Allgemeine Betriebswirtschaftslehre

Witte, Hermann

doi:10.1524/9783486593105

Cited by 15 publications

(2 citation statements)

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“…We fix α = 0.01 or 1%, and the corresponding z α/2 in the upper 99.5th percentile from the z-score table is 2.576. 53 For E r = 0.01 or 1%, the critical sample size is estimated to be 170. Furthermore, we perform a one-sample Kolmogorov− Smirnov (KS) test 53 to ensure that the drawn sample is representative of its population.…”

Section: ■ Mathematical Formulationmentioning

confidence: 99%

“…53 For E r = 0.01 or 1%, the critical sample size is estimated to be 170. Furthermore, we perform a one-sample Kolmogorov− Smirnov (KS) test 53 to ensure that the drawn sample is representative of its population. If the probability of the rarity of the result, p KS , is smaller than the considered confidence level (α KS = 0.05), then the sample is rejected and drawn again.…”

Section: ■ Mathematical Formulationmentioning

confidence: 99%

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Correlating Uncertainties of a CO₂ to CO Microfluidic Electrochemical Reactor: A Monte Carlo Simulation

Kannan

Raman

Fisher

et al. 2019

Ind. Eng. Chem. Res.

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The electrochemical conversion of CO 2 into useful chemicals in a microfluidic flow cell (MFC) reactor depends not only on intrinsic electrochemical, physical, and material parameters but also on extrinsic operating conditions and cell design. Variations in these parameters significantly affect the overall performance of the MFC reactor. In this regard, to correlate the cell performance, conversion efficiency, and selectivity of the MFC reactor with the variability of these input parameters, we carry out a Monte Carlo simulation (MCS) based on a mechanistic mathematical model for the electrochemical conversion of CO 2 to CO. The MCS is conducted in two scenarios: first, by varying the stochastic parameters individually (IND), and second, by varying all of the stochastic parameters simultaneously (SIM), at different cell potentials. These parameters are then ranked on the basis of their contributions to the cell performance, the conversion efficiency, and the selectivity, thereby providing insights into optimum ranges of operation. The charge-transfer coefficient toward CO and H 2 formation, catalyst properties, are the most sensitive parameters toward the cell performance and conversion efficiency and the selectivity, respectively, at all cell potentials. The thickness of the catalyst layer has a significant effect on the cell performance and conversion efficiency during the IND scenario, but its relative effect during the SIM scenario is not significant at all cell potentials. Furthermore, we derive reduced regression models based on supervised machine learning algorithms to predict the overall cell performance without having to solve the complete set of equations and also statistically discuss the distribution of overall cell performance at various cell potentials.

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Section: ■ Mathematical Formulationmentioning

confidence: 99%

Section: ■ Mathematical Formulationmentioning

confidence: 99%