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A soft sensor based on online biomass measurements for the glucose estimation and control of fed-batch cultures of Bacillus thuringiensis

Abstract

On bioprocess engineering, experimental measurements are always a costly part of the modeling effort; therefore, there is a constant need to develop cheaper, simpler, and more efficient methodologies to exploit the information available. The aim of the present work was to develop a soft sensor with the capacity to perform reliable substrate predictions and control in microbial cultures of the fed-batch type, using mainly microbial growth data. This objective was achieved using dielectric spectroscopy technology for online monitoring of microbial growth and hybrid neural networks for online prediction of substrate concentration. The glucose estimator was integrated to a fuzzy logic controller to control the substrate concentration in a fed-batch experiment. Dielectric spectroscopy is a technology sensitive to the air volume fraction in the culture media and the turbulence generated by the agitation; however, the introduction of a polynomial function for the calibration of the permittivity signal allowed biomass estimations with an approximation error of 2%. The methodology presented in this work was successfully implemented for the glucose prediction and control of a fed-batch culture of Bacillus thuringiensis with an approximation error of 6%.

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Acknowledgements

Research supported by Conacyt (INFRA-2012-01-188339) and Cinvestav-IPN (Multi-05). The authors are thankful to the anonymous reviewers for their insightful comments and suggestions.

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Correspondence to Josefina Barrera-Cortés.

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We have no conflicts of interest to disclose.

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Escalante-Sánchez, A., Barrera-Cortés, J., Poggi-Varaldo, H.M. et al. A soft sensor based on online biomass measurements for the glucose estimation and control of fed-batch cultures of Bacillus thuringiensis. Bioprocess Biosyst Eng 41, 1471–1484 (2018). https://doi.org/10.1007/s00449-018-1975-3

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Keywords

  • Glucose estimation
  • Hybrid neural networks
  • Online biomass monitoring
  • Bacillus thuringiensis
  • Dielectric spectroscopy