Control and Modelling of Bioprocesses Slides adapted from Dr.

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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

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 Control Involves the use of monitored information to make decisions that affect the process in a desirable way Make decision On the right path? Process

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 3. Actuators 4. Controllers 2. Sensors 1. The plant (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) 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 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 Physical Measurements – – – – – – 10.12 kg Temperature Weight Liquid flow rates Gaseous flow rates Liquid level Pressure inside vessel

Sensors (continued) Physico-Chemical Measurements pH Oxidation-reduction potential (ORP, Eh) Dissolved oxygen Conductivity Off-gases (CO2, H2, CH4) NH4 (ion-selective electrodes)

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 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 Devices which make the changes to the process, e.g. Aeration pumps Stirrers Feed pumps Chemical dosing pumps Inoculation ports Recycle pumps

(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 Open-Loop Control (Feedforward) Closed-Loop Control (Feedback) – Inferential control Combined feedforward and feedback (modelsupported 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 Sounds very theoretical (examples?)

Feedforward Control (Open-Loop Control) Examples: Washing machine Breadmaker Often in combination with feedback control aspects (e.g. Waste water treatment, DiCOM process

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

Feedback Control (Closed-Loop Control) Measured output Actuator error Controller Process

Ideal Feedback Controller 2 DO mg L-1 1 Time

Overshooting 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 2 DO mg L-1 1 Time

Combined Feedforward and Feedback Control Individual feedback loops (pH, oxygen) within an overall process based on feed forward (DiCOM by ANAECO) 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 Control Feedforward controller Process Feedback controller Set point

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 DO dcL/dt OUR Time

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 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.

Car steering analogy of PID controller Current signal Setpoint

Basic Controller Decision making Get New Temp. N Turn Heater Off Wait X sec Temp Setp.? Y Turn Heater On

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

Integral controller Setpoint 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 Integration of a curve area under the curve Integrated input signal is multiplied by a factor, K i

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 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

Car steering analogy of Differential controller - Setpoint

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 Setpoint 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 elements Setpoint P: No problem: Signal position is on setpoint D: Alarm: Direction is wrong. Left turn needed

Conflicting or neutralising advice by PID control elements Setpoint 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 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? Differentiating elements are capable of detecting small changes providing they occur rapidly - TRUE Integrating elements always respond rapidly to changes in output signals - FALSE A long delay time in a feedback control system may lead to considerable overshoot - TRUE

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 FALSE process at a setpoint will not require any re-adjustment to ensure the output remains constant - Usually FALSE A state estimator allows us to operate on-line control of a variable for which no on-line measurements are available - TRUE

Content beyond this point is not examinable

Proportional Integral Derivative (PID) Controllers Conventional and classical approach of control engineering t 1 d m(t ) K c .dt D dt 1 0 Parameters Kc, I and D can be determined from simple experiments

Determining the PID values DO mg L-1 B A K A/B gain a T Time Actuating signal Process response

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) t 1 d m(t ) K c .dt D dt 1 0

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 Result in significant improvements to the control Tuning of control parameters can be easy when simple “blackbox” 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 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 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

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