A kinetic model for beer production under industrial operational conditions

Andrés-Toro, B. de; Girón-Sierra, José M.; López-Orozco, J. A.; Fernández-Conde, C.; Peinado, J. M.; Garcı́a-Ochoa, Félix

doi:10.1016/s0378-4754(98)00147-5

Cited by 65 publications

(72 citation statements)

References 5 publications

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“…Other models have also appeared since then. Garcia et al 9 present a fusel alcohol model, Garcia et al 10 developed a neural network model for ethyl caproate, de Andres-Toro et al 6 developed a three component biomass model that includes lag phase, active cell and dead cell components. Titica et al 22 present a flavor model based upon prior biological information and an analysis of experimental data, and Trelea et al 23,24 developed models based on biological knowledge, empirical data and artificial neural networks.…”

Section: Introductionmentioning

confidence: 99%

Optimal Beer Fermentation

Ramirez

Maciejowski²

2007

Journal of the Institute of Brewing

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A new tool was developed to solve a wide range of optimal beer fermentation problems. Using a mathematical model of beer fermentation, the direct dynamic optimization technique of sequential quadratic programming was investigated for determining the optimal cooling policy to maximize ethanol production for a fixed final time, including effects that optimize flavor. The new tool is very efficient for determining optimal cooling strategies. New control strategies were found that increase ethanol production while decreasing the deleterious effects of fusel alcohols and keeping the acetaldehyde concentrations at moderate levels. Model parameter sensitivity was investigated and it was shown that the system could be regulated successfully around optimal temperature profiles.

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Section: Introductionmentioning

confidence: 99%

Optimal Beer Fermentation

Ramirez

Maciejowski²

2007

Journal of the Institute of Brewing

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“…The kinetic model of beer fermentation by de Andrés-Toro et al (1998) has been selected for study due to its direct applicability to the industrial process:…”

Section: Model Selectionmentioning

confidence: 99%

“…Diacetyl (2,3-butanedione) has a pungent butter-like aroma (Izquierdo-Ferrero et al, 1997), while ethyl acetate is often used as an indicator of all esters present, and is described as having the odour of nail varnish remover (Hanke, S. et al, 2010). A more detailed description of the model can be found in its original publication (de Andrés-Toro, 1998), along with the constants for the Arrhenius relationship governing the parameters' temperature dependence, as computed from industrial scale fermentation data.…”

Section: Model Selectionmentioning

confidence: 99%

On the application of a nature-inspired stochastic evolutionary algorithm to constrained multi-objective beer fermentation optimisation

Rodman

Fraga

Gerogiorgis

2018

Computers & Chemical Engineering

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On the application of a nature-inspired stochastic evolutionary algorithm to constrained multiobjective beer fermentation optimisation General rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately and investigate your claim. AbstractFermentation is an essential step in beer brewing, often acting as the system bottleneck due to the time consuming nature of the process stage (duration >120hrs), where a trade-off exists between attainable ethanol concentration and required batch time. To explore this trade-off we employ a multi-objective Plant Propagation algorithm (the Strawberry algorithm), for identifying temperature manipulations for improved fermentation performance. The methodology employed successfully produces families of favourable temperature profiles which exist along the Pareto front. A subset of these output profiles can simultaneously reduce batch time and increase product ethanol concentration while satisfying constraints on by-products produced in the fermenters, representing significant improvements in comparison with current industrial practice. A potential batch time reduction of over 12 hours has been highlighted, coupled with a moderate improvement in ethanol content.

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“…Ethanol, yeast production and sugar consumption are related to CO 2 concentration with temperature dependent yield factors: three distinct model forms are considered, each with varying knowledge of the underlying biochemical phenomena. De Andrés-Toro et al (1998) proposed an alternative kinetic model for beer production under industrial operating conditions, relying on predicting yeast evolution in order to subsequently compute chemical species growth. The model is appropriate for study due to its parameter determination and model validation taking place on an industrial scale, its inclusion of flavour degrading compounds and the wide valid temperate range (8-24 ºC).…”

Section: Published Kinetic Modelsmentioning

confidence: 99%

Multi-objective Optimisation of Flavour and Processing Time in Beer Fermentation via Dynamic Simulation

Rodman

Gerogiorgis

2016

Computer Aided Chemical Engineering

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Fermentation is an essential step in beer brewing: when yeast is added to hopped wort, sugars ferment into ethanol and higher alcohols. Progression is highly sensitive to the temperature manipulation invoked, influencing batch time and product quality. A novel computational implementation of a published kinetic model has been produced, rapidly generating temperature manipulations and simulating the operation of each candidate profile. Ethanol and key harmful by-product (diacetyl, ethyl acetate) concentrations are monitored in order to minimize fermentation time while ensuring product quality is maintained. Visualisation of the entire operational envelope clearly illustrates Pareto fronts and trade-offs among these design objectives. Comparing these simulation results with those of an industrial operational profile reveals that batch time can be reduced by as much as 15 hours when an acceptable sacrifice is made to by-product concentrations.

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A kinetic model for beer production under industrial operational conditions

Cited by 65 publications

References 5 publications

Optimal Beer Fermentation

Optimal Beer Fermentation

On the application of a nature-inspired stochastic evolutionary algorithm to constrained multi-objective beer fermentation optimisation

Multi-objective Optimisation of Flavour and Processing Time in Beer Fermentation via Dynamic Simulation

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