Fault Diagnosis in a Yeast Fermentation Bioreactor by Genetic Fuzzy System

Document Type: Research Article


Faculty of Chemical and Petroleum Engineering, Sharif University of Technology, P.O. Box 11365-9465 Tehran, I.R. IRAN


In this paper, the fuzzy system has been used for fault detection and diagnosis of a yeast fermentation bioreactor based on measurements corrupted by noise. In one case, parameters of membership functions are selected in a conventional manner. In another case, using certainty factors between normal and faulty conditions the optimal values of these parameters have been obtained through the genetic algorithm. These two cases are compared based on their performances in fault diagnosis of a yeast fermentation bioreactor for three different conditions. The simulation results indicate that the fuzzy-genetic system is superior in multiple fault detection for the conditions where the minimum and maximum deviations from normal conditions occur in the process variables.


Main Subjects

[1] Venkatasubramanian V., Rengaswamy R., Yin K., Kavuri S.N., A review of Process Fault Detection and Diagnosis Part I: Quantitative Model Based Methods, Computers and Chemical Engineering 27, p. 293 (2003).
[2] Cui J., Wang Sh., Model-Based Online Fault Detection and Diagnosis Strategy for Centrifugal Chiller Systems, International Journal of Thermal Sciences, 44, p. 986 (2005).
[3] Sotomayor O.A..Z., Odloak D., Observer-Based Fault Diagnosis in Chemical Plants, Chemical Engineering Journal, 112, p. 93 (2005).
[4] Nelly Olivier-Maget, Gilles Hetreux, Jean Marc Le Lann, Marie Veronique Le Lann, Model-Based Fault Diagnosis for Hybrid Systems: Application on Chemical Processes, Computers and Chemical Engineering, 33, p. 1617 (2009).
[5] Venkatasubramanian V., Kavuri R.S.N., Yin K., A Review of Process Fault Detection and Diagnosis III: Process History Based Methods, Computers and Chemical Engineering, 27, p. 327 (2003).
[6]  Genovesi A., Harmand J., Steyer J., A fuzzy Logic Based Diagnosis System for the On-Line Supervision of an Anaerobic Digestor Pilot-Plant, Biochemical Engineering Journal, 3, p. 171 (1999).
[7] Eslamloueyan R., Shahrokhi M., Bozorgmehry R., Multiple Simultaneous Fault Diagnosis Via Hierarchical and Single Artificial Neural Networks, Scientia Iranica, 10, p. 300 (2003).
[8]  He X.B., Yang Y.P., Yang Y.H., Fault Diagnosis Based on Variable-Weighted Kernel Fisher Discriminant Analysis, Chemometrics and Intelligent Laboratory Systems, 93, p. 27 (2008).
[9]  Detroja K.P., Gudi R.D., Patwardhan S.C., Plant Wide Detection and Diagnosis Using Correspondence Analysis, Control Engineering Practice, 15, p. 1468 (2007).
[10] Cen Nan, Faisal Khan, M. Tariq Iqbal, Real-Time Fault Diagnosis Using Knowledge-Based Expert System, Process safety and environmental protection, 86, p. 55 (2008).
[11] Zadeh L.A., Making Computers Think Like People, IEEE, 8, p. 26 (1984).
[12] Binaghi E., GalloI., Ghiselli C., Levrini L., Biondi K., An Integrated Fuzzy Logic and Web-Based Framework for Active Protocol Support, International Journal of Medical Informatics, 77, p. 256 (2008).
[13] Sheikhzadeh M., Trifkovic M., Rohani S., Fuzzy Logic and Rigid Control of a Seeded Semi-Batch, Anti-Solvent, Isothermal Crystallizer, Chemical Engineering Science, 63, p. 991 (2008).
[14] Ghoush M.A., Samhouri M., Al-Holy M., Herald T., Formulation and Fuzzy Modeling of Emulsion Stability and Viscosity of a Gum-Protein Emulsifier in a Model Mayonnaise System, Journal of Food Engineering, 84, p. 348 (2008).
[15] Mao Y., Xia Y., Yin Z., Sun Y., Wan Z., Fault Diagnosis Based on Fuzzy Support Vector Machine with Parameter Tuning and Feature Selection, Chinese Journal of Chemical Engineering, 15, p. 233 (2007).
[16] Boozarjomehry R.B., Masoori M., Which Method is Better for the Kinetic Modeling: Decimal Encoded or Binary Genetic Algorithm?, Chemical Engineering Journal, 130, p. 29 (2007).
[17] Nagy Z.,  Model  Based  Control  of  a  Yeast Fermentation Bioreactor Using Optimally Designed Artificial Neural Networks, Chemical Engineering Journal, 127, p. 95 (2007).
[18] Pham H.T.B., Larsson G., Enfors S.O., Modeling of Aerobic Growth of Saccharomyces Cerevisiae in a pH-Auxostat, Bioprocess Engineering, 20, p.537 (1999).