Dynamic models for start-up operations of batch distillation columns with experimental validation

Elgue, Sébastien; Prat, Laurent E.; Cabassud, Michel; Lann, J.M. Le; Cezerac, J.

doi:10.1016/j.compchemeng.2004.07.033

Cited by 24 publications

(12 citation statements)

References 11 publications

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“…The model also appears particularly suitable to represent the start-up of the different steps [10]. Such accuracy is very important from an optimisation viewpoint as start-up time may represent a significant part of the overall operating time.…”

Section: Modelling Frameworkmentioning

confidence: 98%

Optimisation of solvent replacement procedures according to economic and environmental criteria

Elgue

Prat

Cabassud

et al. 2006

Chemical Engineering Journal

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Section: Modelling Frameworkmentioning

confidence: 98%

Optimisation of solvent replacement procedures according to economic and environmental criteria

Elgue

Prat

Cabassud

et al. 2006

Chemical Engineering Journal

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“…Reflux factor (R/Rmin) effects number of actual phase needed. By considering limitation of dimension to motorcycle, decided reflux factor equal to 4 so got acquisition 9 levels in column [9].…”

Section: Fig 1 Compact Distillator On Motorcyclementioning

confidence: 99%

The use of low grade bio ethanol as fuel mixer in gasoline engine with optimization compact distillator

Yamin¹,

Sugiarto²,

Abikusna³

et al. 2018

E3S Web Conf.

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Limited fossil energy resources need to encourage renewable energy development and energy conservation it is called green energy development. One of the alternative energy sources used are bioethanol, from low grade is converted into high grade through distillation process with distillator compact that will be used as a mixture of gasoline. Tests compact distillator consisting of evaporator Indonesia Kampus, separator, and condenser by utilizing exhaust gas to heat distillator already filled with low grade bioethanol, so the heat transfer occurs on the surface of the exhaust into the evaporator chamber. In the evaporator will transform ethanol into the vapor phase. Ethanol vapor flows into the separator, with a mechanism in it is expected that water vapor will evaporate participate separately with ethanol vapor. Ethanol vapors from this distillation will flow to the condenser and will be turned into liquid, the liquid is as high grade bioethanol would be the fuel mixing gasoline. In early studies, distillation rate was 98.5 ml/h with 67% ethanol content. Optimization compact distillator is done by adding the valve at the evaporator branch. When heat has reached 80°C exhaust gas flow to the evaporator chamber is closed. As a result, the heat from the evaporator can be detained and not rise significantly. Further optimization is to change the design of the separator through theoretical and empirical calculations with some assumptions based on the rule of thumb in the field of distillation, adding insulation to reduce heat leakage, as well as varying the feed volume and feed concentrate ethanol. In the end, based on the parameters of the distillation rate and ethanol concentrate, compact distillator with distances between tray 100 mm, volume 800 ml, engine speed 5.400 rpm can produce distillation rate of 274.3 ml/hr and ethanol concentrate of 88.97%.

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“…El modelaje para la simulación de columnas de destilación discontinua durante la operación de arranque es un problema muy difícil debido a la complejidad del comportamiento dinámico (Elgue et al, 2004). Solo unos pocos modelos rigurosos para las columnas de destilación durante la puesta en marcha se encuentran disponibles en la literatura y, en general, requiere una gran cantidad de parámetros relacionados con la geometría de los platos o el empaque.…”

Section: Estado Vacío (Ev) ---> Acumulación De Líquido (Al) ---> Equiunclassified

Redes neuronales de base radial como modelos dinámicos para la puesta en marcha de columnas de destilación por lotes

Sosa

Pérez

2017

Ingeniería

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Recibido: 3 de setiembre 2017Aceptado: 12 de octubre 2017 _____________________________________________________________________ ResumenEn la literatura existen pocos modelos rigurosos para describir el perfil de temperatura durante el periodo de puesta en marcha de las columnas de destilación. En este trabajo, se desarrolla un modelo empleando redes neurales de base radial con datos recolectados durante el período de puesta en marcha de una columna de destilación discontinua para la de mezcla etanol y agua. El entrenamiento de la eficacia de la red neuronal se obtiene realizando un pre-procesamiento de las entradas y un cambio de escala. Para obtener el perfil de temperatura, se recolectan datos a lo largo de diferentes puntos de la columna y los resultados se aplican a múltiples redes. Esto permite la construcción del perfil de temperatura en la columna hasta obtener un error cuadrático medio menor que los valores máximos establecidos durante el preprocesamiento (mse = 0,001) de las redes. Finalmente, se obtiene un modelo que permite observar la transición en la columna desde el estado frío vacío hasta el estado estacionario, normalmente un desafío en los modelos convencionales. Palabras clave:Destilación, redes neuronales artificiales, proceso de arranque, modelación. AbstractFew rigorous models exist in literature for describing the temperature profile during start-up periods of distillation columns. In this work, a model is developed by applying several radial basis neural networks to data collected during the start-up period of a batch distillation column consisting of ethanol and water. Neural network efficiency training is introduced through rescale

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Dynamic models for start-up operations of batch distillation columns with experimental validation

Cited by 24 publications

References 11 publications

Optimisation of solvent replacement procedures according to economic and environmental criteria

Optimisation of solvent replacement procedures according to economic and environmental criteria

The use of low grade bio ethanol as fuel mixer in gasoline engine with optimization compact distillator

Redes neuronales de base radial como modelos dinámicos para la puesta en marcha de columnas de destilación por lotes

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