Monitoring and Control Strategies for Ethanol Production in Saccharomyces Cerevisiae

Authored by: Gilles Roux , Zetao Li , Boutaib Dahhou

Fermentation Microbiology and Biotechnology

Print publication date:  December  2011
Online publication date:  December  2011

Print ISBN: 9781439855799
eBook ISBN: 9781439855812
Adobe ISBN:

10.1201/b11490-18

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Abstract

Fermentation processes are dynamic (i.e., nonlinear and nonstationary) in nature, thus leading to many difficulties in modeling. In addition, the lack of specific sensors makes certain variables inaccessible by online estimation. Measuring the state variables (the smallest possible subset of system variables that can represent the entire state of a dynamical system) is difficult, especially online, and its reliability is uncertain. The difficulties encountered by the control engineer, when studying this type of process, result from the fact that the process model is based on nonlinear algebraic relations and differential equations. These difficulties make the biotechnological process an excellent field for application of advanced automatic control tools (Dahhou et al. 1991a). All available information on the process [e.g., environmental variables (temperature, pH, stirring, etc.)] and control loop signals must be exploited to ensure that the process is operating reliably under the given conditions. For fermentation processes, supervision becomes an issue of primary importance for increasing the reliability, availability, and safety of these systems. Unfortunately, the lack of process knowledge, the absence of reliable sensors, and the unpredictable behavior of microorganisms make this task very difficult, sometimes impossible, for the human operator. Several schemes of supervision applied to various domains (chemical process, petroleum process, wastewater treatment process, etc.) have been proposed (Antsaklis and Passino 1993; Aguilar-Martin 1996; Dojat et al. 1998). However, all of these can be described in general by the scheme shown in the Figure 17.1.

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