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Industrial Microbiology INDM 4005 Lecture 7 18/02/04 PowerPoint Presentation
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Industrial Microbiology INDM 4005 Lecture 7 18/02/04. 3. OPTIMIZATION OF FERMENTATION PROCESS. Overview Fermenter design Process optimisation- Monitor and Control. 3.1. FERMENTOR DESIGN. 3.1.1. Choice of reactor configuration depends on; (a) BIOCATALYST; Animal/ plant cells

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slide1

Industrial Microbiology

INDM 4005

Lecture 7

18/02/04

3 optimization of fermentation process
3. OPTIMIZATION OF FERMENTATION PROCESS
  • Overview
  • Fermenter design
  • Process optimisation- Monitor and Control
3 1 fermentor design
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
choice of reactor configuration depends on
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
slide5

CASE STUDY

  • Draw the major types of aerobic fermenters
  • Draw the major types of low shear fermenters
3 1 2 description of major fermentor configurations
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
why control fermentations
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
control systems
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

3 2 process optimization through monitor and control
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
3 2 2 measurement key parameters
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
3 2 3 control systems general
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
3 2 4 control systems application at plant level
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.

automatic control systems
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)
manual control steam valve to regulate the temperature of water flowing through a pipe
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

slide15

Automatic controlSimple automatic control loop for temperature control

Set-point

Controller

Steam

Control

Valve

Measured valve

Signal to operate valve

Thermocouple

Water

Pipe

automatic control systems16
Automatic control systems

Can be classified into 4 main types

1. Two-position controllers

2. Proportional controllers

3. Integral controllers

4. Derivative controllers

1 two position controller
(1) Two position controller

100% open (on)

Valve or switch

position

100% closed (off)

100% open (on)

Valve or switch

position

100% closed (off)

slide18

(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

1

Positive

deviation

Controlled

variable

Set-point

Negative

deviation

2

4

5, 6

3

Time

automatic control
Automatic control

In complex control systems there are 3 different methods which are commonly used in making error corrections

-proportional

-integral

-derivative

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

slide20

A fermenter with a temperature-controlled

heating jacket

Temperature

controller

Water

outlet

Thermocouple

Pressure line

to valve

Hot

water

Heating

Jacket

Pressure

regulated

valve

automatic control21
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

3 2 6 programmable logic controller chip plc
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 aManagement 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
slide23

CASE STUDY

Briefly outline the benefits of LIMS which contribute to sample handling (data / information handling.

Any other comments on laboratory management?

3 2 7 computers in fermentation
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
slide25

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

3 2 7 computers in fermentation26
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
3 2 8 control process variables
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
slide28

Process sensors and their possible control functions

Category Sensor Possible control function

Physical Temperature Heat/cool

Pressure

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

slide29

CASE STUDY

  • Draw a diagram of a STR include all the major controls
3 2 9 temperature control
3.2.9. TEMPERATURE CONTROL

HEAT BALANCE IN FERMENTATION

Q met = Heat ---> Microbial metabolism

Q ag = " ---> Mechanical agitation

Q aer = " ---> Aeration

Q evap = " ---> Water evaporation

Q sens = " ---> Feed streams

Q exch = " ---> Exchanger / surroundings

UNDER ISOTHERMAL CONDITIONS;

Q met + Q ag + Q aer = Q evap + Q sens + Q exch

slide32

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
3 3 fermentation measurement monitoring
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.)

slide34

CASE STUDY

Report on the methods used to estimate biomass within a reactor - give advantages / disadvantages of each

typical parameters penicillin fermentation
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 --> Mass
      • Maintenance
      • Product
  • (6) Respiration rate
  • (7) Oxygen demand
  • (8) Total broth weight
  • (9) Cumulative efficiency
  • (10) Elemental balance of P, N, S
slide36

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
summary
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