1. Topic 4 Controller Actions And Tuning

2. Chemical Processes Self-regulating Process Dynamics SS Gain, Kp Deadtime, θ Lag, τ Integrating Process Dynamics Gain, Kp,integrate Deadtime, θ In the last lecture

3. What We Will Cover Topic 1 Introduction To Process Control Topic 2 Introduction To Process Dynamics Topic 3 Plant Testing And Data Analysis Topic 5 Enhanced Regulatory Control Strategies Topic 6 Process Control Hardware Systems Topic 4 Controller Actions And Tuning Topic 7 Control Valves Topic 8 Process Control Troubleshooting

4. In This Lecture…  Controller Actions  Controller Modes  Proportional Control  Problems of Proportional-Only Control

5. Feedback Controller  PID controller is the most common type  Sole purpose is to adjust an MV in order to bring a CV (PV) as close to SP in as short a time as possible  The extent at which the controller adjusts the MV depends on the PID tuning constants –K c – Proportional Term –τ I – Integral Term (min) –τ D – Derivative Term (min)

6. Controller Modes  Manual (MAN) –Operator changes OP “manually” –SP not used for control  Auto (AUTO) –Operator changes SP –Controller adjusts OP “automatically” to bring PV to SP  Cascade (CAS) –Secondary controller on CAS and Primary controller on AUTO –Primary’s OP “cascaded” down to secondary’s SP –Secondary adjusts own OP to bring PV to SP

7. Consider this....  Fin fluctuates; SP fixed at 50%  When level = 10%, we want valve full close (OP = 0%)  When level = 90%, we want valve full open (OP = 100%)  Level in between 10 and 90%, valve will be partially open  This is an example of proportional-only control

8. Essence Of P-Action  Take drastic action when far away from SP  Go easy when close to SP OP = K c x Error + Bias –K c = controller gain –Error = SP - PV (depends on manufacturer) –Bias = some constant  Good? Yes, but….

9. An example  DeltaP fluctuates so flow fluctuates if loop is on MAN  Let’s say we now have a flow rate of PV=SP=500 BD, and at that flow rate, OP = 40% (i.e. valve is 40% open) OP = K c x Error + Bias 40 = K c x 0 + Bias Bias = 40

10. An example  We now want to control the flow at 600 BD (Operator increase SP from 500 to 600)  Assume K c = 0.5, so OP = 0.5(Error) + Bias  The controller detects an error of (600-500)/1000 = 10%  New OP = 0.5(10)+40 = 45%, so valve opens to 45%  What will be the new flow rate? –3 possibilities: 500 600 –Unlikely to get exactly 600 BD

11. An example  Let’s say at 45%, the PV= 562.5 BD –Error = (600 – 562.5)/1000 = 3.75%  Error has now decreased from 10% to 3.75% –New OP = 0.5(3.75%) + 40% = 41.88% –New flow = 523.4 BD  Recalculate Error and OP, and observe flow –Error = (600 – 523.4)/1000 = 7.66% –New OP = 0.5(7.66%) + 40% = 43.83% –New flow = 547.9 BD  This cycle will repeat itself  Finally it will settle at a steady value, BUT

12. P-Only Control Response

13. Problem With P-Only Control  It will not settle at 600 BD  There will always be an Offset (SP-final value that PV settles at)  Offset can be reduced by higher controller gain, K c  But that can result in more drastic cycling before the PV settles down

14. In This Lecture…  Controller Actions  Proportional Control  Problems of Proportional-Only Control

15. In The Next Lecture…  Integral Control –Equation –How it works –Interaction with Proportional Action –Problems with Integral Action  Derivative Control –Equation –How it works –Problems with Derivative Action