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Artificial neural networks in bioprocess state estimation.

M N Karim¹, S L Rivera

¹Department of Agricultural and Chemical Engineering, Colorado State University, Fort Collins 80523.

Advances in Biochemical Engineering/Biotechnology

|January 1, 1992

Summary

Artificial neural networks (ANNs) effectively estimate bioprocess variables using sensor data. This study demonstrates ANNs for real-time monitoring and prediction in ethanol production, showing accurate state estimations.

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Area of Science:

Biotechnology
Biochemical Engineering
Computational Biology

Background:

Bioprocess monitoring is crucial for optimizing yield and product quality.
Accurate estimation of species concentration in bioreactors remains a challenge.
Artificial neural networks offer a promising approach for complex bioprocess modeling.

Purpose of the Study:

To present a neural network methodology for estimating bioprocess variables using on-line sensor data.
To apply this methodology to ethanol production by Zymomonas mobilis.
To propose an efficient optimization algorithm for neural network training.

Main Methods:

Utilized environmental and physiological data from on-line sensors.
Developed and applied a neural network architecture for species concentration estimation.
Implemented an optimization algorithm to accelerate neural network convergence.
Evaluated performance using different training sets and methodologies.

Main Results:

The neural network estimator demonstrated good on-line bioprocess state estimations.
Case studies focused on ethanol production by Zymomonas mobilis.
The proposed optimization algorithm reduced required training iterations.
Performance varied based on training sets and methodologies.

Conclusions:

Artificial neural networks are effective tools for on-line bioprocess state estimation.
The proposed methodology provides accurate and efficient monitoring of bioprocesses.
This approach has significant implications for optimizing industrial bioproduction.