Ioan Cristian Trelea

Predictive modelling of brewing fermentation: from knowledge-based to black-box models

Advanced monitoring, fault detection, automatic control and optimisation of the beer fermentation process require on-line prediction and off-line simulation of key variables. Three dynamic models for the beer fermentation process are proposed and validated in laboratory scale: a model based on biological knowledge of the fermentation process, an empirical model based on the shape of the experimental curves and a black-box model based on an artificial neural network. The models take into account the fermentation temperature, the top pressure and the initial yeast concentration, and predict the wort density, the residual sugar concentration, the ethanol concentration, and the released CO2. The models were compared in terms of prediction accuracy, robustness and generalisation ability (interpolation and extrapolation), reliability of parameter identification and interpretation of the parameter values. Not surprisingly, in the case of a relatively limited experimental data (10 experiments in various operating conditions), models that include more process knowledge appear equally accurate but more reliable than the neural network. The achieved prediction accuracy was 5% for the released CO2 volume, 10% for the density and the ethanol concentration and 20% for the residual sugar concentration.

Dynamic optimisation of the aroma production in brewing fermentation

Several key compounds for the final beer flavour (higher alcohols, esters, vicinal diketones) are produced during the alcoholic fermentation phase. The paper demonstrates the possibility of obtaining various desired final aroma profiles and reducing the total process time using dynamic optimisation of three control variables: temperature, top pressure and initial yeast concentration in the fermentation tank. The optimisation is based on a sequential quadratic programming algorithm, on a dynamic model of the alcoholic fermentation and on an aroma production model. The robustness of the optimal control profile with respect to model uncertainty is discussed.

A lubrication analysis of pharyngeal peristalsis: application to flavour release.

After eating a liquid or a semi-liquid food product, a thin film responsible for the dynamic profile of aroma release coats the pharyngeal mucosa. The aim of this article was to analyse the fluid mechanics of pharyngeal peristalsis and to develop a simple biomechanical model in order to understand the role of saliva and food bolus viscosity on the coating of pharyngeal mucosa. We began by analysing the physiology and the biomechanics of swallowing in order to determine relevant model assumptions. This analysis of the literature clarified the types of mechanical solicitations applied on the food bolus. Moreover, we showed that the pharyngeal peristalsis in the most occluded region is equivalent to a forward roll coating process, the originality of which is lubrication by a film of saliva. A model based on the lubrication theory for Newtonian liquids was developed in dimensionless form. The parametric study showed the strong influence of relative saliva thickness on the food bolus coating. A specific experimental device was designed that confirms the model predictions. Two sets of conditions that depend on the relative thickness of saliva were distinguished. The first is characterised by a relatively thin film of saliva: food bolus viscosity has a strong impact on mucosa coating. These phenomena are well represented by the model developed here. The second is obtained when the saliva film is relatively thick: hydrodynamic mixing with saliva, interdiffusion or instabilities may govern mucosa coating. Finally, these results were extrapolated to determine the influence of food bolus viscosity on the dynamic profile of flavour release according to physiological parameters.

Optimization of Fermentation Operating Conditions with a View to Control Beer Aroma Profile

Abstract This article presents an interesting practical tool of determining the optimal T and P with respect to the beer aroma profile. On the basis of an appropriate experimentally validated model, an optimization procedure was elaborated leading to an efficient and reliable solution, oriented towards the industrial application. The applicability and the performances of the proposed tool are successfully tested on a realistic beer aroma profile.