Chapter 01 - Introduction to Process Control.pptx
- 1. Chapter 1 – Introduction to Process Control
- 2. University of Houston - CHEE3367 - Modeling for Control - Kaspar 2 • Examples • Terminology • Introduction to modeling • Implementation of control • Justification of control Introduction to Process Control 2
- 5. University of Houston - CHEE3367 - Modeling for Control - Kaspar 5 Control Terminology controlled variables - these are the variables which quantify the performance or quality of the final product, which are also called output variables. manipulated variables - these input variables are adjusted dynamically to keep the controlled variables at their set- points. disturbance variables - these are also called "load" variables and represent input variables that can cause the controlled variables to deviate from their respective set points. 5
- 7. University of Houston - CHEE3367 - Modeling for Control - Kaspar 7 Typical Continuous Processes 7
- 9. University of Houston - CHEE3367 - Modeling for Control - Kaspar 9 Control Terminology (2) set-point change - implementing a change in the operating conditions. The set-point signal is changed and the manipulated variable is adjusted appropriately to achieve the new operating conditions. Also called servomechanism (or "servo") control. disturbance change - the process transient behavior when a disturbance enters, also called regulatory control or load change. A control system should be able to return each controlled variable back to its set-point.
- 10. University of Houston - CHEE3367 - Modeling for Control - Kaspar 10 Illustrative Example: Blending system Notation: • w1, w2 and w are mass flow rates • x1, x2 and x are mass fractions of component A Blending Tank 10
- 11. University of Houston - CHEE3367 - Modeling for Control - Kaspar 11 Assumptions: 1. w1 is constant 2. x2 = constant = 1 (stream 2 is pure A) 3. Perfect mixing in the tank Control Objective: Keep x at a desired value (or “set point”) xsp, despite variations in x1(t). Flow rate w2 can be adjusted for this purpose. Terminology: • Controlled variable (or “output variable”): x • Manipulated variable (or “input variable”): w2 • Disturbance variable (or “load variable”): x1
- 12. University of Houston - CHEE3367 - Modeling for Control - Kaspar 12 Design Question. What value of is required to have 2 w ? SP x x Overall balance: Component A balance: 1 2 0 (1-1) w w w 1 1 2 2 0 (1-2) w x w x wx (The overbars denote nominal steady-state design values.) • At the design conditions, . Substitute Eq. 1-2, and , then solve Eq. 1-2 for : SP x x SP x x 2 1 x 2 w 1 2 1 (1-3) 1 SP SP x x w w x
- 13. University of Houston - CHEE3367 - Modeling for Control - Kaspar 13 • Equation 1-3 is the design equation for the blending system. • If our assumptions are correct, then this value of will keep at . But what if conditions change? x SP x Control Question. Suppose that the inlet concentration x1 changes with time. How can we ensure that x remains at or near the set point ? As a specific example, if and , then x > xSP. SP x 1 1 x x 2 2 w w Some Possible Control Strategies: Method 1. Measure x and adjust w2. • Intuitively, if x is too high, we should reduce w2; 2 w
- 14. University of Houston - CHEE3367 - Modeling for Control - Kaspar 14 • Manual control vs. automatic control • Proportional feedback control law, 2 2 (1-4) c SP w t w K x x t 1. where Kc is called the controller gain. 2. w2(t) and x(t) denote variables that change with time t. 3. The change in the flow rate, is proportional to the deviation from the set point, xSP – x(t). 2 2, w t w
- 15. University of Houston - CHEE3367 - Modeling for Control - Kaspar 15 Blending system and control method 1
- 16. University of Houston - CHEE3367 - Modeling for Control - Kaspar 16 Method 2. Measure x1 and adjust w2. • Thus, if x1 is greater than , we would decrease w2 so that • One approach: Consider Eq. (1-3) and replace and with x1(t) and w2(t) to get a control law: 1 x 2 2; w w 1 x 2 w 1 2 1 (1-5) 1 SP SP x x t w t w x
- 17. University of Houston - CHEE3367 - Modeling for Control - Kaspar 17 Blending system and control method 2
- 18. University of Houston - CHEE3367 - Modeling for Control - Kaspar 18 • Because Eq. (1-3) applies only at steady state, it is not clear how effective the control law in (1-5) will be for transient conditions. Method 3. Measure x1 and x, adjust w2. • This approach is a combination of Methods 1 and 2. Method 4. Use a larger tank. • If a larger tank is used, fluctuations in x1 will tend to be damped out due to the larger capacitance of the tank contents. • However, a larger tank means an increased capital cost. 8
- 19. University of Houston - CHEE3367 - Modeling for Control - Kaspar 19 Classification of Control Strategies Method Measured Variable Manipulated Variable Category 1 x w2 FB 2 x1 w2 FF 3 x1 and x w2 FF/FB 4 - - Design change Table. 1.1 Control Strategies for the Blending System Feedback Control: • Distinguishing feature: measure the controlled variable
- 20. University of Houston - CHEE3367 - Modeling for Control - Kaspar 20 • It is important to make a distinction between negative feedback and positive feedback. Engineering Usage vs. Social Sciences • Advantages: Corrective action is taken regardless of the source of the disturbance. Reduces sensitivity of the controlled variable to disturbances and changes in the process (shown later). • Disadvantages: No corrective action occurs until after the disturbance has upset the process, that is, until after x differs from xsp. Very oscillatory responses, or even instability… 20
- 21. University of Houston - CHEE3367 - Modeling for Control - Kaspar 21 Feedforward Control: Distinguishing feature: measure a disturbance variable • Advantage: Correct for disturbance before it upsets the process. • Disadvantage: Must be able to measure the disturbance. No corrective action for unmeasured disturbances. 21
- 22. University of Houston - CHEE3367 - Modeling for Control - Kaspar 22 Closed-loop Artificial Pancreas controller sensor pump patient glucose setpoint u y r measured glucose
- 23. University of Houston - CHEE3367 - Modeling for Control - Kaspar 23 Figure 1.6 Block diagram for composition feedback control system on Fig. 1.4. Block diagram for composition feedback control 23
- 24. University of Houston - CHEE3367 - Modeling for Control - Kaspar 24 Temperature feedback control schematic diagram 24
- 25. University of Houston - CHEE3367 - Modeling for Control - Kaspar 25 Block diagram for temperature feedback control system 25
- 26. University of Houston - CHEE3367 - Modeling for Control - Kaspar 26 Block Diagram for Steam Heated Tank 26 electronic or pneumatic controller
- 27. University of Houston - CHEE3367 - Modeling for Control - Kaspar 27 Incentives for Chemical Process Control • Reasons to do Process Control – Safety – Production Specifications – Environmental Regulations – Operational Constraints – Economics • Tasks the Control System Should Perform – Suppress the effects of external disturbances – Ensure the stability of the process – Optimize the performance of the process Control System Process Monitoring Intervention This is a “control loop”
- 28. University of Houston - CHEE3367 - Modeling for Control - Kaspar 28 Safety (Process Safety) • Avoid situations that lead to – Massive loss of containment (chemical release) – Explosion – Fire • Control layers (order of increasing severity) – Basic regulatory control – Alarm with operator intervention – Automatic preprogrammed abnormal situation management – Safety Instrumented System (SIS) Process
- 29. University of Houston - CHEE3367 - Modeling for Control - Kaspar 29 Process Safety – Things to Avoid Union Carbide’s Bhopal factory After Dec. 3, 1984
- 30. University of Houston - CHEE3367 - Modeling for Control - Kaspar 30 Process Safety – Incidents to Learn from and Not Repeat • ICMESA, Sevesa, Italy, July 10, 1976 – Reactor overheated, resulting in an explosion and release of mixture containing 2,4,5-Trichlorophenol. • Union Carbide, Bhopal, India, December 3, 1984 – Methyl Isocyanate release due to water entering vessel and reacting with contents. 2000 immediate fatalities with another 8000 delayed. Many thousands of injuries. • BP, Texas City, Texas, March 23, 2005 – Distillation tower overfilled and caused release of hydrocarbons which subsequently vaporized. Vapor cloud ignited/exploded killing 15 and injuring 180. • Arkema, Crosby, Texas, August 31, 2017 – Flooding as a result of Hurricane Harvey caused loss of power and backup power and consequently loss of refrigeration. Organic peroxides decomposed and exploded and burned releasing toxic vapors. • Many others • United States Chemical Safety Board www.csb.gov Paul Gruhn lecture
- 31. University of Houston - CHEE3367 - Modeling for Control - Kaspar 31 Production Specifications (Quality) • Final product must be of the purity the customer expects • Any impurities must be only the expected and acceptable impurities • In-process streams must also meet operational specifications If Not • Rework • Disposal Costs • Lost capacity opportunity • Wasted raw materials
- 32. University of Houston - CHEE3367 - Modeling for Control - Kaspar 32 Environmental Regulations • Legal obligation to satisfy regulations – Air permits – Water permits – Ground contamination – Remediation (of past mistakes) • Good Corporate Citizenship to be responsible stewards of the environment (beyond regulations) If Not • Fines • Lawsuits • Damaged Reputation • Criminal Charges • Loss of License to Operate
- 33. University of Houston - CHEE3367 - Modeling for Control - Kaspar 33 Operational Constraints • Maintain pressures so that flow goes the intended direction • Maintain liquid levels so that pumps have adequate NPSH • Maintain temperatures so that heat transfers as intended • Maintain temperatures and pressures within design limits of equipment If Not • Equipment will not operate • Equipment damage
- 34. University of Houston - CHEE3367 - Modeling for Control - Kaspar 34 Economics • Operate profitably • Operate the most profitably possible subject to constraints (optimization) – Drive process to the most profitable operating point and keep it there (continuous) • The location of the most profitable point depends on the current conditions, which are constantly changing. • Requires constant adjustment – Drive process along the most profitable profile in time (batch) • Example: Maximizing yield from a batch reactor may require following a certain temperature profile in time,
- 35. University of Houston - CHEE3367 - Modeling for Control - Kaspar 35 Tasks the Control System Should Perform • Suppress the effects of external disturbances • Ensure the stability of the process • Optimize the performance of the process
- 36. University of Houston - CHEE3367 - Modeling for Control - Kaspar 36 Suppress the effect of external disturbances • “Process” is whatever is inside the system boundary – What you are modeling, controlling, analyzing, or studying • “Environment” is everything else • “Disturbances” are variables that cross the boundary Environment Process Weather • Temperature • Sunlight • Rain • Wind • Pressure Utilities • Air pressure • N2 pressure • Fuel gas HV • CW temperature • Steam pressure and quality Raw Materials • Composition • Temperature • Pressure Other Process
- 37. University of Houston - CHEE3367 - Modeling for Control - Kaspar 37 Ensure the Stability of the Process • Process can be: – Physical process (open loop – without control) – Combination of physical process and control system (closed loop) • Physical process can be stable or unstable – Unstable process can be stabilized by properly designed feedback control – Stable process can be destabilized by poor control design (avoid this)
- 38. University of Houston - CHEE3367 - Modeling for Control - Kaspar 38 Stabilizing an Unstable CSTR Heat Generation Heat Removal
- 39. University of Houston - CHEE3367 - Modeling for Control - Kaspar 39 Destabilizing a Stable Process Stable, but not under control (open loop) Good closed loop control Increased controller gain Increase More Increase More
- 40. University of Houston - CHEE3367 - Modeling for Control - Kaspar 40 Optimize the Performance of the Process • Operate the process at the “best” point or along the “best” trajectory • What is “best” requires the objective to be defined – Profit – Throughput – Environmental Considerations • Best point – can vary over time based on external disturbances – Must respect constraints (physical, operational, quality) • Control system should adjust accordingly
- 41. University of Houston - CHEE3367 - Modeling for Control - Kaspar 41 Example: Stripping Column Economic objective • Bottom product is valuable • Top product is incinerated • Manipulated variables: – Steam flow – Top product flow – Feed rate • Controlled variable: Bottom product purity • Easiest way to run – Moderate feed rate – High steam flow – High top product flow • Best way to run – High feed rate – Just enough steam flow – Just enough top product flow Easy to avoid flooding High separation High impurity removal Limits Production rate Wastes steam Wastes product Maximize production Just enough separation Just meet specification
- 42. University of Houston - CHEE3367 - Modeling for Control - Kaspar 42 Other Examples • Batch reactor example 1.3 from book – Economic objective: maximize profit • No steam gives little product • Too much steam produces too much byproduct (and wastes steam) • Want it just right – and just the right time profile • Fired heater – Environmental objective: minimize emissions – Economic objective: efficient use of fuel • Air/fuel ratio too low produces too much CO and wastes fuel due to incomplete combustion • Air/fuel ratio too high produces too much NOX and wastes fuel due to heating excess air
- 43. University of Houston - CHEE3367 - Modeling for Control - Kaspar 43 Economic Incentives of Improved Control 43
- 45. University of Houston - CHEE3367 - Modeling for Control - Kaspar 45 Summary: Incentives for Chemical Process Control • Reasons to do Process Control – Safety – Production Specifications – Environmental Regulations – Operational Constraints – Economics • Tasks the Control System Should Perform – Suppress the effects of external disturbances – Ensure the stability of the process – Optimize the performance of the process Control System Process Monitoring Intervention
- 46. University of Houston - CHEE3367 - Modeling for Control - Kaspar 46 Hierarchy of Process Control activities 1. Measurement and Actuation 2. Safety, Environment and Equipment Protection 3a. Regulatory Control 4. Real-Time Optimization 5. Planning and Scheduling Process 3b. Multivariable and Constraint Control (days-months ) (< 1 second) (< 1 second) (seconds-minutes) (minutes-hours ) (hours-days ) Figure 1.8 Hierarchy of process control activities.
- 47. University of Houston - CHEE3367 - Modeling for Control - Kaspar 47 Major Steps in Control System Development Figure 1.10 Major steps in control system development