One-line monitoring, state and parameter estimation, adaptive/computer control and dynamic optimization of a continuous bioreactor
Date
1993
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Doctoral
Abstract
In this research program, various important aspects for computer-based adaptive
control and optimization strategy for a continuous bioreactor, have been investigated.
The study was carried out in the following four phases: (a) development of a new
method for on-line monitoring of biomass concentration; (b) measurement of kinetics
of growth of S.cerevisiae; (c) dynamic bioreactor simulation studies; and (d)
experimental control of a continuous bioreactor.
An interesting observation was made in absorption of light by yeast cells at
"high" concentrations leading to a new equation: log (T/T0) = K log (C/C0), while
developing a suitable method for on-line monitoring of yeast cell concentrations. A
consistent theoretical explanation was developed starting from the fundamental
assumptions of Beer-Lambert's law. This equation was shown to be valid for several
optically sensitive solutions [with negative deviations] and thus has the potential of
becoming a law. The underlying reasoning behind this phenomenon may improve our
present day understanding about absorption of electromagnetic radiation by various substances.
Based on this new concept, a novel spectrophotometric technique has been
developed and successfully implemented for on-line monitoring of a wide range of yeast cell concentrations in a continuous bioreactor [which has been considered a difficult task
in the literature due to lack of reliable instrumentation]. To the author's knowledge, this
is the first successful method for on-line monitoring of "high" biomass concentrations
which could be implemented for process control applications. This approach may lead
to a new generation of instruments in spectroscopy.
Extensive batch and continuous experiments were carried out on a well-defined
medium using S. cerevisiae at different initial glucose concentrations. The biomass yield
was found to be a function of the inhibitory environment of the bioreactor. Four new
correlations have been proposed to explain the inhibitory kinetics of ethanol
fermentation. These experimental results are expected to have a significant influence in
formulating the fermenter design variables and control strategy for optimizing the
productivity of ethanol fermentation process.
Based on extensive simulation studies, an algorithm [called the SE algorithm]
was successfully formulated using state equations: (a) for on-line estimation of important
unmeasurable states and critical time-varying parameters; and (b) for adaptive control
and dynamic optimization of a bioprocess. Based on simulation studies, a numerical
technique was also developed to improve the convergence of the extended Kalman filter algorithm.
The SE algorithm was implemented for on-line state estimation and dynamic
optimization of a lab-scale [450 mL working volume] continuous ethanol fermenter. An
IBM PC along with an OPTO board were used for on-line data acquisition and for
execution of the optimization algorithm. A number of experiments were carried out to verify the performance and true adaptive nature of the algorithm. The experimental
results clearly illustrate the successful development and implementation of computer-based
adaptive control and dynamic optimization strategies to a continuous bioprocess.
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Degree
Doctor of Philosophy (Ph.D.)
Department
Chemical Engineering
Program
Chemical Engineering