The optimization of technological condition in the fermentation process of glutamate by pattern recognition method

Area of Science:

• Biotechnology
• Chemical Engineering
• Industrial Microbiology

Background:

• Fermentation processes require precise control of technological conditions like pH, temperature, and ventilation.
• Optimizing these parameters is crucial for maximizing yield and efficiency in industrial glutamate production.

Purpose of the Study:

• To optimize the technological conditions for fermentation glutamate production using a novel computerized pattern recognition method.
• To develop a new mathematical model for predicting optimal fermentation parameters based on experimental data.

Main Methods:

• Utilized computerized pattern recognition to map multi-dimensional parameter space onto a plane for identifying optimal regions.
• Employed Monte Carlo simulation to transform optimal regions back into the original data space for parameter determination.
• Developed and validated a new mathematical model based on production data.

Main Results:

• The optimization method identified the best combination of fermentation parameters.
• Achieved a 2.9% increase in glucose to glutamic acid transfer ratio.
• Increased production capacity by 1.45% and glutamic acid concentration by 2.65%.

Conclusions:

• The computerized pattern recognition method effectively optimizes fermentation glutamate production.
• The developed mathematical model provides a basis for improved industrial fermentation control.
• The method has been successfully implemented in factories, leading to reduced raw material and production costs.