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Control and Modelling of Bioprocesses. Slides adapted from Dr. Katie Third. Lecture Outline. Purpose of Process Control Building blocks of process control The bioreactor (modelling) Sensors Actuators Controllers Basic control schemes Basic Controller Actions Case examples.

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Control and modelling of bioprocesses
Control and Modellingof Bioprocesses

Slides adapted from Dr. Katie Third

Lecture outline
Lecture Outline

  • Purpose of Process Control

  • Building blocks of process control

    • The bioreactor (modelling)

    • Sensors

    • Actuators

    • Controllers

  • Basic control schemes

  • Basic Controller Actions

  • Case examples

Process control
Process Control

Guidance 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)

Process control1
Process Control

Involves the use of monitored information to make decisions that affect the process in a desirable way

Make decision

On the right path?


Reasons for process control
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

4 basic building blocks of a controlled process
4 Basic Building Blocks of a Controlled Process

3. Actuators

4. Controllers

2. Sensors

1. The plant (bioreactor)

1 bioreactor
(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

2 sensors measuring devices
(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.

On line measurements
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

Types of on line measuring equipment
Types of On-line Measuring Equipment

Physical Measurements

  • Temperature

  • Weight

  • Liquid flow rates

  • Gaseous flow rates

  • Liquid level

  • Pressure inside vessel

10.12 kg

Sensors continued
Sensors (continued)

Physico-Chemical Measurements

  • pH

  • Oxidation-reduction potential (ORP, Eh)

  • Dissolved oxygen

  • Conductivity

  • Off-gases (CO2, H2, CH4)

  • NH4+ (ion-selective electrodes)

Sensors continued1
Sensors (continued)

Biochemical Measurements

  • Respiration rate (OUR, SOUR)

  • Volatile fatty acids (VFA’s)

  • Flourescence (e.g. NADH)

  • Turbidity

Requirements of a good on line sensor
Requirements of a good on-line sensor

  • Heat and pressure resistant  autoclavable

  • Mechanically robust

  • Resistant to bacterial adhesion

  • Stable over a long period

  • Fast dynamics in relation to the measured variable

  • Linear characteristics  easy in-situ calibration

3 actuators
(3) Actuators

  • Devices which make the changes to the process, e.g.

    • Aeration pumps

    • Stirrers

    • Feed pumps

    • Chemical dosing pumps

    • Inoculation ports

    • Recycle pumps

4 controllers
(4) Controllers

Devices that decide on the appropriate action to be taken to keep the process running along the desired path

  • Computers

  • “Biocontrollers”

Basic control schemes
Basic Control Schemes

  • Open-Loop Control (Feedforward)

  • Closed-Loop Control (Feedback)

    • Inferential control

  • Combined feedforward and feedback (model-supported control)

Feedforward control open loop control
Feedforward Control (Open-Loop Control)

  • 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

Feedforward control open loop control1
Feedforward Control (Open-Loop Control)






E.g. In fed-batch cultivation, the pattern of the feed rate profile is used to directly adjust the feed pump

Feedback control closed loop control
Feedback Control (Closed-Loop Control)

  • 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

Feedback control closed loop control1
Feedback Control (Closed-Loop Control)

Measured output





Ideal feedback controller
Ideal Feedback Controller


DO mg L-1




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



Combined feedforward and feedback control
Combined Feedforward and Feedback Control

  • To compensate for small model deviations and unpredicted disturbances

  • Feedforward control establishes control according to process model

  • Feedback allows for refinement by correcting for deviations

Combined feedforward and feedback control1
Combined Feedforward and Feedback Control

Feedforward controller



Set point

Inferential control
Inferential Control

When direct feedback of the variable of interest is not possible, on-line measurements can be used to “infer” the state of the variables (also called State Estimation)

E.g. DO fluctuations  SOUR


dcL/dt  OUR


State estimation
State Estimation

  • 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

State estimation1
State Estimation

  • Also the Control action itself can be recorded and used as an online or offline process analysis tool.

  • For example the total duration over which the alkali dosing pump has been switched on, allows to calculate the amount of alkali used to counteract the acid produced in the bioprocess  Biological acid production is recorded online.

Basic controller decision making
Basic Controller Decision making




Temp <













Basic controller actions
Basic Controller Actions

  • 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)

On off controller
On-Off controller

  • E.g. stop airflow if DO is higher than setpoint  large oscillations of process variable

  • can use an acceptable band of values with no control action, e.g. If pH > 8 then run acid pump. If pH<6 then run base pump.  no precise control

Proportional controller
Proportional Controller

  • Multiplies the deviation of the variable from the setpoint with a constant, Kp

  • The further away the variable from the setpoint, the stronger the action

    Control input = (Process output – Setpoint).Kp Controller

    signal signal output

Proportional controller1
Proportional controller


Car – steering analogy: Check distance from middle of the lane and correct steering in proportion to distance from desired position

Integral controller
Integral controller


  • Car steering analogy:

  • Look out through the back window and keep track of

  • how long the car has been out of desired position and

  • by how much.

  • How long (sec) * how much (m) is the integral (sec*m).

  • The longer the car was positioned away from the setpoint the stronger the signal

  • Good to correct for long term and only slight deviation from setpoint.

Integrating controllers
Integrating Controllers

  • Integration of a curve  area under the curve

  • Integrated input signal is multiplied by a factor, Ki

Integrating controllers1
Integrating Controllers

  • A purely integrating controller is slow and

  • Error takes long time to build up

  • Action can become too strong  overshooting

  • Int controller is unaware of current position  Generally used combined with P control (looking at current position) – PI control

Differentiating controller
Differentiating Controller

  • Examines the rate of change of the output of the process

  • The faster the change, the stronger the action

  • The derivative of the output (slope) is multiplied by a constant, Kd

Differentiating element and pid controllers
Differentiating Element and PID Controllers

  • Differential control is insensitive to slow changes

  • If the variable is parallel to the setpoint, no change is made (slope = 0)

  • Differential control is very useful when combined with P and I control  PID control

Problems with individual pid control elements
Problems with individual PID control elements


P: Alarm: strong left turn needed

I: No problem: Past Right and Left errors are about equal

D: No problem: Direction is parallel to setpoint

Problems with individual pid control elements1
Problems with individual PID control elements


P: No problem: Signal position is on setpoint

D: Alarm: Direction is wrong. Left turn needed

Conflicting or neutralising advice by pid control elements
Conflicting or neutralising advice by PID control elements


P: Alarm: Position too far left. Turn right

D: Alarm: Direction too far towards right. Turn Left. position is on setpoint

Time analogy of pid controllers
Time Analogy of PID Controllers

  • P: Present time. Only considers current position. Not aware of current direction and of error history

  • I: Past time. Only compiles an error sum of the past. Not aware of current distance of signal from setpoint and of current direction.

  • D: Future time. Only considers current direction (trend). Now aware of current distance of signal from setpoint and of error history.

Questions true of false
Questions – True of False?

  • Differentiating elements are capable of detecting small changes providing they occur rapidly

  • Integrating elements always respond rapidly to changes in output signals

  • A long delay time in a feedback control system may lead to considerable overshoot




Questions true of false1
Questions – True of False?

  • Time between changes in measured values and control action should always be as short as possible

  • A proportional controller once set up to maintain an output of a process at a setpoint will not require any re-adjustment to ensure the output remains constant

  • A state estimator allows us to operate on-line control of a variable for which no on-line measurements are available


- Usually FALSE


Proportional integral derivative pid controllers
Proportional Integral Derivative (PID) Controllers

  • Conventional and classical approach of control engineering

  • Parameters Kc, I andD can be determined from simple experiments

Determining the pid values
Determining the PID values


mg L-1








Actuating signal

Process response

Determining the pid values1

































Determining the PID values

  • Ziegler/Nicols Procedure

    PID Control

    KC = (1.2/K) T/a (proportional)

    I = 2.0 a (differential)

    D = 0.5 a (integral)

Adaptive controllers not examinable
Adaptive Controllers (not examinable)

  • The state of the biomass changes continuously during the course of a non-steady state bioprocess (the car may turn into a boat)

  • Required PID values of controller change

  • Adaptive controllers continuously adjust control parameters during the running process

  • Requires finding how to “tune” the control values

     Experimentation and finding linear relationships between state of biomass and PID values

Adaptive controllers
Adaptive Controllers

  • Result in significant improvements to the control

  • Tuning of control parameters can be easy when simple “black-box” assumptions can be made

  • When simple assumptions are not adequate, process dynamics must be considered in a process model

  • Model-supported control (or combined feedback and feedforward control

Fuzzy logic control
Fuzzy Logic Control

  • Useful when concrete knowledge cannot be transformed into mathematical equations

  • Based on “fuzzy logic”

  • e.g. “If … happens, take … action”

  • Although very simplified, whole bioprocesses can run effectively on fuzzy logic rules

Learning outcomes
Learning Outcomes

You should be able to;

  • Explain the range of control schemes that exist for controlling a bioprocess

  • Understand how the different types of controllers work

  • Identify which variables will need controlling in a bioprocess

  • Identify useful features of an on-line measuring device

  • Recognize applications of process control in the food industry