Skip to main navigation menu Skip to main content Skip to site footer

Approach to automation of a process of yeast inoculum production on industrial scale for ethanol production

Abstract

The results of an applied research for automation the stage of reproduction of Saccharomyces cerevisiae yeas to produce ethanol, are presented in this paper. The identification of the variables to be instrumented, the instrumentation requirements and the proposed control scheme are based on the analysis of the functioning and operation of the process.

Keywords

aerobic fermentation, instrumentation, control

PDF (Español)

References

  • Acevedo, S., Llano, F., Ochoa, J., Parra, J., Calero, L., Caballero, M., Figueroa, M., Marriaga, N. & Vallejo, R., (2005). Producción de alcohol carburante – Praj. Cenicaña.
  • Cenicaña. Alford, J. S. (2006, Sep.). Bioprocess control: Advances and challenges. Computers & Chemical Engineering, 30(10-12), 1464-1475.
  • Ashoori, A. Ghods, H. & Khaki-sedigh, A. (Dec. 2008). Model Predictive Control of a Nonlinear Fed- Batch Fermentation Process. Control, Automation, Robotics and Vision, 2008. ICARCV 2008. 10th International Conference, p. 17-20.
  • Atsushi, V. & Aoyama, V. (1995, Dec.). Internal Model Control Framework Using Neural Networks. Engineering Applications of Artificial Intelligence, 8 (6), 689–70.
  • Becker, T., Hitzmann, B., Ralf, K. M., Reardon, K. F., Stah,F. l, & Ulber, R. (2007). Future Aspects of Bioprocess Monitoring. Adv iochem Engin/Biotechnol, 249-293.
  • Dochain D. & Bastin, G. (1990). On-line estimation and adaptive control of bioreactors. USA: Elsevier Science
  • Dovzan, D. & Skrjanc, I. (2010). Predictive functional control based on an adaptive fuzzy model of a hybrid semi-batch reactor. Control Engineering Practice, 18, 979-989.
  • Echeverry, M. R., Álvarez, H. & Quintero, O. (2003).Control de un biorreactor para fermentación alcohólica en continuo. Bogotá: Universidad Nacional de Colombia.
  • Harms, P., Kostov, Y. & Rao, G. (2002). Bioprocess monitoring. Current Opinion in Biotechnology, 124127.
  • Hernández, C., Escobar, A. & Galvis, J. (2003, marzo).Control difuso adaptativo aplicados a procesos de fermentación. Rev. Fac. Ing. Univ. Antioquia. (58), 105-113.
  • Jiang, J., Fan, S., Kong, B., Cai, H., Guo, M. & Zhuo, V. (2009). Bioprocess Automation Using New Online Sensors. Yeast, 910-914.
  • Lam, H. & Kostov, Y. (2010). Optical Instrumentation for Bioprocess Monitoring. Optical Sensor Systems in Biotechnology Advances in Biochemical Engineering /Biotechnology. 116, 125-142.
  • Lederberg, J. & Schaechter, M. (2004). The Desk Encyclopedia of Microbiology. San Diego, CA: Academic Press.
  • Mantovaneli, I. C. C. & Filho, R. M. (1992). Hybrid Neural Network Model For Alcoholic. Chemical Engineering, 1-10.
  • Meleiro, L., Von Zuben, F. J. & Filho, R. M. (2009). Constructive learning neural network applied to identification and control of a fuel-ethanol fermentation process. Engineering Applications of Artificial Intelligence, 22( 2), 201-215.
  • Merchuk, J. C. (1988). Microbiología Industrial. Organización de los Estados Americanos.
  • Quintero, O., Amicarelli, A. & Scaglia, G. (2009). Control based on numerical methods and recursive bayesian estimation in a continuous alcoholic fermentation process. Bioresources. 4, 1372-1395.
  • Thatipamala, S. R. R. & Hill, G.A. (1993). On-line State and Parameter Estimation and Adaptive Optimization of a Continuous Bioreactor (Ethanol Fermentation ) Using State Equations. American Control Conference. 905909.

Downloads

Download data is not yet available.