IA3112 Automatiseringsteknikk Høsten 2017

Slides:



Advertisements
Similar presentations
Chapter 9 PID Tuning Methods.
Advertisements

Tuning of PID controllers
Tuning PID Controller Institute of Industrial Control,
INDUSTRIAL AUTOMATION (Getting Started week -1). Contents PID Controller. Implementation of PID Controller. Response under actuator Saturation. PID with.
CHE 185 – PROCESS CONTROL AND DYNAMICS
Dynamic Energy Balance. Last time: well-mixed CSTR w/flow & reaction for multiple reactions: rxn #
Exercise 3. Solutions From Fujio Kida, JGC Co. All cases
Ch 2.3: Modeling with First Order Equations Mathematical models characterize physical systems, often using differential equations. Model Construction:
Pipe Networks Pipeline systems Pumps pipe networks measurements
Specialization project Project title “Practical modeling and PI-control of level processes” Student Ingrid Didriksen Supervisor Krister Forsman.
1 Dynamic Behavior Chapter 5 In analyzing process dynamic and process control systems, it is important to know how the process responds to changes in the.
Process Control: Designing Process and Control Systems for Dynamic Performance Chapter 18. Level Control Copyright © Thomas Marlin 2013 The copyright holder.
DYNAMIC BEHAVIOR OF PROCESSES :
Background 1. Energy conservation equation If there is no friction.
CHAPTER 2 MASS BALANCE and APPLICATION
Probably© the smoothest PID tuning rules in the world: Lower limit on controller gain for acceptable disturbance rejection Sigurd Skogestad Department.
Dynamic Behavior Chapter 5
Model SIMC-tunings Tight control Smooth control Level control
CHAPTER V CONTROL SYSTEMS
Presentation at NI Day April 2010 Lillestrøm, Norway
Control engineering and signal processing
PID-tuning using the SIMC rules
Model-based Predictive Control (MPC)
Mathematical Models for Simulation, Control and Testing
CHAPTER VI BLOCK DIAGRAMS AND LINEARIZATION
Introduction to process control
Lesson 18: Integral Process Characteristics
Course PEF3006 Process Control Fall 2017 Lecture: Process dynamics
Course IIA1117 Control Engineering Fall 2017 Plant-wide control
State estimation (Kalman filter)
Model-based Predictive Control (MPC)
Tuning of PID controllers
Dynamic Behavior Chapter 5
Av Finn Aakre Haugen IA3112 Automatiseringsteknikk og EK3114 Automatisering og vannkraftregulering Høstsemesteret 2017 Gain scheduling.
Basic Design of PID Controller
Tuning of PID controllers
Servo Tuning for Path Applications
Model-based Predictive Control (MPC)
Model-based Predictive Control (MPC)
Course PEF3006 Process Control Fall 2018 Lecture: Process dynamics
Course PEF3006 Process Control Fall 2018 PID Control
State estimation (Kalman filter)
4.1 Stability Routh Tabulation Example 4.1
Christoph J. Backi and Sigurd Skogestad
Introduction to process control
IA3112 Automatiseringsteknikk Høsten 2018
Pipe Networks Pipeline systems You are here Transmission lines
بسم الله الرحمن الرحيم PID Controllers
Averaging (or equalizing) level control
Y The graph of a rule connecting x and y is shown. From the graph, when x is -1, what is y? Give me a value of x that makes y positive, negative, equal.
First-Order System Chapter 5
Course IIA1117 Control Engineering Fall 2018 Plant-wide control
Av Finn Aakre Haugen IA3112 Automatiseringsteknikk og EK3114 Automatisering og vannkraftregulering Høstsemesteret 2018 Gain scheduling.
Course PEF3006 Process Control Fall 2017 Cascade control
7. Stability [Ref:Rao] 7.1 System stability
How a control system may become unstable
Averaging (or equalizing) level control
Course IIA1117 Control Engineering Fall 2018 Ratio control
Course IIA1117 Control Engineering Fall 2017 Ratio control
Proportional Control and Disturbance Changes
Averaging (or equalizing) level control
Applications of Bernoulli Equations
Closed-Loop Frequency Response and Sensitivity Functions
On-Off Control (alternative to PID)
How a control system may become unstable
Pipe Networks Pipeline systems You are here Transmission lines
Controller Tuning Relations
Performance and Robustness of the Smith Predictor Controller
Course PEF3006 Process Control Fall 2017 PID Control
Dynamic Behavior Chapter 5
Presentation transcript:

Av Finn Aakre Haugen (finn.haugen@usn.no) IA3112 Automatiseringsteknikk Høsten 2017 Midlende nivåregulering av buffertank (eng.: averaging level control...) Av Finn Aakre Haugen (finn.haugen@usn.no) HSN. IA3112 Auttek. Haugen. 2017.

Buffer tank with a level control system 2 Buffer tank with a level control system Aim: Averageing (or equalizing, or attenuating) inflow variations so that the outflow becomes smoother than the inflow. HSN. IA3112 Auttek. Haugen. 2017.

3 Example of application (results shown at end of this PPT): Level control of equalization magazine upstreams the VEAS water resource recovery facility (wrrf) or resource recovery facility (wrrf), at Slemmestad, south of Oslo, Norway: HSN. IA3112 Auttek. Haugen. 2017.

4 How to tune LC? We need a sluggish or soft or compliant LC so that the liquid volume (the level) can take up the inflow variations. Ziegler-Nichols is useless here since it gives fast or stiff control :-( But Skogestad is excellent, using Tc as tuning parameter :-) Kc = 1/(Ki*Tc) Ti = 2*Tc where Ki = -1/A is the integrator gain or normalized process step response. How to select Tc? HSN. IA3112 Auttek. Haugen. 2017.

Δhmax <= (Tc/A)*ΔFin 5 ... How to select Tc? As a start, assume P (proportional) level controller. It can be shown, from a mathematical model of the level control system, that Δhmax = (Tc/A)*ΔFin where Δhmax is corresponding maximum allowed level change (in steady state) after max inflow step change, ΔFin. With a PI controller with the same Tc: Δhmax <= (Tc/A)*ΔFin Solving this inequality for Tc gives Tc >= A* Δhmax/ΔFin => Specification of Tc in the PI settings: Tc = A* Δhmax/ΔFin HSN. IA3112 Auttek. Haugen. 2017.

Comparison of responses in level h 6 Comparison of responses in level h due to step change in inflow Fin, with P and with PI controllers: HSN. IA3112 Auttek. Haugen. 2017.

Example Assumptions: A = 2000 m2 ΔFin = 1 m3/s Δhmax = 0.5 m 7 Example Assumptions: A = 2000 m2 ΔFin = 1 m3/s Δhmax = 0.5 m Resulting Tc: Tc = A* Δhmax/ΔFin = 2000*(-0.5)/(-1) = 1000 s PI settings: Kc = 1/(Ki*Tc) = -A/Tc = -2000 m2 / 1000 s = 2.0 (m3/s)/m Ti = 2*Tc = 2*1000 s = 2000 s HSN. IA3112 Auttek. Haugen. 2017.

8 Simulation! HSN. IA3112 Auttek. Haugen. 2017.

Results from VEAS: Pump flow Much smoother pump flow 9 Results from VEAS: With original PI settings in the LC (Kc = 8.0 , Ti = 1000 s) With new (Skogestad) PI settings in the LC (Kc = 3.1 , Ti = 3240 s) Pump flow Much smoother pump flow HSN. IA3112 Auttek. Haugen. 2017.