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Experimental optimization of a real time fed-batch fermentation process using Markov decision process.

V M Saucedo¹, M N Karim

¹Department of Chemical and Bioresource Engineering, Colorado State University, Fort Collins, Colorado 80523-1370, USA.

Biotechnology and Bioengineering

|July 20, 1997

Summary

This summary is machine-generated.

This study optimizes ethanol production in fed-batch experiments using Markov decision process (MDP) optimization. The technique models biological systems stochastically for improved real-time control and yield.

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Area of Science:

Biotechnology
Biochemical Engineering
Process Optimization

Background:

Biological systems exhibit stochastic behavior, necessitating advanced modeling techniques.
Fed-batch cultivation is a key bioprocess for producing valuable compounds like ethanol.
Optimization of fed-batch processes is crucial for maximizing product yield and economic viability.

Purpose of the Study:

To implement and evaluate a Markov decision process (MDP) optimization technique for real-time fed-batch experiments.
To demonstrate the suitability of MDP for optimizing stochastic biological systems.
To compare MDP-optimized ethanol production with previous experimental results.

Main Methods:

A nonlinear input/output model was employed to determine probability transitions within the MDP framework.
All Markov decision process elements were identified using physical process parameters.
The infinite horizon problem with a total expected discount policy was used for optimization.

Main Results:

The methodology successfully applied MDP optimization to a real-time fed-batch experiment.
The study provided a framework for integrating stochastic modeling with process control.
Optimized ethanol production using MDP was compared to established experimental outcomes.

Conclusions:

Markov decision process (MDP) is an effective technique for optimizing stochastic biological systems in real-time fed-batch experiments.
The integration of physical parameters into MDP elements enhances model accuracy.
This approach offers a robust method for improving ethanol production yields in bioprocesses.