Skip to main content
SpringerLink
Log in

An artificial neural network for biomass estimation from automatic pH control signal

  • Published:
Biotechnology and Bioprocess Engineering Aims and scope Submit manuscript

Abstract

This study developed an artificial neural network (ANN) to estimate the growth of microorganisms during a fermentation process. The ANN relies solely on the cumulative consumption of alkali and the buffer capacity, which were measured on-line from the on/off control signal and pH values through automatic pH control. The two input variables were monitored on-line from a series of different batch cultivations and used to train the ANN to estimate biomass. The ANN was refined by optimizing the network structure and by adopting various algorithms for its training. The software estimator successfully generated growth profiles that showed good agreement with the measured biomass of separate batch cultures carried out between at 25 and 35_C.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. [1]Schügerl, K. (1991) Common instruments for process analysis and control. pp. 6–25. In: R. J. Rehm, G. Reed, A. Puhler, and P. Stadler (eds.).Biotechnology 4. VCH Pub-lishers Inc., New York, NY, USA.

    Google Scholar 

  2. Olsson, L. and J. Nielsen (1997) On-line andin situ monitoring of biomass in submerged cultivations.Trends Biotechnol. 15: 517–522.

    Article  CAS  Google Scholar 

  3. Salgado, A. M., R. O. M. Folly, and B. Valdman (2001) Biomass monitoring by use of a continuous on-line optical sensor.Sens. Actuators B: Chem. 75: 24–28.

    Article  Google Scholar 

  4. MacMichael, G., W. B. Armiger, J. F. Lee, and R. Mutharasan (1987) On-line measurement of hybridoma growth by culture fluorescence.Biotechnol. Tech. 1: 213–218.

    Article  Google Scholar 

  5. Li, J. K. and A. E. Humphrey (1991) Use of fluorometry for monitoring and control of a bioreactor.Biotechnol. Bioeng. 37: 1043–1049.

    Article  CAS  Google Scholar 

  6. Na, J.-G., H. H. Kim, and Y. K. Chang (2005) On-line estimation of cell growth from agitation speed in DO-stat culture of a filamentous microorganism,Agaricus blazei.Biotechnol. Bioprocess Eng. 10: 571–575.

    Article  CAS  Google Scholar 

  7. Ferreira, A. P., L. M. Vieira, J. P. Cardoso, and J. C. Menezes (2005) Evaluation of a new annular capacitance probe for biomass monitoring in industrial pilot-scale fermentations.J. Biotechnol. 116: 403–409.

    Article  Google Scholar 

  8. Harris, C. M. and D. B. Kell (1983) The radio-frequency dielectric properties of yeast cells measured with a rapid, automated, frequency-domain dielectric spectrometer.Bio-electrochem. Bioenerg. 11: 15–28.

    Article  Google Scholar 

  9. Bogaerts, P. h. and R. Hanus (2001) On-line state estimation of bioprocesses with full horizon observers.Math. comput. Simul. 56: 425–441.

    Article  Google Scholar 

  10. Chattaway, T., A. L. Demain, and G. Stephanopoulos (1992) Use of various measurements for biomass estimation.Biotechnol. Prog. 8: 81–84.

    Article  CAS  Google Scholar 

  11. Di Massimo, C., P. A. Lant, A. Saunders, G. A. Montague, M. T. Tham, and A. Morris (1992) Bioprocess application of model-based estimation technique.J. Chem. Technol. Biotechnol. 53: 265–277.

    Google Scholar 

  12. Maher, M., G. Roux, and B. Dahhou (1995) A method for estimating the state variables and parameters of fermentation systems.J. Chem. Technol. Biotechnol. 63: 153–159.

    Article  CAS  Google Scholar 

  13. Thibault, J., V. V. Breusegem, and A. Cheruy (1990) On-line prediction of fermentation variables using neural networks.Biotechnol. Bioeng. 36: 1041–1048.

    Article  CAS  Google Scholar 

  14. Pons, M. N., A. Rajab, J. M. Flaus, J. M. Engasser, and A. Cheruy (1988) Comparison of estimation methods for biotechnological processes.Chem. Eng. Sci. 43: 1909–1914.

    Article  CAS  Google Scholar 

  15. Shi, Y. and W.-K. Yuan (1988) Application of adaptive estimation in microbial fermentation processes.Chem. Eng. Sci. 43: 1915–1920.

    Article  CAS  Google Scholar 

  16. Di Massimo, C., G. A. Montague, M. J. Willis, M. T. Tham, and A. J. Morris (1992) Towards improved penicillin fermentation via artificial neural networks.Comp. Chem. Eng. 16: 283–291.

    Article  Google Scholar 

  17. James, S., R. Legge, and H. Budman (2002) Comparative study of black-box and hybrid estimation methods in fed-batch fermentation.J. Process Control 12: 113–121.

    Article  CAS  Google Scholar 

  18. Baughman, D. R. and Y. A. Liu (1995)Neural Networks in Bioprocessing and Chemical Engineering. pp. 1–29. Academic Press, San Diego, CA, USA.

    Google Scholar 

  19. Jung Y.-K. and W. Hur (1997) Analysis of pH change and an automatic pH control with a new function: on-line estimation of acetic acid.Biotechnol. Bioprocess Eng. 90: 580–582.

    Google Scholar 

  20. Miller, G. L. (1959) Use of dinitrosalicylic acid reagent for determination of reducing sugar.Anal. Chem. 31: 426–428.

    Article  CAS  Google Scholar 

  21. Luli, G. W. and W. R. Strohl (1990) Comparison of growth, acetate production, and acetate inhibition ofEscherichia coli strains in batch and fed-batch fermentations.Appl. Environ. Microbiol. 56: 1004–1011.

    CAS  Google Scholar 

  22. Han, K. (1992)A Study of Acetic Acid Formation in Escherichia coli Fermentation. Ph.D. Thesis. University of California, Irvine, CA, USA.

    Google Scholar 

  23. Beluhan, D. and S. Beluhan (2000) Hybrid modeling approach to on-line estimation of yeast biomass concentration in industrial bioreactor.Biotechnol. Lett. 22: 631–635.

    Article  CAS  Google Scholar 

  24. Feitkenhauer, H. and U. Meyer (2004) Software sensors based on titrimetric techniques for the monitoring and control of aerobic and anaerobic bioreactors.Biochem. Eng. J. 17: 147–151.

    Article  CAS  Google Scholar 

  25. Suzuki, T., T. Yamane, and S. Shimizu (1990) Phenomenological background and some preliminary trials of automated substrate supply in pH-stat model fed-batch culture using a set point of high limit.J. Ferment. Bioeng. 69: 292–297.

    Article  CAS  Google Scholar 

  26. San, K. and G. Stephanopoulos (1984) Studies on on-line bioreactor identification. IV. Utilization of pH measurements for product estimation.Biotechnol. Bioeng. 26: 1209–1218.

    Article  CAS  Google Scholar 

  27. Hur, W. and Y.-K. Chung (2005) On-line monitoring of IPTG induction for recombinant protein production using an automatic pH control signal.Biotechnol. Bioprocess Eng. 10: 304–308.

    Article  CAS  Google Scholar 

  28. Lee, D. (2005) Component-based software architecture for biosystem reverse engineering.Biotechnol. Bioprocess Eng. 10: 400–407.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Bioengineering and Technology, College of Engineering, Kangwon National University, 200-701, Chunchon, Korea

    Won Hur & Yoon-Keun Chung

Authors
  1. Won Hur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yoon-Keun Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Won Hur.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Won, H., Yoon-Keun, C. An artificial neural network for biomass estimation from automatic pH control signal. Biotechnol. Bioprocess Eng. 11, 351–356 (2006). https://doi.org/10.1007/BF03026252

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF03026252

Keywords

Search

Navigation