Post on 14-Apr-2018
7/27/2019 Bioprocess Modeling and Optimization-Part 1.pptx
1/13
Yusuf Hendrawan, STP. M.App. Life Sc.,
Ph.D
Bioprocess Modeling and Optimization
(Part-1)
7/27/2019 Bioprocess Modeling and Optimization-Part 1.pptx
2/13
Characteristics of food and bioprocesses
(1) They often involve drastic physical, chemical,and biological transformation of the material,during processing.
(2) Many of the transformations have not been
characterized, primarily because of the following:a) such a large variety of possible materials; b)their biological origin, variabilities are significant,even in the same material; c) because thematerial contains large amount of water, unlesstemperatures are low, there is always evaporationin the food matrix. This evaporation is hard tohandle in physics based models and increasescomplexity of the process; d) many food
processes involve coupling of different physicse. . microwave heatin involves heat transfer
7/27/2019 Bioprocess Modeling and Optimization-Part 1.pptx
3/13
Real-world
problem
Mathematical
model
Solution to
model
Solution to
real-world
problem
assumptions,
abstraction,data,simplifications
optimization
algorithm
interpretation
makes sense? change
the model,
assumptions?
A schematic view of modeling and optimization
7/27/2019 Bioprocess Modeling and Optimization-Part 1.pptx
4/13
What is a model? A model is an analog of a physical reality, typically
simpler and idealized. Models can be physical ormathematical and are created with the goal to gaininsight into the reality in a more convenient way.
Advantages:
(1) reduction of the number of experiments, thusreducing time and expenses;
(2) providing great insight into the process that may notbe possible with experimentation;
(3) process optimization; (4) predictive capability i.e. ways of performing what if
scenarios;
(5) providing improved process automation and controlcapabilities.
7/27/2019 Bioprocess Modeling and Optimization-Part 1.pptx
5/13
Need for understanding the
detailed mechanism
Availability of time and
resources, depending on the
state of a-priori knowledge of
the physiscs
Use fundamental lawsto develop physics-
based model
Validate model against
experimental data
Obtained experimental datato develop observation-based
model
Possibly validate against
additional experimental data
Extract knowledge from the
model using sensitivity
analysis
Use model in optimization
and control
Not reall
y
necessary
constrained
Strong need
available
Use
7/27/2019 Bioprocess Modeling and Optimization-Part 1.pptx
6/13
Modeling of Bioprocesses
Physics-basedObservation-
based
Techniques that can
be useful in either
model
Microscalee.g. Mol. Dynamics
Mesoscale
Macroscale
Fluid flow
Heat
transfer
Mass
transferHeat &
Mass
transfer
Classical
Statistical
Data
driven
Data
mining
Neural
Network
GeneticAlgorithm
Fractal
Analysis
Fuzzy
Logic
Response
surface
methodMultivariate
Analysis
Monte-
Carlo
Dimension
al AnalysisLinear
Programmi
ng
Kinetics
7/27/2019 Bioprocess Modeling and Optimization-Part 1.pptx
7/13
Physics-basedPhysics-based models follow fundamental physical laws
such as conservation of mass and energy and Newtons
laws of motion; however, empirical rate laws are needed to
apply the conservation laws at the macroscopic scale. For
example: to obtain temperatures using a physical-based
model, combine of energy with Fouriers law.
The biggest advantages of physics-based models are that
they provide insight into the physical process in a manner
that is more precise and more trustable, and the
parameters in such models are measurable, often using
available techniques.
The Advantages:
(1) They can be exact analogs of the physical process;
(2) They allow in-depth understanding of the physical
process as opposed to treating it as a black box;
(3) They allow us to see the effect of changing parametersmore easily.
The Disadvantages:
(1) High level of specialized technical background is
required;
(2) Generally more work is required to apply to real-life
problems;(3) Often longer development time and more resources are
7/27/2019 Bioprocess Modeling and Optimization-Part 1.pptx
8/13
Macroscale
Fluid flow
Heat
transfer
Mass
transferHeat &
Mass
transfer
Macroscale models primarily deal with transport
phenomenoa, i.e. fluid flow, heat transfer, and mass
transfer. These physicsbased models based on
fundamental physical laws. Typically, these models consist
of a governing equation that describes the physics of the
process along with equations that describe the condition at
the boundary of the system.
KineticsKinetic models mathematically describe rates of chemical
or microbiological reactions. They generally can be
considered to be physics-based. However, in complex
chemical and microbiological processes, as is true for food
and bioprocesses, the mechanisms are generally hard toobtain and are not always available. The kinetic models for
such systems are more data-driven than fundamental.
7/27/2019 Bioprocess Modeling and Optimization-Part 1.pptx
9/13
Observation-
based
The physics-based modeling process described before
assumes that a model is known, which is frequently difficult
to achieve in complex processes. Although a physics-
based model may also be adjusted based on measured
data, observation-based models are inferred primarily from
measured data. Observational models are black box
models to different degrees in relation to physics of the
process.
Physics-based models often require more specialized
training and/or longer development time. In some
application, detailed understanding provided by the
physics-based model may not be necessary. For example,
in process control, detailed physics-based models often are
not needed, and observation-based models can suffice.
Observation-based models can be extremely powerful in
providing a practical, useful relationship between input andoutput parameters for complex processes.ClassicalStatistical
The classical statistical models can have a model in mind
before obtaining the measured data. This makes them less
of a black box than models such as neural network or
genetic algorithm that are frequently completely data
driven., no prior assumption is made about the model and
no attempt is made to physical interpret the model
parameters once the model is built.
7/27/2019 Bioprocess Modeling and Optimization-Part 1.pptx
10/13
Response
surface
method
This is statistical technique that use regression analysis to
develop a relationship between the input and output
parameters by treating it as an optimization problem. This
method is quite popular in food applications.Multivariate
Analysis
MVA is a collection of statistical procedures that involve
observation and analysis of multiple measurements madeon one or several samples of items. MVA techniques are
classified in two categories: dependence and
interdependence methods.
In a dependence technique the dependent variable is
predicted or explained by independent variables.
In an Interdependence technique are not used forprediction purposes and are aimed at interpreting the
analysis output for the best representative model.
7/27/2019 Bioprocess Modeling and Optimization-Part 1.pptx
11/13
NeuralNetwork
Genetic
Algorithm
Fractal
Analysis
Fuzzy
Logic
Data mining refers to automatic searching of large volumes of
data to establish relationships and identify patterns. To do this,
data mining uses statistical techniques and other computing
methods such as machine learning and pattern recognition. It
can be seen as a meta tool that can combine a number of
modeling tools.
Data
mining
An Artificial Neural Network Model (as opposed to a biologicalneural network) is an interconnected group of functions
(equivalent to neurons or nerve cells in a biological system) that
can represent complex input-output relationships. The power of
neural networks lies in their ability to represent both linear and
nonlinear relationships and in their ability to learn these
relationships directly from the modeled data.Genetic Algorithm are search algorithms in a combinational
optimization problem that mimic the mechanism of the biological
evolution process based on genetic operators.
Fractal analysis uses the concepts from fractal geometry. It has
been primarily used to characterize surface microstructure (such
as roughness) in foods and to relate properties such as texture,oil absorption in frying, or the Darcy permeability of a gel to the
microstructure.Fuzzy logic is derived from fuzzy set theory that permits the
gradual assessment of the membership of elements in relation
to a set in contrast to the classical situation where an element
strictly belongs or does not belong to a set. It seems to be
successful for processes in which human reasoning and
7/27/2019 Bioprocess Modeling and Optimization-Part 1.pptx
12/13
7/27/2019 Bioprocess Modeling and Optimization-Part 1.pptx
13/13