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Industrial Microbiology INDM 4005 Lecture
7 18/02/04
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3. OPTIMIZATION OF FERMENTATION PROCESS

• Overview
• Fermenter design
• Process optimisation- Monitor and Control

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3.1. FERMENTOR DESIGN

• 3.1.1. Choice of reactor configuration depends
  on
• (a) BIOCATALYST
• Animal/ plant cells
• Microbial cells
• Growing
• Non-growing
• Enzymes
• Soluble
• Immobilised

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Choice of reactor configuration depends on
(b) Reactor configuration Batch, Semi-,
Continuous, Plug-flow Free, Immobilised (c)
Economics Value of product Degree of process
control Product parameters
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CASE STUDY Draw the major types of aerobic
fermenters Draw the major types of low shear
fermenters
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3.1.2. DESCRIPTION OF MAJOR FERMENTOR
CONFIGURATIONS
Laboratory vs Industrial scale Batch Continuous
Tower / loop, air-lift Plug-flow Immobilised
Geometry / shape Types of aerators and
agitators Generalised difference between
animal, plant and microbial cells
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Why control fermentations?

• Success of a fermentation depends on the
  maintenance of defined environmental conditions
  for biomass and product formation
• Therefore many criteria or parameters need to be
  kept in control
• Any deviations from optimum conditions need to be
  controlled and corrected by a control system

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Control systems
A control system consists of three basic
components 1. A measuring element (senses a
process property and generates a corresponding
output signal) 2. A controller (compares the
measurement signal with a pre-determined desired
value, the set point, and produces an output
signal to counteract any differences between the
two 3. A final control element, which receives
the control signal and adjusts the process by
changing a valve opening or pump speed causing
the controlled process to return to the set point
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3.2. PROCESS OPTIMIZATION THROUGH MONITOR AND
CONTROL
3.2.1. KEY OBJECTIVE ? Analyse process
status ? Establish optimum conditions MONITOR
Sampling, on-, off-line, state and control
variables, sensors, gate-way sensors,
biosensors MEASURE Factors significant in
sensing, measurement and display, data capture
and storage CONTROL Key variables controlled,
state and control / process variables, levels of
process control, automatic control
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3.2.2. MEASUREMENT - KEY PARAMETERS
? ACCURACY Ability to provide a signal related to
the true value of the stimulus ?
RESOLUTION Smallest change in stimulus to the
sensor which causes a significant change ?
SENSITIVITY Ratio of change in sensor output to
the corresponding change in the stimulus ?
DRIFT Variation in the output of a sensor
independent of change in the stimulus
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3.2.3. CONTROL SYSTEMS - general
Control system consists of 3 basic components 1.
A measuring element (e.g. a pH probe) 2. A
controller 3. A final control element CAN
BE Simple manual - control operator instructed
to observe and take corrective action Automatic -
signal sent from sensor to a controller, compared
with a reference value (set-point) value, signal
then relayed to a valve or motor (e.g.
turn-on) IF CONTROL BASED ON Event has occurred
FEED BACK CONTROL Premise that an event will
occur FEED FORWARD
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3.2.4. CONTROL SYSTEMS - application AT PLANT
LEVEL
1. SEQUENCING OPERATIONS Manipulating valves,
activating pumps 2. INDIVIDUAL CONTROL
LOOPS For example Temperature or pH control in
reactors 3. PROCESS OPTIMIZATION Monitoring
course of a fermentation and taking corrective
action.
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Automatic control systems
? Two position (e. g. on / off) ? Proportional
(effect/ action proportional to input) ?
Integral (effect is determined by integral of
input over time i.e. area under the
curve) ?Derivative ( change related to rate of
change of input signal i.e.slope of the curve)
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Manual controlSteam valve to regulate the
temperature of water flowing through a pipe
EXPENSIVE
Human operator instructed to control temperature
within set limits
Steam Valve (Final control element)
Visual awareness
Manual adjustment of valve
Thermometer
Water
Pipe
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Automatic controlSimple automatic control loop
for temperature control
Set-point
Controller
Steam Control Valve
Measured valve
Signal to operate valve
Thermocouple
Water
Pipe
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Automatic control systems
Can be classified into 4 main types 1.
Two-position controllers 2. Proportional
controllers 3. Integral controllers 4.
Derivative controllers
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(1) Two position controller
100 open (on) Valve or switch position 100
closed (off)
100 open (on) Valve or switch position 100
closed (off)
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(2) Proportional control
1. Output without control 2. Proportional
action 3. Integral action 4. Proportional
integral action 5. Proportional derivative
action 6. Proportional integral derivative
action
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Positive deviation Controlled
variable Set-point Negative deviation
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5, 6
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Time
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Automatic control
In complex control systems there are 3 different
methods which are commonly used in making error
corrections -proportional -integral -derivat
ive May be used singly or in combination With
electronic controllers the response to an error
is represented as a change in output current or
voltage
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A fermenter with a temperature-controlled
heating jacket
Temperature controller
Water outlet
Thermocouple
Pressure line to valve
Hot water
Heating Jacket
Pressure regulated valve
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Automatic control
Proportional control the change in output of the
controller is proportional to the input signal
produced by the environmental change Integral
control output signal of an integral controller
is determined by the integral of the error input
over the time of the operation Derivative
control when derivative control is applied the
controller senses the rate of change of the error
signal and contributes a component of the output
signal that is proportional to a derivative of
the error signal
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3.2.6. PROGRAMMABLE LOGIC CONTROLLER / CHIP (PLC)
Each has an input section, output section and a
central processing unit (CPU) ? Input- connect to
sensors ? Output - connected to motors / valves
etc. ? CPU - provides and executes
instructions May be linked to a Management
Information System (MIS) resulting in a database
of production data. A Laboratory Information
Management System (LIMS) can also be interfaced
giving all test data (e.g. info on tests carried
out on all samples) ADVANTAGE ? REPEATABILITY ?
TRACEABILITY
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CASE STUDY Briefly outline the benefits of LIMS
which contribute to sample handling (data /
information handling. Any other comments on
laboratory management?
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3.2.7. COMPUTERS IN FERMENTATION
3 Main areas of computer control ?LOGGING OF
PROCESS DATA Amount of data generated very great
- need electronic capture ?DATA ANALYSIS
Reduction of logged data Data reduction very
significant - generates trends (e.g.
graphs) Makes analysis, management of data
easier LIMS is a good example of the benefits
from this area Predictive Modelling and Expert
systems would be other examples ?PROCESS
CONTROL
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Computer-controlled fermenter with control loop
Mainframe computer
Analogue to digital converter
Printout
Dedicated mini-computer
Interface
VDU
Meter
Analogue to digital converter
Data store
Graphic unit
Reservoir
Pump
Clock
Alarms
Sensor
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3.2.7. COMPUTERS IN FERMENTATION
PROCESS CONTROL ? Digital Set-point Control
(DSC) Computer scans set-points of individual
controllers and takes corrective action when
deviations occur ? Direct Digital Control
(DDC) Sensors interfaced directly with the
computer
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3.2.8. CONTROL / PROCESS VARIABLES
1. Temperature 2. Pressure 3. Vessel
contents 4. Foam 5. Impeller speed 6. Gas Flow
rates 7. Liquid flow 8. pH 9. Dissolved and Gas
phase Oxygen 10. Dissolved and Gas phase Carbon
Dioxide 11. General gas analysis
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Process sensors and their possible control
functions Category Sensor Possible control
function Physical Temperature Heat/cool Pressu
re Agitator shaft power RPM Foam Foam
control Weight Change flow rate Flow
rate Change flow rate Chemical pH Acid or
alkali addition Carbon source feed
rate Redox Additives to change redox
potential Oxygen Change feed rate Exit-gas
analysis Change feed rate Medium
analysis Change in medium composition
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CASE STUDY Draw a diagram of a STR include all
the major controls
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(No Transcript)
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3.2.9. TEMPERATURE CONTROL
HEAT BALANCE IN FERMENTATION Q met Heat
---gt Microbial metabolism Q ag "
---gt Mechanical agitation Q aer
" ---gt Aeration Q evap " ---gt
Water evaporation Q sens " ---gt
Feed streams Q exch " ---gt
Exchanger / surroundings UNDER ISOTHERMAL
CONDITIONS Q met Q ag Q aer Q evap
Q sens Q exch
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CASE STUDY ? Draw a flow sheet of the heat
balance in a typical fermentation ? List the
methods of measuring temperature (chapter
8) ? Outline methods of temperature control
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3.3. FERMENTATION MEASUREMENT /monitoring
PHYSICAL (e.g Temperature, Pressure
etc.) CHEMICAL ( e.g. pH, Redox, Ions
etc.) INTRACELLULAR ( Cell mass composition,
enzyme levels etc.) BIOLOGICAL ( e.g.
Morphology, cell size, viable count etc.)
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CASE STUDY Report on the methods used to estimate
biomass within a reactor - give advantages /
disadvantages of each
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TYPICAL PARAMETERS - Penicillin fermentation
(1) Feeding rate of substrate / precursor (2)
Biomass conc. per litre and per fermenter
(mass) (3) Penicillin conc. and mass (4) Growth
rate (5) Fraction of glucose --gt
Mass Maintenance Product (6) Respiration
rate (7) Oxygen demand (8) Total broth weight (9)
Cumulative efficiency (10) Elemental balance of
P, N, S
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• Models
• Series of equations used to correlate data and
  predict behavior.
• Based on known relationships
• Cyclical nature of models, involves formulation
  of a hypothesis, then experimental design
  followed by experiments and analysis of results
  which should further advance the original
  hypothesis
• Conceptual, Empirical, and Mechanistic models

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Summary

• Why fermentations need to controlled
• How to control fementations
• Use of computers in control of bioprocesses
• Difference between manual and automatic control
  systems
• Process variables that need controlling