Fault Diagnosis in a Yeast Fermentation Bioreactor by Genetic Fuzzy System

Authors

  • Mohammad Shahrokhi Faculty of Chemical and Petroleum Engineering, Sharif University of Technology, P.O. Box 11365-9465 Tehran, I.R. IRAN
  • Ramin Bozorgmehry Boozarjomehry Faculty of Chemical and Petroleum Engineering, Sharif University of Technology, P.O. Box 11365-9465 Tehran, I.R. IRAN
  • Shokoufe Tayyebi Faculty of Chemical and Petroleum Engineering, Sharif University of Technology, P.O. Box 11365-9465 Tehran, I.R. IRAN
Abstract:

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.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

fault diagnosis in a yeast fermentation bioreactor by genetic fuzzy system

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 genet...

full text

Control of Yeast Fermentation Bioreactor in Subspace

PCA based temperature controller was used to control Ethanol concentration produced in Yeast fermentation process. The controller was designed at a specific operating point and its disturbance rejection performances were studied. Substrate inlet temperature proved to be the most significant disturbance input from the analysis of open loop responses. Q-statistic (SPE) of process measurements con...

full text

Bioreactor-on-a Chip: Application to Baker’s Yeast Fermentation

This paper presents a miniaturized bioreactor (bioreactor-on-a-chip) applied to baker’s yeast fermentation. The bioreactor-on-a-chip is fabricated using a silicon and glass wafers applying micromachining technology (wet-etching techniques) in order to create microchannels, mixerchannels, microvalves. The miniaturization and integration allows smaller volumes to be used, which can be often rathe...

full text

A Fuzzy Rule Based System for Fault Diagnosis, Using Oil Analysis Results

    Condition Monitoring,   Oil Analysis, Wear Behavior,   Fuzzy Rule Based System   Maintenance , as a support function, plays an important role in manufacturing companies and operational organizations. In this paper, fuzzy rules used to interpret linguistic variables for determination of priorities. Using this approach, such verbal expressions, which cannot be explicitly analyzed or statistic...

full text

Enhanced Bioethanol Production in Batch Fermentation by Pervaporation Using a PDMS Membrane Bioreactor

The integration of batch fermentation and membrane-based pervaporation process in a membrane bioreactor (MBR) was studied to enhance bioethanol production compared to conventional batch fermentation operated at optimum condition. For this purpose, a laboratory-scale MBR system was designed and fabricated. Dense hydrophobic Polydimethylsiloxane (PDMS) membrane was used for pervaporation. For fer...

full text

Fuzzy Estimation of a Yeast Fermentation Parameters

The dynamics of fermentation processes are very complex and not completely known. Some state variables are nonmeasurable, and the process parameters are strongly time dependent. Recently, there are some control methods like fuzzy learning and neural networks, which are promising in dealing with non-linearity, complexity, and uncertainly of these processes. These methods are suitable for the mod...

full text

My Resources

Save resource for easier access later

Save to my library Already added to my library

{@ msg_add @}


Journal title

volume 29  issue 3

pages  61- 72

publication date 2010-09-01

By following a journal you will be notified via email when a new issue of this journal is published.

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023