Sugar + oxygen → yeast + CO2 + alcohol - WordPress.com · fermentation system to determine the...

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1.0 Introduction: Yeast Fermentation

Modelling, Simulation and Computing Laboratory (mscLab)School of Engineering and Information Technology, Universiti Malaysia Sabah, Malaysia

SOMChE201125th Symposium of Malaysian Chemical Engineers 2011

Kuantan, Malaysia, 28 November -1 December 2011

• Saccharomyces cerevisiae (aka baker’s yeast) - the most studied strain

• As the benchmark

• What we want? ---MORE YEAST! Enhance the supply of raw material Sustainable environment and production Future potential

Sugar + oxygen → yeast + CO2 + alcohol

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1.0 Introduction: Fed batch operation




Yeast (biomass)

Fermentor

Product (after some time)

Substrate (Food)

(1) Dynamic growth behavior (2) Quality (3) Resource saving

WHY Fed-batch?

AIM: Maximize yeastMinimize alcohol

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2.0 Objective

• To introduce and apply the learning algorithm (Q-Learning) infermentation system to determine the optimal substrate feeding profile

• To use the Q-Learning optimized profile as predetermined profile forfermentation system

Motivations:• Unsupervised learning ability• Reduction of human input• Applicable to highly dynamic system• Multi-objectives algorithm




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3.0 Methodology




Observation and Learning Module• QL can be described as the following equation:

Wheres = state s’ = next statea = action a’ = next actionQ = current Q-valueQ’ = Expected Q-value in next statet = time α = learning rateR = reward γ = discount factor

• Q-learning (QL) is an unsupervised learning algorithm

• Works by learning state-action function that gives expected reward of taking an action in a specific state with a fixed policy.

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3.0 Methodology

In this research, QL is used to:

•Observe and learn the effect ofinput feed flow rate on thevarious product concentration

•Maximum reward is chosen forthe feed which gives maximumyeast and less alcohol

•Determine the optimal feedprofile by total maximum reward.




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4.0 Simulation




• Complex process dynamics

• Substrate and product inhibition

DYNAMIC

KINETIC

Material Balances

Product (after some time)

Feed inS0

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5.0 Results and Discussion




0 1 2 3 4 5 6 7 8 9 100

0.5

1

1.5

2

2.5

3

3.5

4

Time (h)

Con

cent

ratio

n of

Glu

cose

(g/L

), E

than

ol (g

/L)

glucoseethanol

0 1 2 3 4 5 6 7 8 9 1010

15

20

25

30

35

40

45

50

Time (h)

Con

cent

ratio

n of

Yea

st (g

/L)

yeast

0 1 2 3 4 5 6 7 8 9 100

0.5

1

1.5

2

2.5

3

3.5

4

Time (h)C

once

ntra

tion

of G

luco

se (g

/L),

Eth

anol

(g/L

)

glucoseethanol

0 1 2 3 4 5 6 7 8 9 1010

15

20

25

30

35

40

45

50

Time (h)

Con

cent

ratio

n of

Yea

st (g

/L)

yeast

Q-learning (QL) optimized feeding performances

Nominal exponential feeding (EF) performances: F = 500e0.05t

Comparison of substrate (glucose), undesirable products (ethanol) and biomass(yeast) concentrations for exponential feeding and Q-learning feeding strategy.

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0 1 2 3 4 5 6 7 8 9 100

500

1000

1500

2000

2500

3000

Time (h)

Feed

Flo

w R

ate

(L/h

)

Feed flow rate (EF)Feed flow rate (QL)

QL manage to optimize feed flow rate to the maximum before inhibitions andoverflow metabolism which triggers ethanol production happens.

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0 1 2 3 4 5 6 7 8 9 100

0.5

1

1.5

2

2.5

3

3.5

4

Time (h)

Con

cent

ratio

n of

Glu

cose

(g/L

), E

than

ol (g

/L)

glucose (EF)glucose (QL)

0 1 2 3 4 5 6 7 8 9 100

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Time (h)

Con

cent

ratio

n of

Glu

cose

(g/L

), E

than

ol (g

/L)

ethanol (EF)ethanol (QL)

0 1 2 3 4 5 6 7 8 9 100

5

10

15

20

25

30

35

40

45

50

Time (h)

Con

cent

ratio

n of

Glu

cose

(g/L

), E

than

ol (g

/L)

yeast (EF)yeast (QL)

QL-suggested feeding profile is able to achieve multi-objective purposes: reduce substrate usage, maximize yeast production and minimize ethanol.

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6.0 Conclusion

• Based on the performances, Q-learning is able to:

suggest satisfying optimal profile as predetermined feeding strategy for yeast fermentation based on its objectives;

act in dynamic-complex system;

suggest an alternative algorithm for biosystem




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