Dynamic Modeling of Bacterial Cellulose Production Using Combined Substrate- and Biomass-Dependent Kinetics
In this work, kinetic models are assessed to describe bacterial cellulose (BC) production, substrate consumption, and biomass growth by <i>K. xylinus</i> in a batch-stirred tank bioreactor, under 700 rpm and 500 rpm agitation rates. The kinetic models commonly used for <i>Acetobact...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
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| Series: | Computation |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-3197/12/12/239 |
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| Summary: | In this work, kinetic models are assessed to describe bacterial cellulose (BC) production, substrate consumption, and biomass growth by <i>K. xylinus</i> in a batch-stirred tank bioreactor, under 700 rpm and 500 rpm agitation rates. The kinetic models commonly used for <i>Acetobacter</i> or <i>Gluconacetobacter</i> were fitted to published data and compared using the Akaike Information Criterion (AIC). A stepwise fitting procedure was proposed for model selection to reduce computation effort, including a first calibration in which only the biomass and substrate were simulated, a selection of the three most effective models in terms of AIC, and a calibration of the three selected models with the simulation of biomass, substrate, and product. Also, an uncoupled product equation involving a modified Monod substrate function is proposed for a 500 rpm agitation rate, leading to an improved prediction of BC productivity. The M2c and M1c models were the most efficient for biomass growth and substrate consumption for the combined AIC, under 700 rpm and 500 rpm agitation rates, respectively. The average coefficients of determination for biomass, substrate, and product predictions were 0.981, 0.994, and 0.946 for the 700 rpm agitation rate, and 0.984, 0.991, and 0.847 for the 500 rpm agitation rate. It is shown that the prediction of BC productivity is improved through the proposed substrate function, whereas the computation effort is reduced through the proposed model fitting procedure. |
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| ISSN: | 2079-3197 |