An alternative to Monte Carlo simulation method

Gadallah, Mohamed H.

doi:10.1504/ijedpo.2011.040261

Cited by 3 publications

(2 citation statements)

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“…For the purpose of experimentation, a full factorial experimental plan (3 3 ) L27 OA was adopted where the considered parameters were varied at three levels as given in Table 1. This design had 27 rows corresponding to the number of tests (26 degrees of freedom) with 13 columns at three levels [20,21]. Laser power, assist gas pressure and cutting speed were assigned to columns 1, 2, and 5, respectively.…”

Section: Methodsmentioning

confidence: 99%

Mathematical Modelling of the Co2 Laser Cutting Process Using Genetic Programming

et al. 2022

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The development of mathematical models by using experimental data is of great importance for modelling and optimization of the laser cutting process. Motivated by the lack of research regarding the use of genetic programming (GP) for deriving empirical mathematical models that describe the laser cutting process, the present study discusses the application of GP to the development of a kerf taper angle mathematical model. The aim was to quantify the relationship between three selected input parameters (cutting speed, laser power and assist gas pressure) and kerf taper angle using GP in the CO2 laser cutting of aluminium alloy AlMg3. To obtain the experimental database for the GP model evolution process, a laser cutting experiment was planned as per standard full factorial design where all three selected parameters were varied at three levels. The fit between the experimental and the GP model prediction values of kerf taper angle was found to be appropriate. Finally, by using the derived GP mathematical model, the analysis of the effects of input parameters on the change in kerf taper angle values was performed by generating 3D surface plots.

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Section: Methodsmentioning

confidence: 99%

Mathematical Modelling of the Co2 Laser Cutting Process Using Genetic Programming

et al. 2022

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“…One of the most common ways to conduct uncertainty analysis is through Monte Carlo simulations (Hsu, 2012) which randomly produce samples of the parameter to analyse the level of uncertainty in the results. Considering the probabilities on a subjective basis in the Monte Carlo simulations bring some doubt over suitability of the method (Gadallah 2011). Van Groenendaal and Kleijnen (1997) propose methods from design of experiments (DOE) as an alternative to identify the most influential factors on profitability of a project without considering probability distributions.…”

Section: Introductionmentioning

confidence: 99%

Combining Monte Carlo simulations and experimental design for incorporating risk and uncertainty in investment decisions for cleantech: a fast pyrolysis case study

Kuppens

Rafiaani

Vanreppelen

et al. 2018

Clean Techn Environ Policy

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The value of phytoextracting crops (plants cultivated for soil remediation) depends on the profitability of the sequential investment in a conversion technology aimed at the economic valorization of the plants. However, the net present value (NPV) of an investment in such an innovative technology is risky due to technical and economic uncertainties. Therefore, decision makers want to dispose of information about the probability of a positive NPV, the largest possible loss, and the crucial economic and technical parameters influencing the NPV. This paper maps the total uncertainty in the NPV of an investment in fast pyrolysis for the production of combined heat and power (CHP) from willow cultivated for phytoextraction in the Belgian Campine. The probability of a positive NPV has been calculated by performing Monte Carlo simulations. Information about possible losses has been provided by means of experimental design. Both methods are then combined in order to identify the key economic and technical parameters influencing the project's profitability. It appears that the case study has a chance of 87 % of generating a positive NPV with an expected value of 3 million euro (MEUR), whilst worst case scenarios predict possible losses of 7 MEUR. The amount of arable land, the biomass yield, the purchase price of the crop, the policy support and the product yield of fast pyrolysis are identified as the most influential parameters. It is concluded that both methods, i.e. Monte Carlo simulations and experimental design, provide decision makers with complementary information with regard to economic risk.

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