Integrating Mathematical Modeling and Numerical Optimization in Biotechnological Processes: A Case Study on Microbial Fermentation
Keywords:
Bioprocess engineering, Excel Solver, Mathematical modelling, Metabolite production, Microbial fermentation, Numerical optimization.Abstract
In recent years, biotechnology has evolved into a multidisciplinary field that integrates biology, engineering, and computational sciences to develop efficient and sustainable bioprocesses. The production of valuable compounds through microbial fermentation such as enzymes, antibiotics, organic acids, or other secondary metabolites requires precise control over process parameters, including substrate concentration, nutrient balance, pH, temperature, and fermentation time. Optimizing microbial fermentation is essential for maximizing the yield of valuable compounds, but the complexity of these bioprocesses makes analytical solutions impractical. This paper presents an accessible framework for bioprocess improvement by integrating mathematical modelling with numerical optimization using Microsoft Excel’s Solver. Leveraging its powerful GRG Nonlinear and Evolutionary algorithms, a dynamic fermentation model was implemented to identify the most favourable process conditions including substrate concentration, pH, and temperature for enhanced metabolite production. This approach provides a versatile and user-friendly tool for researchers and students, enabling effective experimental design and data-driven decision-making in bioprocess engineering without requiring specialized programming knowledge.
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