2nd Mercosur Congress on Chemical Engineering 4th Mercosur Congress on Process Systems Engineering
HYBRID MODELING AND NEURAL PREDICTION OF THE WILD KILLER YEAST FERMENTATION PERFORMANCE IN A WINEMAKING PROCESS Martha Dina Vallejo1, Pablo Marcelo Aballay 2, Maria Eugenia Toro1 Fabio Vazquez1, Graciela Ingrid Suarez2, and Oscar Alberto Ortiz2∗ 1 Instituto de Biotecnologia – Universidad Nacional de San Juan 2 Instituto de Ingenieria Química - Universidade Nacional de San Juan
Abstract. At present, due to the market demands, the wineries in the region of Cuyo (Argentina) produce wines from varietal musts using selected yeast strains. In order to produce fine wines with constant quality, some wineries use killer yeasts. In winemaking processes, alcoholic fermentation is a very important step, where many complex physical, chemical and biological changes occur, which are well known and modeled. Normally, such changes have a great influence on the final wine quality. Prediction on performance and risk on fermentation process allows taking early appropriate corrective actions. The numerous parameters and some biochemical and operating uncertainties difficult the use of available kinetic models, for monitoring, prediction and control of the alcoholic fermentations involving mixed killer Saccharomyces and nonSaccharomyces yeasts. This work describes a dynamic hybrid neural model that allows estimating sugar, viable cells and ethanol concentration profiles and predicts the ending fermentation time, besides the risks of sluggish and stuck fermentations. The model consists of a set of ordinary differential equations which calculate the mentioned profiles and an artificial neural network that estimate the final time and risk. The model kinetic parameters were adjusted from experimental data obtained from anaerobic lab-scale cultures with Syrah varietal must, inoculated with S. cerevisiae (killer), and/or Candida cantarelli yeasts. The obtained results with the model proposed acceptably predict the fermentation performance. In a next contribution, the developing of an advanced control algorithm will be addressed. Keywords: Hybrid model, neural network, wine fermentation.
1. Introduction On this year, the grape production in the province of San Juan (Argentina) would reach around the 700000 metric tons and, a large amount will be used for winemaking. In the regional wine industry there are many types of fermenters (bioreactors), which generally are operated on batch mode, and some on fed-batch. Control strategies are diverse and semi-automated control systems coexist with manual-mode controls. In order to improve the operation performance, in most cases, a control technology must be incorporated. On the other hand, the present market-demands address winemaking activities to produce fine wines with constant quality. Accordingly, varietal musts and selected yeast starters are used. Some of these selected strains possess killer properties, which in this case represent a guarantee for their implantation in the fermentation (Zagorc et al., 2001). The alcoholic fermentation constitutes the core of winemaking processes. Since it is operated on batch mode, yeasts must get adapted to highly variable environmental conditions, which greatly difficult the kinetic predictions of the bioprocess. Numerous and complex physical, chemical and biological changes occur at this stage and fix on the final wine quality; much of them have been largely studied (Boulton et al., 1996; Cramer et ∗
To whom all correspondence should be addressed. Address: Instituto de Ingenieria Química – Universidad Nacional de San Juan – Lib. San Martin 1109 oeste (5400) – San Juan – Argentina. E-mail:
[email protected]
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2nd Mercosur Congress on Chemical Engineering 4th Mercosur Congress on Process Systems Engineering al., 2002, Rosenfeld et al., 2003). However, only two mathematical descriptions on population dynamics with killer yeasts have been reported (Ramón-Portugal et al., 1997; Pommier et al., 2004). From the biologic and economic points of view there are two serious problems in the industrial wine production: the sluggish fermentations, which delay the ending point, and the stuck fermentations, which are completely stopped before sugar is depleted (Fleet, 1993). The knowledge about of fermentation performance and stuck risks are helpful tools for enological practice, because they allow taking early proper corrective actions. Numerous parameters and some biochemical aspects related to environmental conditions, become practically impossible the application of available kinetic models to the alcoholic fermentation supervision, prediction and control. Also, the presence of killer Saccharomyces and non-Saccharomyces yeasts hinder even more the process control actions. However, hybrid models, that combine the classical approaches with artificial intelligence, seem to be the most appropriated to model such biological systems (Schubert et al., 1994; Feyo de Azevedo et al., 1997; Thibault et al., 2000). Insa et al. (1994) has combined data analysis and neural modelling, studying different musts, and avoiding the yeasts population dynamics in order to predict sluggish and stuck fermentations. On the other hand, a dynamic stoichiometric model useful for simulating the anaerobic yeast metabolism in wine fermentation was developed by Sainz et al (2003), based on the metabolic pathway and the metabolic flux distribution according with the environment changes. The purpose of this work is to provide a model that allows simulating the alcoholic fermentation progress during Syrah grape must winemaking, in pure and mixed batch cultures of killer Saccharomyces cereviciae and Candida cantarellii yeasts. Therefore, a hybrid model based on the first principles and an artificial neural network has been proposed. Such tool permits to relate metabolite accumulation in medium and the obtained process kinetic information (carbon dioxide production rate) with fermentation performance. The proposed model will be utilized in the process prediction and control. The achieved results about the process performance will be used to characterize the fermentation type as standard or risky (sluggish or stuck). The paper is organized as follow. First, the modeling of bioprocess which predicts sugar, viable cells and ethanol concentration profiles is presented. Second, an artificial neural network that estimate the final time and risk is developed. Third, the experimental data from laboratory tests and, the simulation results are presented. Four, discussion about the capability of the model to predict the final time and risk and conclusions are exposed.
2. Modeling The yeasts population growth in anaerobic conditions can be characterized by the following metabolic reductive pathway: S → X +C + P
where S corresponds to glucose; X, is biomass; C, is carbon dioxide; and P, is ethanol.
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2nd Mercosur Congress on Chemical Engineering 4th Mercosur Congress on Process Systems Engineering The ethanol formation reaction from glucose is as follows: C 6 O6 H 12 → 2CH 3CH 2 OH + 2CO2
Metabolite accumulation in extracellular medium, was modeled with a set of ordinary differential equations (ODE), based on mass balance for yeasts, carbon source substrate (considered at first as the only limiting substrate, thus, the nitrogen source balance can be neglected at this instance), and ethanol (the principal product, which then can be related more or less easily with another important products at this stage, as the glycerol produced by yeasts). Viable cells balance: dX = rX − rD dt
(1)
Substrate Balance: dS = rS dt
(2)
Product Balance: dP = rP dt
(3)
Cellular growth rate: B
⎛ P ⎞ ⎟ X rX = µ ⎜⎜1 − PM ⎟⎠ ⎝
(4)
Cellular death rate: ⎛ P ⎞ 1 ⎟ X rD = µ D ⎜⎜1 + PM ⎟⎠ K D ⎝
(5)
Monod expression:
µ = µm
S KS + S
(6)
The relationship of substrate consumption with yield and maintenance: rS = −
rX − mS X YX / S
(7) 3
2nd Mercosur Congress on Chemical Engineering 4th Mercosur Congress on Process Systems Engineering
Product formation rate: rP = (K1 + K 2 µ )X
(8)
In these equations, X, S and P correspond to viable cells, carbon source (glucose and fructose) and ethanol concentrations (Kg m-3), respectively; rX is the cellular population growth rate (Kg m-3 h-1), rD is the cellular death rate (Kg m-3 h-1), µ is the specific cellular growth rate (h-1), µD is the specific cellular death rate(h-1). KS is the substrate saturation coefficient in the Monod equation (Kg m-3), YX/S is the cellular yield coefficient (Kg.Kg1
), mS (Kg Kg-1 h-1) is the cellular maintenance coefficient and, K1 (Kg Kg-1 h-1) and K2 (Kg Kg-1) are the
coefficients for products formation in the Luedeking and Piret equation. Model parameter values were fitted from experimental data by standard regression methods. Mechanisms underlying ethanol tolerance and killer activity are not completely explained, neither mathematically described in winemaking conditions; but it is known that kinetic parameters are complex functions of various variables and that they can change in the presence of some products. The present model includes KD, B and PM as parameters that reflect the changes in the viability of the culture. KD account for growth limitations due to nutritional deficiencies (like nitrogen and vitamins) and some inhibition caused by the presence of toxic compounds or stressing conditions., like the killer phenomenon. This latter largely depends on: the amount of killer toxin, the sensitivity of the sensible yeasts and, the medium composition. PM is related to the ethanol-tolerance (high for Saccaromyces and low for Candida); and B is associated with the presence of “survival factors” (like sterols, unsaturated fatty acids). 2.1. Artificial neural network (ANN) architecture and training. There are many types of architectures for neural networks, but the feed-forward multilayered backpropagation network is the most widely one applied in modeling of a wide range of non-linear relationships and is specially suitable for chemical processes (Linko and Zhu, 1992). Figure 1 shows the artificial neural network architecture used. t rCO2 (t)
final time
X(t-1) risk P(t-1)
S(t)/S0
Fig. 1. The artificial neural network architecture implemented.
In this work, the ANN has five inputs: time (t) in hours, carbon dioxide production rate (rCO2) in kg m-3 h-1, viable cells concentration (X) in kg m-3, ethanol concentration (P) in kg m-3 and the normalized substrate 4
2nd Mercosur Congress on Chemical Engineering 4th Mercosur Congress on Process Systems Engineering concentration (S/S0). The hidden layer has the optimal number of four neurons which minimizes the mean square error (mse) between real outputs and the predicted ones by the ANN after training. In this layer the transfer function used is a log-sigmoid function, because its use is common and in part is differentiable. Finally, the output layer has two neurons with a linear transfer function type; the outputs are: final time (tf) in hours, and risk, which is based on the maximum carbon dioxide production rate and it is classified as follows in Table 1. Table 1. Fermentation risk classification.
Fermentation type rCO2, max -3
Risk
-1
[kg m d ] Normal
15 – 50
0.1
Sluggish
1 – 15
0.5
Stuck
