Xx

Circulation loop

Probes:

Temp.

Level

_ DO probe

Controller

Mass flow meter

Load cells

Process tank (80 hi operating volume)

Fig. 6.29 Schematic diagram of the equipment used for oxygenating yeast at production scale.

minimum pH. As with the laboratory trials discussed previously, the process was accompanied by ethanol formation and glycogen dissimilation. The latter was associated with a concomitant fall in cell dry weight. Yeast viability did not decrease to any significant degree during 6 hours' oxygenation.

At production scale, it was demonstrated that the use of oxygenated yeast produced more consistent attenuation profiles compared with conventional wort oxygenated fermentations using similar yeast and wort (Figs 6.32(a) and (b)). The improved consistency of attenuation profile observed for yeast oxygenated fermentations was accompanied by more consistent patterns of yeast growth extent and concentrations of beer esters and higher alcohols. In addition, there was an overall improvement in fermentation efficiency. Thus, yeast growth was reduced by approximately 20% compared to average values for wort oxygenated control fermentations. Surprisingly, concentrations of trial beer esters were not significantly different to those of the controls (Figs 6.33 and 6.34).

Masschelein et al. (1995) described a yeast oxygenation system in which yeast slurry

Fig. 6.30 Yeast oxygenation plant (courtesy of Bass Brewers Limited).

was circulated through an external loop attached to a storage vessel. Within the loop were cylindrical elements consisting of silicone carbide with aluminium oxide membranes of the type that the same author has suggested elsewhere might be applied to fermentations using immobilised yeast (see Section 5.7). In this application, the yeast is circulated through channels in the silicone carbide elements whilst the oxygen is forced through the silicone carbide matrix. The advantage of this system was that the enclosed design allowed high rates of oxygen transfer to be achieved without fobbing. Results were presented showing similar relationships between sterol synthesis, glycogen dissimilation and oxygen consumption rates as shown in Fig. 6.23.

6.4.2.3 Interactive control regimes. Interactive control regimes involve a combination of automatic monitoring of suitable parameter(s) and in response, corrective action(s) to ensure that the process proceeds according to a pre-determined path. A requirement of such systems, therefore, is availability of suitable sensors, knowledge of how changes in measured parameters relate to fermentation progress and beer quality and a means of applying corrective actions should deviations occur.

The means by which fermentation progress may be monitored in-line are described in Section 6.3. It is possible to monitor parameters such as oxygen uptake rate, carbon dioxide evolution, ethanol production, decline in gravity and rate of exothermy. The profiles obtained may be compared with a pre-determined ideal and corrections made by automatic adjustment of temperature. Undoubtedly such methods would lead to improvements in consistency of fermentation performance. However, such simple approaches are likely to be at best only partial solutions. Thus, there remains insufficient understanding of how changes in parameters that can be measured during

Oxygenation time (h)

Oxygenation time (h)

Oxygenation time (h) (b)

Oxygenation time (h) (c)

Fig. 6.31 Biochemical changes associated with oxygenation of lager yeast slurry (45% wet weight to volume) suspended in beer, using the equipment shown in Fig. 6.27. During oxygenation the yeast was maintained at a temperature of 25°C. Maltose (5% gT1 final concentration) was added 30 minutes after the commencement of oxygenation.

Oxygenation time (h) (c)

Fig. 6.31 Biochemical changes associated with oxygenation of lager yeast slurry (45% wet weight to volume) suspended in beer, using the equipment shown in Fig. 6.27. During oxygenation the yeast was maintained at a temperature of 25°C. Maltose (5% gT1 final concentration) was added 30 minutes after the commencement of oxygenation.

early fermentation, relate to the timing of the end-point of fermentation and beer quality.

A development which perhaps offers a way forward is the use of software models of brewery fermentations. Thus, the 'number-crunching' ability of modern computers offers the potential of identifying hitherto obscured patterns and relationships between control parameters and fermentation performance. Two general approaches have been proposed: those based on self-teaching systems such as neural networks and those that use simulation models.

The concept of artificial neural networks was developed by McCulloch and Pitts (1943) to simulate the function of human neurons. The premise is that multiple data inputs are fed into a computational system that uses the received information to generate a predictive model. The software does not contain a mathematical model of

g 30 20 10

Fig. 6.32 Attenuation profiles of (a) control (wort oxygenated) and (b) trial (yeast oxygenated) 1600 hi high-gravity lager fermentations (re-drawn from Boulton et al., 1991).

the system under consideration in which real data are used as the basis of calculation. Instead, the input data are used in a 'learning by experience' process in a way analogous to the development of neuron interconnections in the human brain. All data inputs are assigned relative discrete weighting. These weighted data are combined in the network and used to form the basis of decisions.

For application to the study of biological systems, a back-propagation neural network system is often used. This is an example of a neural network with supervised learning, in which a desired output is compared with actual output and the weights between the individual elements of the network updated accordingly. Each network consists of a number of layers, each made up of a number of individual processing elements. In each case, there is an input layer, an output layer and any number of intervening hidden layers. Thus, in terms of a brewing fermentation the input layer could receive information such as pitching rate, aspects of wort composition, dissolved oxygen tension, etc. The output layer could be a time-based parameter such as the rate of formation of ethanol, C02 or yeast biomass. Each processing element

trials

Controls

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