Latin hypercube vs monte carlo4/10/2023 ![]() In Monte Carlo simulations, for a given input distribution, e.g. Furthermore, we used Monte Carlo sampling techniques to perform rigorous uncertainty analysis by propagating input uncertainties of parameters through the model output, which provides a degree of reliability of the model based analysis ( Sin et al., 2009, Laššák et al., 2010). In this study, Monte Carlo based methods were used to sample and evaluate different multiple deviations in process parameters simultaneously. Combining process simulation features with hazard identification techniques could provide means for investigating the consequences of deviations from normal operating conditions as well as other invaluable process safety information ( Janošovský et al., 2017). To give it a more quantitative character, it is possible to use steady-state analysis and dynamic simulations to complement the HAZOP study ( Labovský et al., 2007, Enemark-Rasmussen et al., 2012). In industrial process safety, Hazard and Operability study is traditionally used as a qualitative method to identify hazards and operational problems through the deviation effects of one design condition at a time. Gürkan Sin, in Computer Aided Chemical Engineering, 2017 1 Introduction This work demonstrates the applicability of simulation-based optimization for engineering designs under uncertainty. Stochastic Kriging approach showcases superior results when optimizing a two-column distillation loop of a DIPE + IPA + 2MEt system, showcasing three times lower cost evaluation compared to other methods while respecting propagation of uncertainty. Both proposed methods showcase superior results to a traditional SQP in a case study. The methodology is applied to two distillation flowsheets built in Aspen Plus. This work will also include an Artificial Neural Network alternative approach. The surrogate methods used are the developed Stochastic Kriging methods along with a novel infill criterion. The resulting information is then used in a surrogate assisted optimization approach designed specifically for stochastic simulations. Assuming that uncertainty behaves as random disturbances, Monte Carlo sampling techniques of model parameters uncertainty are used to quantify disturbances effects in model output. Successful implementation will give statistically strong optimum designs that do not require the use of safety factors that could compromise the cost competitiveness of the process design. The aim is to integrate uncertainty information of model parameters directly into optimization workflow. This study presents a methodology that combines Monte Carlo methods for uncertainty propagation along with modern stochastic programming methods for optimization of chemical engineering process models. Gürkan Sin, in Computer Aided Chemical Engineering, 2020 Abstract This works demonstrates that Monte Carlo methods are a simple and useful tool, which can be used in commercial process simulators by industrial users.Ītli Freyr Magnússon. The sensitivity analysis showed that the acentric factor is the most sensitive SRK parameter. The results showed that property uncertainty propagation strongly depends on the correlation between the property parameters. Furthermore, Monte Carlo based standard regression could be used to analyse the sensitivity of the respective fluid properties. This allowed describing the process model output uncertainty in a distribution and with the 95% confidence interval. ![]() The samples were subsequently evaluated in the heat pump flowsheet built in SimSci PRO/II. Monte Carlo sampling technique was used to generate property samples of the SRK equation of state parameters critical temperature, critical pressure and acentric factor. The property uncertainty and sensitivity analysis tools were applied to a heat pump system with cyclopentane as a working fluid. The aim of this work is to integrate advanced uncertainty and sensitivity analysis tools into commercial process simulators. This study presents a methodology to apply Monte Carlo methods for property uncertainty propagation in the process simulation software SimSci PRO/II. Gürkan Sin, in Computer Aided Chemical Engineering, 2018 Abstract
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