1 / 32

CHE 185 – PROCESS CONTROL AND DYNAMICS

CHE 185 – PROCESS CONTROL AND DYNAMICS. PID ENHANCEMENTS. Limitations of Convential PID Controllers. The performance of PID controllers can be substantially limited by: Process nonlinearity Measurement deadtime Process constraints

pekelo
Download Presentation

CHE 185 – PROCESS CONTROL AND DYNAMICS

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. CHE 185 – PROCESS CONTROL AND DYNAMICS PID ENHANCEMENTS

  2. Limitations of Convential PID Controllers • The performance of PID controllers can be substantially limited by: • Process nonlinearity • Measurement deadtime • Process constraints • there are several approaches for PID controllers to handle each of these problems

  3. Inferential Control • Uses easily measured process variables (T, P, F) to infer more difficult to measure quantities such as compositions and molecular weight. • Can substantially reduce analyzer delay. • Can be much less expensive in terms of capital and operating costs. • Can provide measurements that are not available any other way

  4. Inferential Control • Examples of variables that are not easy to measure directly • DENSITY • VAPOR PRESSURE • MELT INDEX • GAS COMPOSITION • MOLECULAR WEIGHT

  5. Inferential Control • SECONDARY MEASUREMENTS ARE USED WITH THE FOLLOWING FOR INFERENTIAL CONTROL • PROCESS MODEL EQUATIONS • THERMODYNAMIC RELATIONSHIPS, I.E. LINKING TEMPERATURE TO CONCENTRATION • EMPIRICAL MODELING • ISOTHERMAL VISCOSITY VERSUS LIQUID COMPOSITION

  6. INFERENTIAL CONTROL • MEASURES A VARIABLE USING AN INDIRECT METHOD • USED WHEN • IT IS NOT PRACTICAL TO MEASURE THE TARGET VARIABLE • EXCESSIVE COST FOR CONTROL EQUIPMENT TO DIRECTLY MEASURE THE VARIABLE • EXCESSIVE DOWNTIME IN A TARGET VARIABLE SENSOR • THERE IS AN INFERENTIAL VARIABLE AVAILABLE

  7. INFERENTIAL CONTROL • CHARACTERISTICS OF THE INFERENTIAL VARIABLE • IT MUST BE CLOSELY RELATED TO THE TARGET VARIABLE • IT MUST NOT BE AFFECTED BY CHANGES IN THE PROCESS CONDITIONS • DYNAMICS ARE ADEQUATE FOR FEEDBACK CONTROL

  8. INFERENTIAL CONTROL • CORRECTIONS TO INFERENTIAL CONTROL VARIABLE • CAN USE A CASCADE CONTROL SOURCE • CAN BE MANUALLY ADJUSTED

  9. INFERENTIAL CONTROL • example USING TEMPERATURE TO CONTROL COMPOSITION for isobaric flash

  10. INFERENTIAL CONTROL • example USING TEMPERATURE TO CONTROL COMPOSITION for isobaric flash • CONTROLS COMPOSITION BASED ON FLASH TEMPERATURE • DIRECT CONTROLLED VARIABLE IS FLASH PRESSURE • LEVEL IS ALSO DIRECTLY CONTROLLED

  11. INFERENTIAL CONTROL • example USING TEMPERATURE TO CONTROL COMPOSITION for isobaric flash • HOW IS THE TEMPERATURE SETTING CHECKED FOR THIS EXAMPLE? • MANUAL ANALYSIS CAN BE USED TO ADJUST • A FEED FORWARD SIGNAL FROM A PROCESS ANALYZER CAN ALSO BE USED (SEE SKETCH next slide)

  12. INFERENTIAL CONTROL • IT IS ASSUMED THAT THE LAG TIME FOR THE ANALYZER LOOP IS LONGER THAN THAT FOR THE TEMPERATURE LOOP. • THIS ALSO WILL TAKE CARE OF ANY STEADY-STATE OFFSET FOR THE TEMPERATURE CONTROL

  13. Inferential Temperature Control for Distillation Columns • Reboiler control based on tray temperature

  14. Inferential Temperature Control for Distillation Columns • Choosing a Proper Tray Temperature Location • tray temperature used for inferential control should show strong sensitivity

  15. Inferential Temperature Control for flow reactor • See example 13.2 in text

  16. ARTIFICIAL NEURAL NETWORKS (ANN’s) • THESE ARE NON-LINEAR CONTROLLERS THAT ARE USED TO CONTROL NON-LINEAR PROCESSES • THE MODEL TAKES INPUT(S) FROM THE SYSTEM AND USES THESE WITH WEIGHTED FUNCTIONS, TO PROVIDE THE OUTPUT FOR THE CONTROLLER

  17. ARTIFICIAL NEURAL NETWORKS (ANN’s) • THE WEIGHTING FUNCTIONS ARE REVISED OVER TIME TO OPTIMIZE THE OUTPUT • THE ANN IS TUNED BY THE SYSTEM AND ONLY APPLIES TO ONE SYSTEM.

  18. ARTIFICIAL NEURAL NETWORKS (ANN’s) • Soft Sensors Based on Neural Networks • Neural network (NN) provides nonlinear correlation. • Weights are adjusted until NN agrees with plant data • NN-based soft sensors are used to infer NOx levels in the flue gas from power plants.

  19. SCHEDULING CONTROLLER TUNING • THIS IS A METHOD TO COMPENSATE FOR PROCESS NON-LINEARITY THAT CAN AFFECT CONTROL RESPONSE • THE BASIC TECHNIQUE IS TO TUNE THE CONTROLLER BASED ON EMPIRICAL DATA • OPTIMUM TUNING DATA IS OBTAINED OVER A RANGE OF PROCESS SETTINGS.

  20. SCHEDULING CONTROLLER TUNING • THE TUNING DATA IS THEN CONVERTED INTO Proportional, INTEGRAL AND DERIVATIVE RESET FUNCTIONS OF THE MANIPULATED VARIABLE. • THIS METHOD IS SIMILAR TO ANN EXCEPT IT ONLY LOOKS AT ONE INPUT VARIABLE AND RESULTS IN CLEARLY DEFINED FUNCTIONS

  21. SCHEDULING CONTROLLER TUNING • Adjust tuning of heat exchanger control for various feed rates • Link tuning parameters to the flow rates

  22. SCHEDULING CONTROLLER TUNING • Typical open loop response

  23. SCHEDULING CONTROLLER TUNING • Close loop response with scheduling

  24. SCHEDULING CONTROLLER TUNING • Close loop response with scheduling

  25. SCHEDULING CONTROLLER TUNING • IMPLEMENTATION CAN TAKE THE FORM OF ADJUSTMENT OF PI GAIN AND INTEGRAL TIME USING THE TUNING FACTORS • For example using zeigler-nichols (equation 9.11.2):

  26. OVERRIDE/SELECT CONTROL • THIS METHOD EMPLOYS A SELECTION AMONG MULTIPLE INPUTS • IT CAN BE APPLIED TO ROUTINE CONTROL • IT CAN BE USED TO IMPLEMENT EMERGENCY CONTROL • UNDER NORMAL OPERATION A LOW SELECT OR A HIGH SELECT METHOD IS USED BY THE CONTROLLER TO ADJUST THE MANIPULATED VARIABLE

  27. OVERRIDE/SELECT CONTROL • INPUT COMES FROM TWO OR MORE CONTROLLERS TO A SECOND IN A CASCADE CONFIGURATION • THE COMPARISON CONTROLLER CHOOSES THE LOWEST OR HIGHEST TO SEND TO THE ACTUATOR • CONSIDER A REACTOR WITH COOLING FOR TEMPERATURE CONTROL

  28. OVERRIDE/SELECT CONTROL • THE LOW SELECTOR TAKES THE LOWER VALUE FROM THE COMPOSITION ANALYZER AND THE REACTOR TEMPERATURE SENSOR • THE LOWER VALUE IS SELECTED BECAUSE THIS ASSURES THE HIGHEST COOLING FLOW TO THE UNIT.

  29. OVERRIDE/SELECT CONTROL • TEXT PROVIDES SEVERAL OTHER EXAMPLES BASED ON HIGH, LOW AND COMBINED SELECTION • NOTE THAT IT IS IMPORTANT FOR THE OPERATOR TO KNOW WHICH SIGNAL IS BEING USED BY THE CONTROLLER. • mAY BE USED FOR LOW AND HIGH LEVEL ALARM ACTIONS • ALERTS OPERATOR TO OUT-OF-RANGE AND INITIATES CORRECTION WITHIN THE LOOP • NOT INTENDED TO REPLACE SEPARATE HI-HI AND LO-LO ALARMS

  30. COMPUTED MANIPULATED VARIABLE CONTROL • THESE ARE APPLIED MASS BALANCES, ENERGY BALANCES OR REACTION MODELS THAT ARE USED TO SPECIFY OPERATING SET POINTS. • CAN BE USED FOR COMPLICATED SYSTEMS THAT CAN BE CONVENIENTLY MODELED • TYPICALLY USED AS A SECONDARY SET POINT GENERATOR • May be linked to simulators

  31. COMPUTED MANIPULATED VARIABLE CONTROL • Computed Reboiler Duty Control

  32. COMPUTED MANIPULATED VARIABLE CONTROL • Internal Reflux Control

More Related