The Process of Industrial Bioethanol Production Explained by Self-Organised Maps

  • Miguel A. Sanz-Bobi
  • Pablo Ruiz
  • Julio Montes
Conference paper
Part of the Intelligent Systems, Control and Automation: Science and Engineering book series (ISCA, volume 61)


Bioethanol is produced on an industrial scale by means of fermentation of a sugar substrate by Saccharomyces cerevisiae. Models for the detection of anomalies and their possible evolution are difficult to elaborate due to the biological nature of the fermentation process. This paper describes a method able to characterize patterns for explaining industrial bioethanol production using self-organised maps. Also, this method allows for an estimation of the probabilities of evolution to any pattern that the process may have from its last recognized state, therefore helping to take measures to correct a possible problem as soon as possible.


Fermentation Process Succinic Acid Distillation Column Condensed Fraction Fermentation Tank 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors are very grateful to the entire staff of the Bioetanol Galicia plant and ABENGOA for their valuable collaboration during all this work. This project has been partially supported by the research program PROFIT promoted by the Spanish Government.


  1. 1.
    Ingledew WM (2004) Alcohol production by Saccharomyces cerevisiae: a yeast primer. In: Jacques K, Lyons TP, Kelsall DR (eds) The alcohol text book, 3rd edn. Nottingham University Press, Nottingham, pp 49–87Google Scholar
  2. 2.
    Ccopa Rivera E, Farias F Jr, Pires Atala DI, Ramos de Andrade R, Carvalho da Costa A, Maciel R (2009) Development and implementation of an automated monitoring systems for improved bioethanol production. Chem Eng Trans 18:445–450Google Scholar
  3. 3.
    García MC, Sanz-Bobi MA, del Pico J (2006) SIMAP: intelligent system for predictive maintenance: application to the health condition monitoring of a windturbine gearbox. Comput Ind 57(6):552–568CrossRefGoogle Scholar
  4. 4.
    Kohonen T (1990) The self-organizing map. Proc IEEE 78(9):1464–1480CrossRefGoogle Scholar
  5. 5.
    Kohonen T, Oja E, Simula O, Visa A, Kangas J (1996) Engineering applications of the self-organizing map. Proc IEEE 84(10):1358–1384CrossRefGoogle Scholar
  6. 6.
    Ccopa Rivera E, Candida Rabelo S, dos Reis GD, Maciel Filho R, Carvalho da Costa A (2010) Enzymatic hydrolysis of sugarcane bagasse for bioethanol production: determining optimal enzyme loading using neural networks. J Chem Technol Biotechnol 85:883–992Google Scholar
  7. 7.
    Emmanuel AN, David A, Benjamin YK, Yannick ET (2009) A hybrid neural network approach for batch fermentation simulation. Aust J Basic Appl Sci 3(4):3930–3936Google Scholar
  8. 8.
    Eyng E, da Silva FV, Palú F, Fileti AMF (2009) Neural network based control of an absorption column in the process of bioethanol production. Int J Braz Arch Biol Technol 52(4):961–972CrossRefGoogle Scholar
  9. 9.
    Box GP, Jenkins GM, Reinsel GC (2008) Time series analysis. Forecasting and control, 4th edn. Wiley series in probability and statistics. Wiley, ChichesterGoogle Scholar
  10. 10.

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Miguel A. Sanz-Bobi
    • 1
  • Pablo Ruiz
    • 1
  • Julio Montes
    • 1
  1. 1.IIT- Institute of Technological Research, Engineering SchoolComillas Pontifical UniversityMadridSpain

Personalised recommendations