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Biogas Purification through the use of a Microalgae-Bacterial System in Semi-Industrial High Rate Algal Ponds

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Optimization of an industrial microalgae fermentation.

A K Hilaly¹, M N Karim, D Guyre

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

Biotechnology and Bioengineering

|February 20, 1994

Summary

This summary is machine-generated.

This study optimized microalgae fermentation for industrial applications. A direct search algorithm proved simplest and most effective for maximizing cellular productivity.

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Area of Science:

Biotechnology
Biochemical Engineering
Industrial Microbiology

Background:

Industrial microalgae fermentation requires optimization for enhanced cellular productivity.
Pilot plant data from Coors Biotech Products Company provided the basis for mathematical modeling.

Purpose of the Study:

To investigate and optimize cellular productivity in industrial microalgae fermentation.
To develop and compare mathematical models and control algorithms for fermentation optimization.

Main Methods:

Development of a mathematical model based on pilot plant data.
Application of Pontryagin's maximum principle for optimal feed policy determination.
Implementation of feedback control algorithms, adaptive optimization (steepest descent, recursive least squares), and a direct search algorithm for optimization.

Main Results:

The direct search algorithm was identified as the preferable optimization scheme due to its simplicity.
Experimental results from real-time feedback algorithm application showed good agreement with theoretical analyses.
Adaptive optimization determined the optimum dilution rate during continuous operation.

Conclusions:

The direct search algorithm offers a practical and effective method for optimizing industrial microalgae fermentation.
Mathematical modeling and feedback control are valuable tools for enhancing cellular productivity.
The study successfully demonstrated the feasibility of optimizing microalgae fermentation processes using advanced control strategies.