Monte Carlo Simulation of Olefin Block Copolymers: Bivariate Distribution of Molecular Weight and Chemical Composition

2018 ◽  
Vol 377 (1) ◽  
pp. 1700060 ◽  
Author(s):  
Poramet Buakrong ◽  
Siripon Anantawaraskul ◽  
João B.P. Soares
Keyword(s):  
Molecular Weight ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Chemical Composition ◽  
Block Copolymers ◽  
Bivariate Distribution

scholarly journals Fuzzy-Based Multivariate Analysis for Input Modeling of Risk Assessment in Wind Farm Projects

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 325
Author(s):  
Emad Mohamed ◽  
Parinaz Jafari ◽  
Simaan AbouRizk
Keyword(s):  
Risk Assessment ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Wind Farm ◽  
Historical Data ◽  
Expert Knowledge ◽  
Bivariate Distribution ◽  
Expert Elicitation ◽  
Illustrative Case ◽  
Input Modeling

Currently, input modeling for Monte Carlo simulation (MSC) is performed either by fitting a probability distribution to historical data or using expert elicitation methods when historical data are limited. These approaches, however, are not suitable for wind farm construction, where—although lacking in historical data—large amounts of subjective knowledge describing the impacts of risk factors are available. Existing approaches are also limited by their inability to consider a risk factor’s impact on cost and schedule as dependent. This paper is proposing a methodology to enhance input modeling in Monte Carlo risk assessment of wind farm projects based on fuzzy set theory and multivariate modeling. In the proposed method, subjective expert knowledge is quantified using fuzzy logic and is used to determine the parameters of a marginal generalized Beta distribution. Then, the correlation between the cost and schedule impact is determined and fit jointly into a bivariate distribution using copulas. To evaluate the feasibility of the proposed methodology and to demonstrate its main features, the method was applied to an illustrative case study, and sensitivity analysis and face validation were used to evaluate the method. The results demonstrated that the proposed approach provides a reliable method for enhancing input modeling in Monte Carlo simulation (MCS).

scholarly journals Monte Carlo Simulation and Scenario Analysis Based Limestone Quarry Production Planning

2021 ◽  
Vol 12 (1) ◽  
pp. 41-45
Author(s):  
Kausar Sultan Shah ◽  
Safi Ur Rehman
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Chemical Composition ◽  
Production Planning ◽  
Scenario Analysis ◽  
Raw Materials ◽  
Raw Material ◽  
Mix Design ◽  
Efficient Planning ◽  
The Impact

The key factor behind raw mix design in the cement factory is the appropriate production planning, resulting in high-quality raw material. Quarry managers usually come up with uncertainty-related raw materials due to variations in chemical composition. These uncertainties required efficient planning in terms of useful insight into this problem. This research provides a detailed explanation of scenario analysis of raw materials used in cement manufacturing using Monte Carlo simulation (MCS) and indices. Scenario analysis is used to predict the possibility of best, worst and most likely cases of raw material’s quality. Whereas, Monte Carlo simulation is used to evaluate the inherent uncertainty associated with chemical composition values in order to analyze the impact of truly unpredictable scenarios. The predictive results help in decisions related to production planning, raw mix design optimization and increasing the probability of designing the best plan.

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