bioproceses dynamics and control

3. Process ControlGuidance of the process along a certain
path to produce a product that meets
predefined quality specifications
The Aim
To produce the product of interest at a
minimum of operating costs (ie. Increase
the cost/benefit ratio)

4. Reasons for Process Control
 Easier optimisation of the process
 More constant product quality
 Detection of problems and their
location at an early stage
 Greater quality assurance

5. (1) Bioreactor
Batch process
• significant changes of process variables over time
• requires more complex control
• requires experience with the process (feed
forward control)
Steady state processes (chemostat)
• constant process conditions
• more simple process control
• feedback control often sufficient

6. (2) Sensors (Measuring Devices)
 Enable monitoring of the state of the
process
– e.g. temperature, DO concentration,
biomass conc.
 Measurements can be on-line or off-line.

7. On-line Measurements
 Performed automatically
 Results directly available for control
 Monitored continuously
Off-line Measurements
 Require human interface
 Less frequent and usually irregular
 Best suited for checking and calibrating

8. Types of On-line Measuring Equipment
Physical Measurements
 Temperature
 Weight
 Liquid flow rates
 Gaseous flow rates
 Liquid level
 Pressure inside vessel
10.12 kg

9. (3) Actuators
 Devices which make the changes to
the process, e.g.
 Aeration pumps
 Stirrers
 Feed pumps
 Chemical dosing pumps
 Inoculation ports
 Recycle pumps

10. Basic Control Schemes
 Open-Loop Control (Feedforward)
 Closed-Loop Control (Feedback)
 Inferential control
 Combined feedforward and feedback
(model-supported control)

11. Devices that decide on the appropriate action
to be taken to keep the process running along
the desired path
◦ Computers
◦ “Biocontrollers”

12. Examples of feedback loops:
 Temperature control
 pH control
 Oxygen control (e.g. SND)

13.  The pattern of the manipulable variable is
predetermined, and directly adjusts the actuator
 There is no feedback from the process to the
controller
 Requires no measurement of the variable
 Often model-based  requires reliable model
 Large deviations of the process from the required
path are not corrected for
 Sounds very theoretical (examples?)

14.  Simplest type – digital
on-off switching, e.g. thermostat
 PID control (very common and
important)
 Fuzzy logic control, Adaptive
Controllers, Self learning systems
(not covered in this unit)

15.  Measurements give indirect information
about critical variables in the process (e.g.
biomass activity, biomass concentration,
substrate concentration etc.)
 Using the on-line measurements to estimate
the current state of the biomass
 state estimators (e.g. SOUR)
 Advantage: enables on-line control of a
variable that cannot be measured on-line
 Modelling plays important role

16.  Measurements give indirect information
about critical variables in the process (e.g.
biomass activity, biomass concentration,
substrate concentration etc.)
 Using the on-line measurements to estimate
the current state of the biomass
 state estimators (e.g. SOUR)
 Advantage: enables on-line control of a
variable that cannot be measured on-line
 Modelling plays important role

17. If the input signal does not immediately affect
the output  delayed action typical of on/off
controllers
Caused by things such as;
 feed pump too large for required dosage
 delay in sensor response
DO
mg L-1
Time
1
2

18. error
Controller Process
Actuator
Measured
output

19.  Conventional and most common type of control
scheme … “safest”
 Measurements from the process are used to
calculate a suitable control action
 Appropriate when the accuracy requirement is
higher
 Deviations between the variable and its setpoint
are used to change the process
 smaller deviations