PPT 206 Instrumentation, Measurement and Control
SEM 2 (2012/2013)
Dr. Hayder Kh. Q. Ali
[email protected]
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1- Bioprocesses:
The bioprocess may be defined as any process that
uses complete living cells or their components (e.g.,
enzymes, chloroplasts) to effect desired physical or
chemical changes. In other words, a bioprocess consists
of a cell culture in a bioreactor, which is a process able to
create an optimal growth environment.
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Cells and cell cultures
The central object of a bioprocess is the cell. A living cell is a
highly complex system which is often defined as the smallest
autonomous biological unit. Its main tasks are to maintain itself
alive, to reproduce and to manage itself. So, it is able to build its
own constituents and to provide its own energy through physical
and chemical processes which constitute the cell metabolism. This
latter consists of a network of thousands interconnecting reactions,
the metabolic pathways, which are catalyzed by enzymes and
accurately controlled by regulation processes.
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The autonomy properties of cells allow to think of growing
dissociated cells outside their natural environment. Such cell
cultures find many applications in biotechnology industry.
Indeed, bacteria, yeast or animal cell cultures allow the synthesis
of numerous products of interest for food or pharmaceutical
sectors: vaccines, antibiotics, antibodies, wine, beer, industrial
alcohols, yeast or enzymes for food technology. Moreover, some
intervene in waste treatment and pollution control.
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However, these applications require the use of a
bioreactor. Indeed, such a system favours cell growth by
creating a good environment. It monitors and controls the
cell environmental conditions like gas flow rates,
temperature, pH, dissolved oxygen level and agitation
rate.
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Bioreactor
The most common bioreactor used in industry is the
classical stirred tank reactor as the one described in figure 1.
The central part of the reactor is the tank containing the
growing cells in their culture medium. All the peripheral
devices are used to control and monitor the cellular growth
and production.
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Figure 1: Schematic description of a perfectly stirred bioreactor
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A typical bioreactor involves the following control
processes: pH is supervised through a pH probe and controlled
by addition of acid or base into the reactor, temperature is
monitored by a thermocouple and controlled thanks to a heat
exchanger, dissolved oxygen is observed by a probe and
controlled by agitation rate and/or air flow and /or gas
composition.
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Besides
these
environmental
considerations,
culture strategies maybe used to control the nutrient
availability. In a batch process, all the culture medium
is directly available to the cell and no medium is added
or withdrawn during the culture. A fed-batch process is
characterized by an addition of culture medium during
the culture thanks to a predefined or a controlled flow
rate.
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In a continuous culture mode, fresh culture medium is
added while the culture is continuously withdrawn.
Finally, an alternative to the continuous culture mode is
the perfusion mode where culture medium is added and
withdrawn whereas the cells are maintained in the
bioreactor.
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Bioprocess modeling
In order to improve process understanding or performance,
different automatic tools can be developed: simulators able to
reproduce system behaviors, software sensors which allow to
obtain an estimation of an unmeasured signal or controllers to
maintain optimal conditions. All these tools rely on a
representation of the considered system, a mathematical model.
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Such a model may come in various shapes and be
phrased with varying degrees of mathematical
formalism. The intended use determines the degree of
sophistication that is required to make the model
purposeful.
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Different
kinds
of
bioprocess
models
are
distinguishable according to their possible biological
interpretation or their level of complexity. However,
before describing the most widespread bioprocess
models, let us remind some general characteristics of a
dynamic model.
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General characteristics of models
Linear or nonlinear
A linear model has to satisfy the superposition principle, i.e.
any linear combination of model inputs (and state initial
conditions) corresponds to the same linear combination of
the states or the outputs. In brief, if the system functions can
be represented entirely by linear equations, then the model is
known as a linear model.
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If one or more of these functions are represented with a
nonlinear equation, then the model is a nonlinear model.
Actually, for more advanced applications (especially
bioprocesses), many models are nonlinear.
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Static or dynamic
A static model does not take the element of time into account
unlikea dynamic model. Dynamic models are typically
represented by mathematical expressions like differential
equations in order to describe the dynamic evolution of state
variables (like cell growth, substrate consumption or product
formation).
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Lumped or distributed parameters
The parameters of a model are lumped when the model is
homogeneous: the time is the unique independent
variable. When some state varies within the system, the
time is no more the unique independent variable, the
model is heterogeneous and the parameters are
distributed.
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distributed
parameter
models
are
typically
represented by partial differential equations. In
bioprocess,
lumped
parameters
are
often
preferred because they assume a perfectly stirred
bioreactor.
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Continuous or discrete
Many models are continuous; their independent
variables are considered to be defined for any real
values of time. However, measured data are usually
obtained through discrete sampling which implies
measurements at discrete time instants
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White or black box
Black boxes are models without physical interpretation. They
are usually used when no a priori information about the
system is available. White boxes involve a model structure
that allows physical interpretations. An example of black box
models used in bioengineering is the Artificial Neural
Networks.
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As for white boxes, they usually rely on mass balances
involving substrate inputs, accumulation and dilution terms
as well as kinetics described by activation, inhibition and
saturation coefficients. Between these two extremes, there
exist grey boxes which combine the principles of white and
black box models. An example of such a kind of model are
the hybrid models which replace some model elements by
neural networks.
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Characteristics of bioprocess models
The classification presented here was proposed in Tsuchiya et
al. (1966). However, it is still often used in the literature (in
Mu et al. (2005), for instance).
The authors introduce two new characteristics for bioprocess
models.
First, a model can be structured or unstructured
depending on whether it describes intracellular characteristics
of the cell (metabolic reactions, cellular processes etc.) or
considers the cell like an entity without internal structure.
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Characteristics of bioprocess models
There are two new characteristics for bioprocess models. First, a
model can be structured or unstructured depending on whether it
describes intracellular characteristics of the cell (metabolic reactions,
cellular processes etc.) or considers the cell like an entity without
internal structure. Second, a model can be segregated or unsegregated
depending on whether it considers or not the heterogeneity of the
cellular population, the position in the cell cycle for example. The
choice among these properties depends on the objective of the model.
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Such reaction rates vary with time and are usually
influenced by many physicochemical and biological
environmental factors like substrate, biomass and product
concentrations as well as pH, temperature, dissolved
oxygen concentration or various microbial growth
inhibitors.
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Parameter estimation
The selection of an appropriate model structure is essential for
modeling
engineering
processes.
Nevertheless,
the
model
parameters within the structure are fundamental ingredients and
therefore not less important. Once the structure is selected, the
unknown parameters have to be determined. This is usually done
by optimization of a criterion that evaluates the agreement of the
model output with some information from experimental studies.
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Optimization criteria
The aim of optimization is the determination of a set of
variables (parameters) that leads to the best model.
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