ABSTRACT
In this contribution, the advantages of the artificial neural network approach to the identification and control of a laboratory-scale biochemical reactor are demonstrated. It is very important to be able to maintain the levels of two process variables, pH and dissolved oxygen (DO) concentration, over the course of fermentation in biosystems control. A PC-supported, fully automated, multi-task control system has been designed and built by the authors. Forward and inverse neural process models are used to identify and control both the pH and the DO concentration in a fermenter containing a Saccharomyces cerevisiae based-culture. The models are trained off-line, using a modified back-propagation algorithm based on conjugate gradients. The inverse neural controller is augmented by a new adaptive term that results in a system with robust performance. Experimental results have confirmed that the regulatory and tracking performances of the control system proposed are good.
Subject(s)
Algorithms , Bioreactors/microbiology , Cell Culture Techniques/instrumentation , Cell Culture Techniques/methods , Hydrogen-Ion Concentration , Neural Networks, Computer , Oxygen/metabolism , Saccharomyces cerevisiae/physiology , Computing Methodologies , Equipment Design , Equipment Failure Analysis , Feedback/physiology , Models, Biological , Oxygen Consumption/physiology , Saccharomyces cerevisiae/chemistryABSTRACT
The growth and activity of formate dehydrogenase (FDH) in five methanol-utilizing yeasts at various methanol concentrations were investigated. The parameters observed were inhibited at 4% methanol concentration in the medium. For Candida boidinii and Pichia trehalophila FDH activity was not found. The highest value was detected for Pichia lindneri (0.14 U/mg protein).
