Lecture 7

steamgloomyΗλεκτρονική - Συσκευές

15 Νοε 2013 (πριν από 3 χρόνια και 8 μήνες)

68 εμφανίσεις

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





Microbial cells;


Growing


Non
-
growing





Enzymes;

Soluble

Immobilised


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



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

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?


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

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

3.2.1.

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



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

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

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



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

Human operator instructed to control

temperature within set limits

Manual adjustment

of valve

Visual awareness

Steam



Valve

(Final control

element)

Water

Pipe

Thermometer

EXPENSIVE

Automatic control

Simple automatic control loop for temperature control

Controller

Signal to operate valve

Measured valve

Steam



Control

Valve




Water

Pipe

Thermocouple

Set
-
point

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

100% open (on)


Valve or switch

position


100% closed (off)

100% open (on)


Valve or switch

position


100% closed (off)

(2) Proportional control

Positive

deviation



Controlled

variable


Set
-
point




Negative

deviation

Time


1. Output without
control


2. Proportional
action


3. Integral action


4. Proportional +
integral action


5. Proportional +
derivative action


6. Proportional +
integral +
derivative action

1

2

3

5, 6

4

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


Temperature

controller

Pressure line

to valve

Hot

water

Pressure

regulated

valve

Heating

Jacket

Water

outlet

Thermocouple

A fermenter with a temperature
-
controlled


heating jacket

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)

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


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 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


Printout

VDU

Data store

Graphic unit

Alarms

Clock

Dedicated

mini
-
computer

Mainframe

computer

Interface

Analogue to

digital converter

Meter

Reservoir

Analogue to

digital converter

Pump

Sensor

Computer
-
controlled fermenter with control loop

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

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


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

CASE STUDY

Draw a diagram of a STR include all the major controls


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



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;

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.)


CASE STUDY

Report on the methods used to estimate biomass within
a reactor
-

give advantages / disadvantages of each

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


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



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