Applied Biochemistry and Biotechnology

, Volume 166, Issue 1, pp 87–111 | Cite as

Design and Evaluation of an Optimal Controller for Simultaneous Saccharification and Fermentation Process

  • Ganti S. Murthy
  • David B. Johnston
  • Kent D. Rausch
  • M. E. Tumbleson
  • Vijay Singh
Article

Abstract

Ethanol from corn is produced using dry grind corn process in which simultaneous saccharification and fermentation (SSF) is one of the most critical unit operations. In this work an optimal controller based on a previously validated SSF model was developed by formulating the SSF process as a Bolza problem and using gradient descent methods. Validation experiments were performed to evaluate the performance of optimal controller under different process disturbances that are likely to occur in practice. Use of optimal control algorithm for the SSF process resulted in lower peak glucose concentration, similar ethanol yields (13.38±0.36% v/v and 13.50±0.15% v/v for optimally controlled and baseline experiments, respectively). Optimal controller improved final ethanol concentrations as compared to process without optimal controller under conditions of temperature (13.35±1.28 and 12.52±1.19% v/v for optimal and no optimal control, respectively) and pH disturbances (12.65±0.74 and 11.86±0.49% v/v for optimal and no optimal control, respectively). Cost savings due to lower enzyme usage and reduced cooling requirement were estimated to be up to $1 million for a 151 million L/yr (40 million gal/yr) dry grind plant.

Keywords

Dry grind corn ethanol Saccharomyces cerevisiae Cybernetic model SSF process Optimal controller Gradient descent Process disturbances 

References

  1. 1.
    AACC (2002). Approved methods of the AACC. Method 44–15A (10th ed.). St. Paul, MN: The Association.Google Scholar
  2. 2.
    Anuradha, R., Suresh, A., & Venkatesh, K. (1999). Simultaneous saccharification and fermentation of starch to lactic acid. Process Biochemistry, 35, 367–375.CrossRefGoogle Scholar
  3. 3.
    Chen, L., Bastin, G., & Breusegem, V. V. (1995). A case study of adaptive nonlinear regulation of fed-batch biological reactors. Automatica, 31, 55–65.CrossRefGoogle Scholar
  4. 4.
    Cimander, C., Bachinger, T., & Mandenius, C. F. (2003). Integration of distributed multi-analyzer monitoring and control in bioprocessing based on a real-time expert system. Journal of Biotechnology, 103, 237–248.CrossRefGoogle Scholar
  5. 5.
    Ferreira, L. S., Jr. D. S., Trierweiler, M. B., Broxtermann, J. O., Folly, ROM, & Hitzmann, B. (2003). Aspects concerning the use of biosensors for process control: Experimental and simulation investigations. Computer and Chemical Engineering, 27, 1165–1173.CrossRefGoogle Scholar
  6. 6.
    Giuseppin, M. L. F., & Reil, N. A. W. (2000). Metabolic modeling of saccharomyces cerevisiae using the optimal control of homeostasis: A cybernetic model definition. Metabolic Engineering, 2, 14–33.CrossRefGoogle Scholar
  7. 7.
    Horiuchi, J. (2002). Fuzzy modeling and control of biological processes. Journal of Bioscience and Bioengineering, 94, 574–578.Google Scholar
  8. 8.
    Kapadi, M. D., & Gudi, R. D. (2004). Optimal control of fed-batch fermentation involving multiple feeds using differential evolution. Process Biochemistry, 39, 1709–1721.CrossRefGoogle Scholar
  9. 9.
    Kroumov, A., Modenes, A. N., & Tait, M. C. (2006). Development of new unstructured model for simultaneous saccharification and fermentation of starch to ethanol by recombinant strain. Biochemical Engineering Journal, 28, 243–255.CrossRefGoogle Scholar
  10. 10.
    Manikandan, K., & Viruthagiri, T. (2010). Kinetic and optimization studies on ethanol production from corn flour. International Journal of Chemical and Biological Engineering, 3, 65–69.Google Scholar
  11. 11.
    Murthy, G. S. (2006) Development of a controller for fermentation in the dry grind corn process. Dissertation. Urbana-Champaign, IL: Agricultural and Biological Engineering, University of Illinois.Google Scholar
  12. 12.
    Murthy, G., & Singh, V. (2011). A dynamic optimal controller for control of fermentation processes. US Patent Office US Patent No. 7,862,992.Google Scholar
  13. 13.
    Murthy, G., Johnston, D., Rausch, K., Tumbleson, M., & Singh, V. (2011). A simultaneous saccharification and fermentation model for dynamic growth environments. Bioprocess and Biosystems Engineering. doi:10.1007/s00449-011-0625-9.Google Scholar
  14. 14.
    Murthy, G., Johnston, D., Rausch, K., Tumbleson, M., & Singh, V. (2011). Starch hydrolysis modeling: Application to fuel ethanol production. Bioprocess and Biosystems Engineering. doi:10.1007/s00449-011-0539-6.Google Scholar
  15. 15.
    Muthuswamy, K., & Srinivasan, R. (2003). Phase-based supervisory control for fermentation process development. Journal of Process Control, 13, 367–382.CrossRefGoogle Scholar
  16. 16.
    Patnaik, P. R. (2003). An integrated hybrid neural system for noise filtering, simulation and control of a fed-batch recombinant fermentation. Biochemical Engineering Journal, 15, 165–175.CrossRefGoogle Scholar
  17. 17.
    Roy, S., Gudim, R., Venkatesh, K., & Shah, S. (2001). Optimal control strategies for simultaneous saccharification and fermentation of starch. Process Biochemistry, 36, 713–722.CrossRefGoogle Scholar
  18. 18.
    Sage, A. P. (1968). In: Optimum systems control (2nd ed., pp. 14–19, 42–49, 64–66, 395–402). Englewood Cliffs, NJ: Prentice Hall.Google Scholar
  19. 19.
    Sainz, J., Pizarro, F., Perez-Correa, J. R., & Agosin, E. (2003). Modeling of yeast metabolism and process dynamics in batch fermentation. Biotechnology and Bioengineering, 81, 818–828.CrossRefGoogle Scholar
  20. 20.
    Shapouri, H., & Gallagher, P. (2005). USDA’s 2002 ethanol cost of production survey. Agricultural economic report number 841, United States Department of Agriculture.Google Scholar
  21. 21.
    Srichuwonga, S., Fujiwaraa, M., Wanga, X., Seyamaa, T., Shiromaa, R., Arakanea, M., et al. (2009). Simultaneous saccharification and fermentation (ssf) of very high gravity (vhg) potato mash for the production of ethanol. Biomass Bioenergy, 33, 890–898.CrossRefGoogle Scholar
  22. 22.
    Straight, J. V., & Ramakrishna, D. (1994). Cybernetic modeling and regulation of metabolic pathways. Growth on complementary nutrients. Biotechnology Progress, 10, 574–587Google Scholar
  23. 23.
    Szederkènyi, G., Kristensen, N. R., Hangos, K. M., & Jørgensen, S. B. (2002). Nonlinear analysis and control of a continuous fermentation process. Computers and Chemical Engineering, 26, 659–670.CrossRefGoogle Scholar
  24. 24.
    Tan, T., Zhang, M., & Gao, G. (2003). Ergosterol production by fed-batch fermentation of Saccharomyces cerevisiae. Enzyme Microbial Technology, 33, 366–370.CrossRefGoogle Scholar
  25. 25.
    Verduyn, C., Postma, E., Scheffers, W. A., & van Dijken, J. P. (1990). Energetics of Saccharomyces cerevisiae in anaerobic glucose-limited chemostat cultures. Journal of General Microbiology, 136, 405–412.Google Scholar
  26. 26.
    Wu, W., & Wang, P. (1993). On-line optimal control for ethanol production. Journal of Biotechnology, 29, 257–266.CrossRefGoogle Scholar
  27. 27.
    Zhang, Y. (2011). Fate of lysine during bioprocess of making ddgs. Kansas City, MO: Symp Distillers Grains Tech.Google Scholar

Copyright information

© Springer Science+Business Media, LLC (outside the USA)  2011

Authors and Affiliations

  • Ganti S. Murthy
    • 3
  • David B. Johnston
    • 1
  • Kent D. Rausch
    • 2
  • M. E. Tumbleson
    • 2
  • Vijay Singh
    • 2
  1. 1.Eastern Regional Research CenterARS, USDAWyndmoorUSA
  2. 2.Department of Agricultural and Biological EngineeringUniversity of IllinoisUrbanaUSA
  3. 3.Biological and Ecological EngineeringOregon State UniversityCorvallisUSA

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