Dynamic modeling, kinetics estimation, and uncertainty analysis of batch fermentation for optimal production of a novel co‐polymeric dipeptide from a thermophilic bacterium

Abstract BACKGROUND Biopolymers produced from a thermophilic bacterium induce better thermal stability and tensile strength than those produced from mesophilic bacterium. Developing dynamic models is crucial to providing an optimal design and scale‐up of a batch fermenter targeting biopolymer synthesis. This study aims to formulate certain dynamic models for the production of a novel copolymeric dipeptide (poly‐γ‐glutamic acid) while investigating the batch fermentation kinetics of Bacillus licheniformis . RESULTS Batch experiments were conducted at temperatures of 40 and 50 °C under varied glucose concentrations supplemented with nitrogen, which determined a maximum cell growth and higher biopolymer yield at optimal conditions of 50 °C and 80 g L −1 of glucose. The kinetics of dual substrate consumption, biomass growth, and biopolymer synthesis were estimated by fitting three dynamic models, namely, Monod multiplicative with Haldane inhibition, Bertolazzi, and Volterra to the experimental data. A global sensitivity analysis (GSA) was subsequently performed, which identified the impact of kinetic parametric uncertainties on the endpoint poly‐γ‐glutamic acid concentration. CONCLUSION The Volterra model revealed the best fits, as confirmed by a high coefficient of determination ( R 2 > 0.98) and minimum corrected‐Akaike's information criterion (AIC c = −194.78) compared to the other models. The GSA results revealed that the maximum cell concentration, growth‐associated term of the product formation, and the initial nitrogen concentration should be optimized with higher precision to yield an optimal biopolymer titer. This work facilitates the implementation of model‐based optimization and control tasks, which could enhance the performance of an industrial‐scale batch fermenter operated with a thermophilic bacterium. © 2025 Society of Chemical Industry (SCI).

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Journal
Journal of Chemical Technology & Biotechnology
Published
2025-05-15
DOI
https://doi.org/10.1002/jctb.7892
Primary Topic
Biopolymer Synthesis and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Dynamic modeling, kinetics estimation, and uncertainty analysis of batch fermentation for optimal production of a novel co‐polymeric dipeptide from a thermophilic bacterium

Riju De, Subhranshu Samal, Vivek Rangarajan, Prakruti B Sambal
Journal of Chemical Technology & Biotechnology
Biopolymer Synthesis and Applications
article

Dynamic modeling, kinetics estimation, and uncertainty analysis of batch fermentation for optimal production of a novel co‐polymeric dipeptide from a thermophilic bacterium

Riju De, Subhranshu Samal, Vivek Rangarajan, Prakruti B Sambal
article en

Abstract

Abstract BACKGROUND Biopolymers produced from a thermophilic bacterium induce better thermal stability and tensile strength than those produced from mesophilic bacterium. Developing dynamic models is crucial to providing an optimal design and scale‐up of a batch fermenter targeting biopolymer synthesis. This study aims to formulate certain dynamic models for the production of a novel copolymeric dipeptide (poly‐γ‐glutamic acid) while investigating the batch fermentation kinetics of Bacillus licheniformis . RESULTS Batch experiments were conducted at temperatures of 40 and 50 °C under varied glucose concentrations supplemented with nitrogen, which determined a maximum cell growth and higher biopolymer yield at optimal conditions of 50 °C and 80 g L −1 of glucose. The kinetics of dual substrate consumption, biomass growth, and biopolymer synthesis were estimated by fitting three dynamic models, namely, Monod multiplicative with Haldane inhibition, Bertolazzi, and Volterra to the experimental data. A global sensitivity analysis (GSA) was subsequently performed, which identified the impact of kinetic parametric uncertainties on the endpoint poly‐γ‐glutamic acid concentration. CONCLUSION The Volterra model revealed the best fits, as confirmed by a high coefficient of determination ( R 2 > 0.98) and minimum corrected‐Akaike's information criterion (AIC c = −194.78) compared to the other models. The GSA results revealed that the maximum cell concentration, growth‐associated term of the product formation, and the initial nitrogen concentration should be optimized with higher precision to yield an optimal biopolymer titer. This work facilitates the implementation of model‐based optimization and control tasks, which could enhance the performance of an industrial‐scale batch fermenter operated with a thermophilic bacterium. © 2025 Society of Chemical Industry (SCI).

Journal of Chemical Technology & BiotechnologyVol. 100(8)
Birla Institute of Technology and Science, Pilani - Goa Campus (IN)
Birla Institute of Technology and Science, Pilani
Industry, innovation and infrastructure
Openalex Percentile: Top 90%
Biopolymer Synthesis and Applications
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