Abdo and Flaus 2016). Accordingly, the economic model may not be sufficiently robust for the decision making process, thus a supplementary technique is needed with the cost model for investigating the inputs of the model under uncertainty.…”

A safety/security risk analysis approach of Industrial Control Systems: A cyber bowtie – combining new version of attack tree with bowtie analysis

“…(Abdo and Flaus, 2016b). Quantifying and analyzing these major risks contributes to better decision making and ensures that risks are managed according to defined acceptance criteria (Arunraj and Maiti, 2007).…”