Modeling and parameter identification of the simultaneous saccharification-fermentation process for ethanol...

Area of Science:

• Biotechnology
• Biochemical Engineering
• Process Modeling

Background:

• Bioethanol offers environmental benefits but faces economic challenges compared to fossil fuels.
• Improving the competitiveness of bioethanol production requires optimization and control across all process stages.
• Existing process improvements in purification and microbial strains necessitate better modeling for advanced control.

Purpose of the Study:

• To develop and compare predictive models for the simultaneous saccharification-fermentation (SSF) process.
• To introduce a novel cybernetic model for enhanced process description and control.
• To establish a robust parameter identification procedure for these models.

Main Methods:

• Development of an unstructured and a novel cybernetic model for the SSF process.
• The cybernetic model accounts for starch degradation into glucose and dextrins, including intracellular reactions.
• Parameter identification using Metropolis Monte Carlo optimization and sensitivity analysis with literature data.

Main Results:

• The proposed cybernetic model provides a more detailed and accurate description of the SSF process.
• The developed models are suitable for simulation, soft sensing, and control algorithm integration.
• The parameter identification method effectively utilizes experimental data for model calibration.

Conclusions:

• Accurate and predictive models are crucial for optimizing bioethanol production economics.
• The novel cybernetic model offers significant advantages for understanding and controlling the SSF process.
• This work lays the foundation for improved process control and optimization in the bioethanol industry.