Process and Control Flashcards
What is FOPDT
First order plus dead time
The FOPDT model is low order and linear so it can only approximate the behavior of real processes.
The important parameters that result are:
Steady State Process Gain, KP
Overall Process Time Constant, τP
Apparent Dead Time, θP
What does Kp represent in process?
Steady state process gain.
KP describes how much the measured process variable, y(t), changes in response to changes in the controller output, u(t).
A large process gain means the process will show a big response to each control action.
What does τP represent in process?
Overall process time constant
Time Constant τP describes how fast the measured process variable, y(t), responds to changes in the controller output, u(t).
τP is how long it takes for the process variable to reach 63.2% of its total change, starting from when the response first begins.
What does θP represent in process?
Apparent dead time.
θP is the time from when the controller output step is made until when the measured process variable first responds.
Apparent dead time, θP, is the sum of these effects:
- transportation lag, or the time it takes for material to travel from one point to another
- sample or instrument lag, or the time it takes to collect analyze or process a measured variable sample
- higher order processes naturally appear slow to respond
Notes:
Dead time must be positive and have units of time.
Tight control is increasingly difficult if θP > τP.
For important control loops, work to avoid unnecessary dead time.
What are model parameters used for?
Model parameters (KP, τP and θP) are used in correlations to compute initial controller tuning values.
Sign of KP indicates the action of the controller (+KP → reverse acting; −KP → direct acting)
Size of τP indicates the maximum desirable loop sample time (be sure that sample time T ≤ 0.1τP)
Ratio θP /τP indicates whether MPC (Smith predictor) would show benefit (useful if θP ≥ τP)
Model becomes part of the feed forward, Smith Predictor, decoupling and other model-based controllers.
What do the values of the model parameters in process indicate?
Sign of KP indicates the action of the controller (+KP → reverse acting; −KP → direct acting)
Size of τP indicates the maximum desirable loop sample time (be sure that sample time T ≤ 0.1τP)
Ratio θP /τP indicates whether MPC (Smith predictor) would show benefit (useful if θP ≥ τP)
Model becomes part of the feed forward, Smith Predictor, decoupling and other model-based controllers.
Why is process control important?
Allows consistent product quality
Increase process lifetime and enhances stability
Optimises process parameters
What are the components of process control design?
Define control objectives / set-point
Select primary (online) and secondary (offline) variables to be measured
Select variables to be manipulated
Identify relationships between measured and manipulated variables
Control system components:
Primary elements (online sensors/transducers)
Secondary elements (offline analysis, chemical or mathematical)
Controllers (analogue - continuous, or digital - discontinuous)
Final control elements (e.g. valves)
Examples of primary and secondary elements (for process control):
Primary elements:
- Pressure and level sensors.
- Temperature sensors.
- Flow rate and total flow sensors.
- Analysis instruments – e.g. pH, redox potential, conductivity (thermal or electrolytic), gas analysis (O2, CO2, H2 etc), heat of reaction, energy).
- Transducers – convert measurements into electrical signals.
Secondary elements:
- Off-line chemical analysis.
- Simple correlations between what can be measured online (primary element) and what can not (secondary element).
- Use of on-line (primary element) measurements to complete mass balances (secondary element).
- Use of mathematical models.
What are the types of control loop?
Feedback – a measurement is made of the process output(s), and process input(s) are adjusted to achieve control.
Feedforward – a model is used to force or drive the process in a certain direction, or to offset the predicted effect of a disturbance before it actually happens.
Often these are used together, with feed-forward trying to drive the “ideal” process, and feedback checking what is really happening.
What are y(t) and u(t) with respect to process control?
y(t) is the measured process variable
u(t) is the controller output signal
Kp = dy/du
What are design features of feedback control / what do we look for?
Zero offset, i.e. return to the required set-point after any disturbance(s), i.e. zero error.
Insensitivity to errors – no process model can be exact, so can the algorithm tolerate some flexibility in the values of key parameters
Wide applicability – can a single algorithm form handle a wide range of different processes
Simple calculation overheads – how long does it take to compute & execute the control response
Properties of proportional control:
Simple.
Provides rapid adjustment of the manipulated variable (MV).
Does not provide zero offset, although the error is reduced (E > 0).
Speeds the dynamic response.
Can cause instability if improperly tuned.
Properties of integral control:
Simple.
Achieves zero offset (E = 0).
Adjusts the manipulated variable (MV) in a slower manner than the proportional mode – this can lead to poor dynamic performance.
Can cause instability if improperly tuned.
