Multi-objective optimization of glycopeptide antibiotic production in batch and fed batch processes

Area of Science:

• Biotechnology
• Chemical Engineering
• Microbial Fermentation

Background:

• Fermentation processes require balancing competing objectives like product yield and production costs.
• Multiobjective optimization problems yield Pareto optimal solutions, offering flexibility in decision-making.
• Amycolatopsis balhimycina is a key producer of glycopeptide antibiotics.

Purpose of the Study:

• To apply the epsilon-constraint method for optimizing fermentation objectives.
• To generate Pareto optimal solutions for product concentration and cost-effectiveness.
• To validate the optimized solutions experimentally for batch and fed-batch operations.

Main Methods:

• Utilized the epsilon-constraint approach to address multiobjective optimization.
• Developed a process model for Amycolatopsis balhimycina.
• Generated Pareto frontiers for batch and fed-batch fermentation modes.

Main Results:

• Identified a range of Pareto optimal solutions for both batch and fed-batch operations.
• Batch solutions ranged from (0.75 g/L, 3.97 $/g) to (0.44 g/L, 5.19 $/g).
• Fed-batch solutions ranged from (1.5 g/L, 5.46 $/g) to (1.1 g/L, 6.34 $/g).
• Experimental validation confirmed one Pareto solution for each operational mode.

Conclusions:

• The epsilon-constraint method effectively generates Pareto optimal solutions for fermentation.
• These solutions provide valuable insights into the trade-offs between product concentration and media cost.
• Experimental validation supports the practical applicability of the derived Pareto solutions in bioprocess optimization.