This paper deals with the introduction of the dynamics of some organoleptic compounds in the modelling of wine fermentation. The modelling proceeds in two steps: first the selection of the basic dynamical model (without organoleptic compounds) and the identification of its parameters, and the consideration of the five measured markers and their relation of the process variables. It is shown that those compounds that have been considered exhibits a strong relation with the CO2 production.

We show that a simple model with a maintenance term can satisfactorily reproduce the simulations of several existing models of wine fermentation from the literature, as well as experimental data. The maintenance describes a consumption of the nitrogen that is not entirely converted into biomass. We show also that considering a maintenance term in the model is equivalent to writing a model with a variable yield that can be estimated from data.

This paper presents the metabolic-fluxes calculation of a metabolic network representing the central metabolism of the yeast Saccharomyces cerevisiae in the wine-making context. Two solution methods are compared: a metabolic flux analysis (MFA) using convex analysis and providing narrow intervals of variation for the fluxes, and a flux balance analysis (FBA) based on an objective function. The constraints allowing the solution of the underdetermined set of algebraic equations are typically originating from measurements of uptake and secretion rates of external metabolites, and/or the use of an objective function, and/or metabolic constraints. It is shown here that converting reactions schemes in Cmol unit combined with data reconciliation provides a convenient formulation of closed carbon balance. In this form, the constraint formulation is natural and facilitates the understanding of the carbon distribution within the yeast metabolism.

Nitrogen has a strong impact on the key bio-mechanisms involved during the grape-must fermentation but also on the synthesis of flavour markers determining the aromatic profile of the wine. This paper first presents a consistent dynamical mass balance model describing the main physiological phenomena implied in standard batch fermentations, i.e. consumption of sugar and nitrogen and synthesis of ethanol. It also includes nitrogen compounds such as hexose transporters. Moreover, a common practice in wine-making is the addition of nitrogen during the fermentation in order to boost and shorten the process duration. A tractable representation of this boost effect has therefore been developed as an extension of the first model. It is apparent that yeast makes a different use of nitrogen depending on the fermentation stage at which the addition is effected, balancing the regrowth of biomass and the synthesis of supplementary hexose transporters. These models have been validated in line with experimental evidence deduced from extensive experimental studies.

The main physiological phenomena observed during the grape-must fermentation have been modelled based on a set of biological reactions in which nitrogen use is a major phenomenon. Moreover, a common practice in wine-making is the addition of nitrogen during the batch fermentation so as to boost and shorten the process duration. A tractable representation of this boost effect has therefore been developed and validated with experimental data.

The aromatic profile of young and white wines is mainly determined during the grape-must fermentation and is characterized by several compounds called flavour markers. These particular compounds are minority by-products produced from ``leaks of metabolism'' of the used yeast. The final objective of this work is to gain more insight about the synthesis of the aromatic profile in order to optimize it. For this purpose, a first necessary step is the development of a model representing the main physiological phenomena observed during the batch fermentation in the wine-making process in order to later extend it with flavour-markers equations. The main-kinetics model described in this paper is based on a set of biological reactions in which nitrogen compounds such as hexose transporters play a central role, in line with experimental evidence deduced from extensive experimental studies (Malherbe, 2003).

Abstract The aromatic profile of a wine is mainly determined during the grape-must fermentation and is characterized by several compounds called flavour markers. These particular compounds are minority by-products produced from “leaks of metabolism” of the used yeast. The final objective of this work is to gain more insight about the synthesis of the aromatic profile in order to optimize it. For this purpose, a first necessary step is the development of a model representing the main physiological phenomena observed during the batch fermentation in the wine-making process in order to later extend it with flavour-markers equations. The main-kinetics model described in this paper is based on a set of biological reactions in which nitrogen compounds such as hexose transporters play a central role, in line with experimental evidence deduced from extensive experimental studies (Malherbe, 2003).

In this article, two modelling approaches are proposed for winemaking fermentations. The first one is largely based on the first principle modelling approach and considers the main yeast physiological mechanisms. The model accurately predicts the fermentation kinetics of more than 80% of a large number of experiments performed with 20 wine yeast strains, 69 musts and different fermentation conditions. Thanks to the wide domain of validity of the model, a simulator based on this model coupled to a thermal model was developed to help winemakers to optimize tank management. It predicts the end of the fermentation and changes in the rate of fermentation but furthermore includes an optimization module based on fuzzy logic which allows, via temperature profiles and nitrogen addition strategies, to decrease the duration of fermentation and the energy requirements at winery scale according to user specifications. The objective of the second modelling approach is the development of a mathematical model of the fermentation process including some minority by-products known as characteristic flavour compounds. It refers to metabolic engineering and accounts for the intracellular behaviour of the yeast Saccharomyces cerevisiae by using approaches like the metabolic flux analysis (MFA) and the elementary flux modes (EFMs). A state of the art describes the application of these methods in the restrained field of winemaking/fermentation conditions and underlines the potential of such approaches.

This paper proposes a dynamical mass balance model describing the main physiological phenomena observed during the batch fermentation in the wine-making process, on the basis of a set of biological reactions in which the nitrogen consumption plays a central role, in line with experimental evidence deduced from extensive experimental studies (9). The experimental database considered for the parameters identification has been generated by a simulator issued from a logistic model especially dedicated to the wine fermentation (10) which is a valuable representation of the process, yet with a complex formulation that appears to be not suitable for control purposes. The paper presents the results of the modeling efforts performed on the basis of this model so as to include in particular the influence of nitrogen on the key bio-mechanisms involved during the fermentation.

In this paper, attention is focused on a parabolic partial differential equation (PDE) modeling sedimentation in a secondary settler and the proper formulation of the problem boundary conditions (i.e., the conditions prevailing at the feed, clear water and sludge outlets). The presence of a diffusion term in the equation not only allows the reproduction of experimental observations, as reported in a number of works, but also makes the numerical solution of the initial-boundary value problem significantly easier than the original conservation law (which is a nonlinear hyperbolic PDE problem requiring advanced numerical techniques). A Method of Lines (MOL) solution strategy is then proposed, based on the use of finite differences or spectral methods, and on readily available time integrators. The efficiency and flexibility of the general procedure are demonstrated with various numerical simulation results.

Mathematical modeling of sedimentation has attracted considerable attention in the past decades, and nowadays, one of the most popular models of secondary settler in activated sludge processes is the one proposed by Takács et al. [I. Takács, G.G. Patry, D. Nolasco, A dynamic model of the clarification-thickening process, Water Res. 25 (10) (1991) 1263–1271]. This model is based on a discretization in finite volumes (or layers) of the spatial domain, and in a rather inconsistent way, the number of layers is usually considered as a model parameter chosen so as to fit experimental data. In this study, a simple convection–diffusion partial differential equation (PDE) model is first formulated and solved using a Method of Lines strategy allowing the use of various spatial discretization methods with largely improved accuracy and efficiency. Model parameters are estimated using experimental data collected in batch settling experiments by De Clercq [J. De Clercq, Batch and continuous settling of activated sludge: in-depth monitoring and 1D compression modeling, Ph.D. Thesis, Universiteit Gent, Faculteit Ingenieurswetenschappen, Belgium, 2006], showing the good model predictive capability. Finally, the PDE settler model is coupled with a standard ASM1 representation of the activated sludge process, and implemented in a MATLAB dynamic simulator.

In this work, a robust control strategy is proposed for maintaining the oxygen concentration in the aerobic tank and the pollutant, i.e., ammonium, nitrate, nitrite, concentrations at acceptable levels in the effluent water at the outlet of the activated sludge process. To this end, the Activated Sludge Model no. 1 (ASM1) is first reduced using biological arguments and a singular perturbation method, and a simplified model of the secondary settler is included. In contrast with previous studies that make use of piecewise linear models, an average operating point is evaluated using available data (here data from the COST Action 624) and the reduced-order model is linearized around it using standard techniques. Finally, a H(2) robust control strategy acting on the oxygen injection and the recirculated flow rate is designed and tested in simulation.

The Activated Sludge Model no. 1 (ASM1) [6], which is the most widely accepted model of wastewater treatment plants (WWTPs), is used to describe the biotrans-formation processes of nitrification-denitrification. Due to its complexity and nonlinearity, this model is however not well-suited to the design of a control structure. The objective of this work is threefold: (1) to reduce the ASM1, (2) to linearize the reduced model, and (3) to design a robust control algorithm, in order to improve the plant performance and reject disturbances. This procedure is illustrated with data from the COST benchmark [7].

Secondary settlers, which ensure the separation of the activated sludge from the treated effluent, are described by distributed parameter models taking temporal and spatial variations into account. Over the years, a number of mathematical models have been proposed, including Kynch, Takacs and Hamilton models. The objective of this study is threefold: (a) to highlight the influence of the model formulation on the numerical solution procedure, (b) to propose a modified expression of the settling velocity, which avoids sharp spatial variations reported in the literature, and (c) to develop a numerical solution procedure following a method of lines strategy rather than the classical "tanks-in-series" approach.