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Artificial neural networks in bioprocess state estimation

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Modern Biochemical Engineering

Part of the book series: Advances in Biochemical Engineering/Biotechnology ((ABE,volume 46))

Abstract

The application of artificial neural networks to the estimation and prediction of bioprocess variables is presented in this paper. A neural network methodology is discussed, which uses environmental and physiological information available from on-line sensors, to estimate concentration of species in the bioreactor. Two case studies are presented, both based on the ethanol production by Zymomonas mobilis. An efficient optimization algorithm which reduces the number of iterations required for convergence is proposed. Results are presented for different training sets and different training methodologies. It is shown that the neural network estimator provides good on-line bioprocess state estimations.

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Abbreviations

a:

Power of the ethanol inhibition term in μ

b:

Power of the ethanol inhibition term in q p

D h−1:

Dilution rate

F l h−1:

Permeate flow

F0 l h−1:

Total feed flow to the system

Ki g l−1:

Substrate inhibition constant for growth

K′i g l−1:

Substrate inhibition constant for ethanol production

Ks g l−1:

Monod kinetic constant

K′s g l−1:

Saturation constant for q p

p g l−1:

Ethanol concentration

P′i g l−1:

Ethanol threshold concentration for ethanol production

Pm g l−1:

Maximum ethanol concentration for cell growth

P′m g l−1:

Maximum ethanol concentration for ethanol production

qp g g−1 h−1:

Specific ethanol production rate

qpm g g−1 h−1:

Maximum specific ethanol production rate

R:

Recycle ratio

s g l−1:

Glucose concentration

Si g l−1:

Threshold substrate concentration for cell growth

s0 g l−1:

Glucose concentration in the feed stream

x g l−1:

Biomass concentration

xmax g l−1:

Maximum cell concentration

Yp/s g g−1:

Ethanol yield, g ethanol per g substrate consumed

μ h−1:

Specific growth rate

μmo h−1:

Maximum specific growth rate at zero ethanol concentrations

d p :

L-dimensional target vector

E p :

Sum-of-squares error for training example p

E(w):

Total sum-of-squares error for all input patterns, function of weight vector w

G (q) :

Vector of steepest descent directions in iteration (q)

g (q) p :

Gradient vector of one input-output pattern p

g (q) uv :

Element of the gradient vector g (q)p

n:

Number of interconnection weights in the network

O pj :

Output of neuron j from the training set p

p:

number of training pattern

q:

number of iteration

r (q) :

Vector of conjugate gradient directions in Eq. (17)

S pj :

Activation state of neuron j from the training set p

S (q) :

Vector of search directions for conjugate gradient algorithm

w (q) :

Vector of neural network weights in iteration q

w ij :

Interconnection weight from node i to node j

x p :

N-dimensional network input vector

y p :

L-dimensional network output vector

z (q) :

Vector of conjugate gradient directions in Eq. (16)

α(q) :

Step size in iteration q used in Eq. (8)

β(q) :

Step size in iteration q used in Eq. (17)

γ(q) :

Step size in iteration q used in Eq. (16)

δ pv :

Change in E p due to changes in neural activation state S pu.

σ:

Argument of the sigmoid function.

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Authors and Affiliations

  1. Department of Agricultural and Chemical Engineering, Colorado State University, 80 523, Fort Collins, CO., USA

    M. N. Karim & S. L. Rivera

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  1. M. N. Karim
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  2. S. L. Rivera
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© 1992 Springer-Verlag

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Karim, M.N., Rivera, S.L. (1992). Artificial neural networks in bioprocess state estimation. In: Modern Biochemical Engineering. Advances in Biochemical Engineering/Biotechnology, vol 46. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0000703

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  • DOI: https://doi.org/10.1007/BFb0000703

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  • Print ISBN: 978-3-540-55276-5

  • Online ISBN: 978-3-540-47005-2

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