an improved model for prediction of fermentation progress
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An improved model for prediction of
fermentation progress and total diacetyl
profile
Krogerus K., Gibson B., Hytönen E.
VTT Technical Research Centre of Finland
4th International Young Scientists Symposium on Malting,
Brewing and Distilling. Ghent, October 28th–30th 2014
2 23/10/2014 2
Introduction – What is diacetyl?
A vicinal diketone
Butter/butterscotch/toffee flavour
Produced during fermentation
Low flavour threshold (20-100 ppb)
Off-flavour
3 23/10/2014 3
Introduction
Diacetyl levels in green beer can influence Cost
Quality
Necessitates a costly removal step (lagering/maturation/secondary fermentation)
Net increase Net decrease
4 23/10/2014 4
Introduction
The formation and reduction of diacetyl is complex.
Affected by numerous factors: wort pH
fermentation temperature
wort gravity
free amino nitrogen
valine availability
pressure
dissolved oxygen
pitching rate
fermenter geometry
5 23/10/2014 5
Objectives
To assess the influence of various process
parameters on the levels of total diacetyl during
wort fermentation.
To mathematically model the development of total
diacetyl with respect to process conditions and
evaluate the relative importance of these
conditions.
6 23/10/2014 6
Approach
Predictive model
Yeast growth
Ethanol production
Diacetyl concentration
Wort pH
Kinetic model based on
biochemical pathways
User-friendly tool
Basic parameters included
Initial pH
Temperature profile
FAN content
Fermentable sugars• Maltose
• Glucose
PyruvateFermentation products• Ethanol
• Carbon dioxide
α-acetolactate
Diacetyl
Acetoin
Enzymatic
reaction
Enzymatic
reaction
Non-enzymatic
reaction
kS
kP
k1
k2
k3
ValineEnzymatic
reaction
kV
7 23/10/2014 7
Experimental design
Set 1 Set 2
pH 4.8 pH 5.1 pH 5.3
9 °C
12 °C
15 °C
FAN 222 FAN 252 FAN 287
9 °C
12 °C
15 °C
FAN 366 pH 4.8
8 23/10/2014 8
X (Biomass) S (Wort sugars)
DA (Diacetyl)
pH E (Ethanol)
9 23/10/2014 9
Fermentation model
0
1
2
3
4
5
6
0 24 48 72 96 120 144 168 192 216 240 264 288
Yea
st d
ry m
ass
(g/L
)
Fermentation time (h)
Measured (15C, pH 4.75, FAN 252 ppm)
Measured (12C, pH 5.3, FAN 366 ppm)
Measured (9C, pH 4.75, FAN 222 ppm)
Yeast growth based on classic Monod-type kinetics:
lagact
dead sed2 act act act
XX S E1X X X
ES E 11
max
Xsed
E
ddµ µ µ
ddt K dtK
dtK
10 23/10/2014 10
Diacetyl
The change of wort (total) diacetyl concentration during
fermentation is modelled as:
𝑑DA
𝑑𝑡= 𝑌𝐷𝐴/𝐸 ∙
𝑑E
𝑑𝑡− 𝑘𝐷𝐴/𝐴𝐿 ∙ DA
11 23/10/2014 11
Diacetyl removal
Reaction is non-enzymatic
Rate is dependent on the physical and chemical properties of
the wort:
Temperature
pH
Chemical concentrations
The rate of diacetyl removal (Kobayashi et al., 2005):
𝑘𝐷𝐴/𝐴𝐿 = 𝑘0 · (1 + 𝑎 · [EtOH]) · (10−pH + 𝑏) · 𝑒(−𝑐/𝑇)
12 23/10/2014 12
Effect of Temperature
A higher temperature results in both increased diacetyl
production and removal:
0
200
400
600
800
1000
1200
0 24 48 72 96 120 144 168 192 216 240 264 288
Dia
cety
l (p
pb
)
Fermentation time (h)
15C
12C
9C
13 23/10/2014 13
Effect of pH
A lower pH increases diacetyl removal rate
0
100
200
300
400
500
600
700
800
900
1000
24 48 72 96 120 144 168
Dia
cety
l (p
pb
)
Fermentation time (h)
Fermentation temperature: 15C
pH 4.8
pH 5.1
pH 5.3
14 23/10/2014 14
Effect of FAN
A higher FAN level increased diacetyl formation
Little or no effect on diacetyl removal
0
200
400
600
800
1000
1200
0 24 48 72 96 120 144 168 192
Dia
cety
l (p
pb
)
Fermentation time (h)
Fermentation temperature: 15C
Control (FAN 222 ppm)
FAN 252 ppm
FAN 287 ppm
15 23/10/2014 15
Modelled diacetyl plots
0
200
400
600
800
1000
1200
0 24 48 72 96 120 144 168 192 216 240 264 288
Dia
cety
l (µ
g/L
)
Fermentation time (h)
Measured (15C, pH 4.75, FAN 252 ppm)
Measured (12C, pH 5.3, FAN 366 ppm)
Measured (9C, pH 4.75, 222 ppm)
16 23/10/2014 16
Effects on diacetyl formation and removal
Diacetyl formation:
Diacetyl removal:
Increasing temperature Increased rate
Increasing pH Decreased rate
Increasing FAN No effect
Increasing yeast growth No effect
Increasing temperature Increased rate
Increasing pH Decreased / Increased rate
Increasing FAN Increased rate
Increasing yeast growth Increased rate
17 23/10/2014 17
Model application
Can the maturation period be shortened, without affecting other
beer properties, by using a variable temperature profile?
The model is used to predict two scenarios:
8
9
10
11
12
13
14
15
16
0 24 48 72 96 120 144
°C
18 23/10/2014 18
Model application
0
200
400
600
800
1000
1200
0 24 48 72 96 120 144 168 192 216 240 264 288 312 336 360 384
Dia
ce
tyl
(µg
/L)
Fermentation time (h)
Diacetyl
0
1
2
3
4
5
6
0 24 48 72 96 120 144 168 192 216 240 264 288 312 336 360 384
Ye
as
t d
ry m
as
s (
g/L
)
Fermentation time (h)
Yeast biomass
4
4,5
5
5,5
6
0 24 48 72 96 120 144 168 192 216 240 264 288 312 336 360 384
pH
Fermentation time (h)
pH
0
1
2
3
4
5
6
0 24 48 72 96 120 144 168 192 216 240 264 288 312 336 360 384
Alc
oh
ol
(% w
/v)
Fermentation time (h)
Alcohol
50 hour saving for reaching a
100 ppb diacetyl concentration:
290 vs 340 hours
Constant 12 °C
Variable T: 9 -15 °C
19 23/10/2014 19
Model application
0
200
400
600
800
1000
1200
0 24 48 72 96 120 144 168 192 216 240 264 288 312 336 360 384
Dia
cety
l (µ
g/L
)
Fermentation time (h)
Diacetyl
0
1
2
3
4
5
6
0 24 48 72 96 120 144 168 192 216 240 264 288 312 336 360 384
Yeast
dry
mass (
g/L
)
Fermentation time (h)
Yeast biomass
4
4,5
5
5,5
6
0 24 48 72 96 120 144 168 192 216 240 264 288 312 336 360 384
pH
Fermentation time (h)
pH
0
1
2
3
4
5
6
0 24 48 72 96 120 144 168 192 216 240 264 288 312 336 360 384
Alc
oh
ol
(% w
/v)
Fermentation time (h)
Alcohol
Constant 12 °C
Variable T: 9 -15 °C
20 23/10/2014 20
Conclusions
The enhanced model can be applied for:
experimental design
process design in breweries
better optimisation of wort and fermentation properties in order to
achieve improved process efficiency and consistent beer quality.
The current model did not consider the formation of any other
important flavour and aroma compounds besides diacetyl.
The model could be expanded to include other process
parameters
21 23/10/2014 21
Acknowledgements
PBL Brewing Laboratory
Tor-Magnus Enari fund
TEKES
Aila Siltala
Arvi Wilpola
Annika Wilhelmson
Eero Mattila
Liisa Änäkäinen
TECHNOLOGY FOR BUSINESS
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