Stochastic simulation of supercritical fluid extraction processes

Mizutani, F. T.; Costa, André L.H.; Pessoa, Fernando

doi:10.1590/s0104-66322000000300006

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…The sources of the reference data classified as discussed above were: analytical methods [6-9,34-38, ; extraction [2,[4][5][10][11]18,19,21,22,[24][25][26][27][28][29][31][32][33][39][40][41]45,50,52,53,55,[57][58][59]61,62,66,; thermodynamics and fundamental studies [3,[12][13][14][15][16][17]20,23,30,[42][43][44][46][47][48][49]51,54,56,60,[63][64][166][167][168]…”

Section: Historical Factsmentioning

confidence: 99%

Supercritical technology in Brazil: system investigated (1994–2003)

Rosa

Meireles

2005

The Journal of Supercritical Fluids

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Section: Historical Factsmentioning

confidence: 99%

Supercritical technology in Brazil: system investigated (1994–2003)

Rosa

Meireles

2005

The Journal of Supercritical Fluids

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“…A density function is desirable to describe uncertainty statistics. [4,5] Stochastic methods have been proposed to estimate and minimize these uncertainties. A widely used method is the Monte Carlo (MC) method.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Analysis Applied to the Calculation of the Bubble Point Temperature of Mixtures

Santana¹,

Calixto²,

Pessoa³

2021

Blucher Engineering Proceedings

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The article presents the calculation of the bubble point of a mixture of nbutane, iso-butane, n-pentane and isopentane from the bottom of a distillation tower. This work aims to calculate the bubble point of a mixture using the Monte Carlo (MC) method. The calculation was performed under uncertainty in the Antoine parameters, which were obtained in the literature and optimized using Excel's Solver genetic algorithm and Stochastic simulation through MC method. In this way, the result is not a point temperature, but a range of probable values for the temperature of the bubble. This method proved to be promising and means more accurate process calculations, generating energy savings in industrial units.

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“…Uncertainty analysis has been employed in technical and economic assessments of emerging technologies for electricity generation , and other chemical processes . In these new technologies, the phenomenological representations or models of the process involve several parameters whose values are still part of experimental research (involving high degrees of uncertainty) or simply unknown; these parameters are considered as the uncertain variables for the stochastic simulation.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Simulation of Pulverized Coal (PC) Processes

2010

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An increasing population and electricity demand in the U.S. require capacity expansion of power systems. The National Energy Technology Laboratory (NETL), U.S. Department of Energy (DOE), has invested considerable efforts on research and development to improve the design and simulation of these power plants. Incorporation of novel process synthesis techniques and realistic simulation methodologies yield optimal flowsheet configurations and accurate estimation of their performance parameters. To provide a better estimation of such performance indicators, simulation models should predict the process behavior based on not only deterministic values of well-known input parameters but also uncertain variables associated with simulation assumptions. In this work, the stochastic simulation of a load-following pulverized coal (PC) power plant takes into account the variation of three input variables, namely, atmospheric air temperature, atmospheric air humidity, and generation load. These uncertain variables are characterized with probability density functions (pdfs) obtained from available atmospheric and electrical energy generation data. The stochastic simulation is carried out by obtaining a sample of values from the pdfs that generates a set of scenarios under which the model is run. An efficient sampling technique [Hammersley sequence sampling (HSS)] guarantees a set of scenarios uniformly distributed throughout the uncertain variable range. Then, each model run generates results on performance parameters as cycle efficiency, carbon emissions, sulfur emissions, and water consumption that are statistically analyzed after all runs are completed. Among these parameters, water consumption is of importance because an increasing demand has been observed mostly in arid regions of the country and, therefore, constrains the operability of the processes. This water consumption is significantly affected by atmospheric uncertainties. The original deterministic process model simulation was designed in Aspen Plus, and a CAPE-OPEN compliant stochastic simulation capability is employed to run the uncertainty analysis. Initially, the influences of atmospheric conditions and load change on the performance parameters are analyzed separately to understand their individual influences on the process, and then their simultaneous variation is analyzed to generate more realistic estimations of the process performance.

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Stochastic simulation of supercritical fluid extraction processes

Cited by 4 publications

References 3 publications

Supercritical technology in Brazil: system investigated (1994–2003)

Supercritical technology in Brazil: system investigated (1994–2003)

Uncertainty Analysis Applied to the Calculation of the Bubble Point Temperature of Mixtures

Stochastic Simulation of Pulverized Coal (PC) Processes

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