An on-line adaptive glucose feeding system incorporating patterns recognition for glucose concentration control in glutamate fermentations

Abstract

In glutamate fermentation, intermittent feeding is the most widely used glucose feed strategy. This feeding strategy causes severe fluctuations of glucose concentration and osmotic pressure in fermentation broth, which deteriorates the viability of the cell and reduces glutamate production in turn. In order to maintain glucose concentration at stable and constant levels, an on-line prediction and feedback control system based an empiric mass balance model was developed. However, the control system did not work properly and sometimes glucose concentration could even decline to 0 level (glucose exhaustion), as the model parameter varies in different runs. As a result, a novel model-based adaptive feedback control system incoporating with an artificial neural network (ANN) based pattern reconition unit for on-line diagnosizing the fault of glucose exhaustion was proposed and applied for glutamate fermentation. This adaptive control system could accurately detect glucose exhaustion when it occurs, and then immediately updates the control parameter based on some pre-defined rule. With the proposed control system, glucose was automatically fed, and its concentration could be maintained at desired levels constantly. As a result, glutamate concentration was 17 ~ 30% higher than that of the traditional fermentations using the intermittent glucose feed strategy.

This is a preview of subscription content, access via your institution.

References

  1. 1.

    Sano, C. (2009) History of glutamate production. Amer. J. Clin. Nutr. 90: 728S-732S.

    Article  Google Scholar 

  2. 2.

    Xiao, J., Z. P. Shi, P. Gao, H. J. Feng, Z. Y. Duan, and Z. G. Mao (2006) On-line optimization of glutamate production based on balanced metabolic control by RQ. Bioproc. Biosyst. Eng. 29: 109–117.

    CAS  Article  Google Scholar 

  3. 3.

    Gourdon, P. (2003) Osmotic stress, glucose transport capacity and consequences for glutamate overproduction in Corynebacterium glutamicum. J. Biotechnol. 104: 77–85.

    CAS  Article  Google Scholar 

  4. 4.

    Heller, A. and B. Feldman (2008) Electrochemical glucose sensors and their applications in diabetes management. Chem. Rev. 108: 2482–2505.

    CAS  Article  Google Scholar 

  5. 5.

    Jin, H., X. Chen, L. Wang, K. Yang, and L. Wu (2015) Adaptive soft sensor development based on online ensemble gaussian process regression for nonlinear time-varying batch processes. Ind. Eng. Chem. Res. 54: 7320–7345.

    CAS  Article  Google Scholar 

  6. 6.

    Zhu, J., Z. Ge, and Z. Song (2015) Robust semi-supervised mixture probabilistic principal component regression model development and application to soft sensors. J. Proc. Control. 32: 25–37.

    Article  Google Scholar 

  7. 7.

    Gustavsson, R., C. Lukasser, and C.-F. Mandenius (2015) Control of specific carbon dioxide production in a fed-batch culture producing recombinant protein using a soft sensor. J. Biotechnol. 200: 44–51.

    CAS  Article  Google Scholar 

  8. 8.

    Agatonovic-Kustrin, S. and R. Beresford (2000) Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J. Pharmaceut. Biomed. Anal. 22: 717–727.

    CAS  Article  Google Scholar 

  9. 9.

    Jin, H., Z. Zheng, M. Gao, Z. Duan, Z. Shi, Z. Wang, and J. Jin (2007) Effective induction of phytase in Pichia pastoris fedbatch culture using an ANN pattern recognition model-based online adaptive control strategy. Biochem. Eng. J. 37: 26–33.

    CAS  Article  Google Scholar 

  10. 10.

    Ardehali, M. M., M. Farmad, and C. C. Adams (2010) Development of pattern recognition based ANN for energy auditing and inefficiency diagnostics of influential design elements utilising electrical energy data. J. Energy Inst. 83: 101–107.

    CAS  Article  Google Scholar 

  11. 11.

    Chinas, P., I. Lopez, J. A. Vazquez, R. Osorio, and G. Lefranc (2015) SVM and ANN Application to Multivariate Pattern Recognition Using Scatter Data. IEEE Lat. Amer. Trans. 13: 1633–1639.

    Article  Google Scholar 

  12. 12.

    Zhang, C., Z. Shi, P. Gao, Z. Duan, and Z. Mao (2005) On-line prediction of products concentrations in glutamate fermentation using metabolic network model and linear programming. Biochem. Eng. J. 25: 99–108.

    CAS  Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding authors

Correspondence to Minjie Gao or Xidong Ren.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ding, J., Jia, L., Mpofu, E. et al. An on-line adaptive glucose feeding system incorporating patterns recognition for glucose concentration control in glutamate fermentations. Biotechnol Bioproc E 21, 758–766 (2016). https://doi.org/10.1007/s12257-016-0394-z

Download citation

Keywords

  • glutamate fermentation
  • glucose concentration
  • feedback control
  • artificial neural network
  • pattern recognition
  • adaptive control