PROCESS CONTROL DESIGN PASI 2005
|
|
- Corey Knight
- 6 years ago
- Views:
Transcription
1 PROCESS CONTROL DESIGN PASI 25 Pan American Advanced Studies Institute Program on Process Systems Engineering August 16-25, 25, Iguazu Falls Thomas E. Marlin Department of Chemical Engineering And McMaster Advanced Control Consortium McMASTER U N I V E R S I T Y 128 Main Street West, Hamilton, ON, Canada L8S 4L7 Copyright 25 by Thomas Marlin
2 Design is a challenging task. We must use all of our technical and problem-solving skills. Where do we start? When are we finished? v8 F2 F1 T1 T3 T4 v3 T5 F3 T6 F4 F5 P1 v1 v2 L1 v5 v7 v6 T7 L2 T2 T8 Hot Oil T9 Hot Oil F6
3 You will be the leaders in the PSE community; practitioners, educators, and researchers. You need to know The process needs, independent of PSE technology F2 F1 T1 T3 v1 v2 T2 Hot Oil T4 v3 T5 L1 T9 F3 v5 T6 F4 v7 T8 Hot Oil v6 F6 T7 F5 v8 P1 L2 The current best practices, strengths and gaps Principles informing research and practice Key breakthroughs in associated technologies
4 Course Goals Be able to define the control objectives for this goal-driven engineering task. Evaluate unique features of multivariable dynamic systems resulting from interaction Design simple control strategies using process insights and performance metrics Design challenging problems using a systematic, optimization method Become enthusiastic and investigate further
5 Course Resources Lecture Notes Annotated Reading List Solutions to Workshops (38 problems, 73 Pages) WEB sites Undergraduate Control: Graduate Control Design: This course will be a success if you study, apply, and improve good control design techniques
6 Theory & Practice Course Content Defining the Control Design Problem & Workshop Single-Loop Control Concepts & Workshop Multivariable Principles - Interaction & Workshop - Controllability & Workshop - Integrity & Workshop - Directionality & Workshop Slides provided but not covered because of limited me. Short-cut Design Procedure & Workshop Optimization-Based Control Design This is a small subset of the topics in the graduate course noted on the previous slide.
7 Course Emphasis So many great topics! A Foss: Key Challenge is which variables to measure, which inputs to manipulate, and what links should be made between these two sets CONTROL STRUCTURE! C. Nett: Objective minimize control system complexity subject to achievement of accuracy specifications SIMPLICITY! PERFORMANCE!
8 CONTROL DESIGN: DEFINING THE PROBLEM Control Design is a Goal-Driven Problem v8 F2 F1 T1 T3 T4 v3 T5 F3 T6 F4 F5 P1 v1 v2 L1 v5 v7 v6 T7 L2 T2 T8 Hot Oil T9 Hot Oil F6 The problem is defined based on knowledge of safety, process technology, sales, market demands, legal requirements, etc. Process systems technology is applied to achieve these goals.
9 CONTROL DESIGN: DEFINING THE PROBLEM Outline of the Topic. Defining the control design problem - Categories Measures of control performance - Controlled variable - Manipulated variable Benefits of reduced variation - Class exercise Workshop
10 CONTROL DESIGN: DEFINING THE PROBLEM The Control Design Form The form provides a useful check list for items that should be considered. It also provides a concise yet complete presentation of the important decisions that must be reviewed by all stake holders. The objectives and performance descriptions must be stated in process terms, not limited by anticipated control performance. See Marlin (2), Chapter 24 CONTROL DESIGN FORM TITLE: ORGANIZATION: PROCESS UNIT: DESIGNER: DRAWING ORIGINAL DATE: PROCESS FLOW: REVISION No. DATE: PIPING AND INSTR.: PAGE No. OF CONTROL OBJECTIVES: 1) SAFETY OF PERSONNEL 2) ENVIRONMENTAL PROTECTION 3) EQUIPMENT PROTECTION 4) SMOOTH, EASY OPERATION 5) PRODUCT QUALITY 6) EFFICIENCY AND OPTIMIZATION 7) MONITORING AND DIAGNOSIS [Define the control performance required by specifying standard deviations, approach to constraint, response to major upset, or other relevant, quantitative performance measure for each objective.] MEASUREMENTS: VAR. SENSOR RANGE ACCURACY&DYNAMICS RELIABILITY PRINCIPLE LOCATION REPRODUCIBILITY MANIPULATED VARIABLES: VAR. AUTO/ LOCAL/ CONTIN. RANGE PRECISION DYNAMICS RELIABIL. MANUAL REMOTE DISCRETE CONSTRAINTS: VARIABLE LIMIT VALUES MEASURED/ PENALTY FOR VIOLATION INFERRED DISTURBANCES: SOURCE MAGNITUDE FREQUENCY MEASURED? DYNAMIC RESPONSES: (ALL INPUTS, MANIPULATED AND DISTURBANCE) INPUT OUTPUT GAIN DYNAMIC MODEL ADDITIONAL CONSIDERATIONS: CURRENT OPERATION: CONTROL DESIGN: (INCLUDE DRAWING) BENEFITS: COSTS:
11 CONTROL DESIGN: DEFINING THE PROBLEM Let s design controls for this process How do we start? What are the common steps? When are we finished? T6 P1 Vapor Product, mostly C 1 and C 2 (L. Key) Feed Methane Ethane (LK) Propane Butane Pentane T1 F1 T2 T4 T5 T3 L1 P 1 kpa T 298 K F2 Process fluid F3 Steam A1 L. Key Liquid product
12 CONTROL DESIGN: DEFINING THE PROBLEM Control Design Form Objectives 1. Safety 2. Environmental protection 3. Equipment protection 4. Smooth operation 5. Product quality WORKSHOP: Complete a Control Design Form Vapor product 6. Profit Measurements 7. Monitoring and diagnosis T6 P1 Manipulated variables T4 Constraints T1 T2 T5 Disturbances F1 T5 L1 Dynamic responses Additional considerations F2 T3 F3 Process fluid Steam A1 L. Key Liquid product
13 CONTROL DESIGN: DEFINING THE PROBLEM Entries must be specific and measurable to guide design CONTROL OBJECTIVES: 1) SAFETY OF PERSONNEL a) the maximum pressure of 12 kpa must not be exceeded under any (conceivable) circumstances 2) ENVIRONMENTAL PROTECTION a) material must not be vented to the atmosphere under any circumstances 3) EQUIPMENT PROTECTION a) the flow through the pump should always be greater than or equal to a minimum 4) SMOOTH, EASY OPERATION a) the feed flow should have small variability 5) PRODUCT QUALITY a) the steady-state value of the ethane in the liquid product should maintained at its target of 1 mole% for operating condition changes of +2 to -25% feed flow, 5 mole% changes in the ethane and propane in the feed, and -1 to +5 C in the feed temperature. b) the ethane in the liquid product should not deviate more than ±1 mole % from its set point during transient responses for the following disturbances i) the feed temperature experiences a step from to 3 C ii) the feed composition experiences steps of +5 mole% ethane and -5 mole% of propane iii) the feed flow set point changes 5% in a step 6) EFFICIENCY AND OPTIMIZATION a) the heat transferred should be maximized from the process integration exchanger before using the more expensive steam utility exchanger 7) MONITORING AND DIAGNOSIS a) sensors and displays needed to monitor the normal and upset conditions of the unit must be provided to the plant operator b) sensors and calculated variables required to monitor the product quality and thermal efficiency of the unit should be provided for longer term monitoring
14 CONTROL DESIGN: DEFINING THE PROBLEM Entries must be specific and measurable to guide design CONSTRAINTS: VARIABLE LIMIT VALUES MEASURED/ HARD/ PENALTY FOR VIOLATION INFERRED SOFT drum pressure 12 kpa, high P1, measured hard personnel injury drum level 15%, low L1, measured hard pump damage Ethane in F5 ± 1 mole%, A1, measured & soft reduced selectivity in product (max deviation) T6, inferred downstream reactor DISTURBANCES: SOURCE MAGNITUDE DYNAMICS feed temperature (T1) -1 to 55 C infrequent step changes of 2 C magnitude feed rate (F1) 7 to 18 set point changes of 5% at one time feed composition ±5 mole% feed ethane frequent step changes (every 1-3 hr) For an excellent problem definition, see the Tennessee Eastman design challenge problem. Downs, J. and E. Vogel (1993) A Plant-wide Industrial Process Control Problem, Comp. Chem. Engr., 17,
15 CONTROL DESIGN: DEFINING THE PROBLEM What measures of control performance would we use? 1.5 Controlled Variable Time Manipulated Variable Time
16 CONTROL DESIGN: DEFINING THE PROBLEM = IAE = SP(t)-CV(t) dt A B.5 B/A = Decay ratio Return to set point, zero offset Rise time Time 1.5 C/D = Maximum overshoot of manipulated variable 1 C.5 D Time
17 CONTROL DESIGN: DEFINING THE PROBLEM = IAE = SP(t)-CV(t) dt.8.6 Maximum CV deviation from set point Time Time
18 CONTROL DESIGN: DEFINING THE PROBLEM An important, but often overlooked, factor All objectives and CVs are not of equal importance! CV Priority Ranking A product A recycle
19 CONTROL DESIGN: DEFINING THE PROBLEM An important, but often overlooked, factor Our primary goal is to maintain the CV near the set point. Besides not wearing out the valve, why do we have goals for the MV? Steam flow Manipulated Variable Time AC
20 CONTROL DESIGN: DEFINING THE PROBLEM Our primary goal is to maintain the CV near the set point. Besides not wearing out the valve, why do we have goals for the MV? PI 1 AT 1 PI 4 Fuel flow FT 1 TI 1 PI 5 TI 2 5 TI 2 15 PT 1 TI 3 TI TI 6 TI 7 TC TI 1 Manipulated Variable FT 2 TI 8 FI 3 TI 11 Time PI 2 PI 3 PI 6 Fuel For more on Life Extending Control, see Li, Chen, and Marquez (23)
21 CONTROL DESIGN: DEFINING THE PROBLEM What measures of control performance would we use? Controlled Variable Time Manipulated Variable Time
22 CONTROL DESIGN: DEFINING THE PROBLEM Often, the process is subject to many large and small disturbances and sensor noise. The performance measure characterizes the variability. 2 Controlled Variable 1-1 Variance or standard deviation of CV Time 2 Manipulated Variable 1-1 Variance or standard deviation of MV Time
23 CONTROL DESIGN: DEFINING THE PROBLEM Process performance = efficiency, yield, production rate, etc. It measures performance for a control objective. Calculate the process performance using the CV distribution, not the average value of the key variable!
24 Class Exercise: Benefits for reduced variability for chemical reactor Goal: Maximize conversion of feed ethane but do not exceed 864C Which operation, A or B, is better and explain why. A B
25 Class Exercise: Benefits for reduced variability for boiler A Goal: Maximize efficiency and prevent fuel-rich flue gas Which operation, A or B, is better and explain why. B
26 DEFINING THE PROBLEM: Workshop 1 Complete a control design form for a typical 2-product distillation tower. Make reasonable assumptions and note questions you would ask to verify your assumptions. Note that the figure is not complete; you are allowed to make changes to sensors and final elements.
27 DEFINING THE PROBLEM: Workshop 2 Complete a control design form for a typical fired heater. Make reasonable assumptions and note questions you would ask to verify your assumptions. Note that the figure is not complete; you are allowed to make changes to sensors and final elements.
28 DEFINING THE PROBLEM: Workshop 3 Typically, we have only a steady-state flowsheet (if that) when designing a plant. Discuss the information in the Control Design Form that can be determined at this stage of the design. Discuss the information in the Control Design Form that is not known at this stage of the design.
29 DEFINING THE PROBLEM: Workshop 4 Two process examples show the benefit of reduced variability, the fired heater reactor and the boiler. Discuss the difference between the two examples. Can you think of another example that shows the principle of each? Squeeze down the variability.4 frequency of occurrence deviation from mean
30 DEFINING THE PROBLEM: Workshop 5 Discuss an important assumption that is made on the procedure proposed for calculating the average process performance. (Hint: consider dynamics) How would you evaluate the assumption?
31 DEFINING THE PROBLEM: Workshop 6 The following performance vs. process variable correlations are provided. All applications require the same average value for the process variable (see arrow). What is the best distribute for each case? (Sketch histogram as your answer.) Distribution A Distribution B Distribution C Process performance Process performance Process performance Process variable Process variable Process variable
32 Principles of Single-Loop Feedback Control Multiloop control contains many single-loop systems. Conclusion: We need to understand single-loop principles. Vapor product, C2- Lesson Outline? TC6 PC1? Ten observations on what affects single-loop feedback T5 T3 LC1? Workshop F3 Steam A1 L. Key Liquid product, C3+
33 Principles of Single-Loop Feedback Control Performance Observations #. Very obvious, but not so obvious for multivariable systems. Process gain must not be zero (K p ). Gain should not be too small (range) or too large (sensitivity). Which valves can be used to control each measured variable? Would the answer change if many singleloops were implemented at the same time? F1 T A Product concentration L F2
34 Principles of Single-Loop Feedback Control Performance Observation #1. Feedback dead time limits best possible performance Controlled Variable S-LOOP plots deviation variables (IAE = 9.791) Time 1.5 θ p, feedback dead time Discuss why the red area defines deviation from set point that cannot be reduced by any feedback. Manipulated Variable 1.5 T in v1 TC v Time
35 Principles of Single-Loop Feedback Control Performance Observation #1. Feedback dead time limits best possible performance Controlled Variable S-LOOP plots deviation variables (IAE = ) Time θ p, feedback dead time Discuss why the red area defines deviation from set point that cannot be reduced by any feedback. Manipulated Variable T in v1 TC v Time
36 Principles of Single-Loop Feedback Control Performance Observation #2. Large disturbance time constants slow disturbances and improve performance..8 T in T in v1 TC v2 Controlled Variable Time.8 Please discuss v1 TC Controlled Variable Smaller maximum deviation v Time
37 Principles of Single-Loop Feedback Control Performance Observation #3. Feedback must change the MV aggressively to improve performance. Controlled Variable Manipulated Variable Kc = 1.3, TI = 7, Td = 1.5 S-LOOP plots deviation variables (IAE = ) Time Time Controlled Variable Manipulated Variable S-LOOP plots deviation variables (IAE = ) Time 5-5 Kc =.6, TI = 1, Td = Time Please discuss
38 Principles of Single-Loop Feedback Control Performance Observation #4. Sensor and final element dynamics are in feedback loop, slow responses degrade performance. 8 S-LOOP plots deviation variables (IAE = ) 8 S-LOOP plots deviation variables (IAE = ) Controlled Variable Manipulated Variable Time IAE = 88 θ 5, τ = 5, τ =, τ = p = p sensor valve Time Controlled Variable Manipulated Variable Time IAE = 113 θ 5, τ = 5, τ = 1, τ = 1 p = p sensor valve Time retuned
39 Principles of Single-Loop Feedback Control Performance Observation #5. Inverse response (RHP zero) degrades feedback control performance. Process reaction curve for the effect of solvent flow rate on the reactor effluent concentration. Two, isothermal CSTRs with reaction A B and F S >> F A reactant F A C A solvent F S C AS = C A1 V1 C A2 V2
40 Principles of Single-Loop Feedback Control Performance Observation #5. Inverse response degrades feedback control performance. PARALLEL STRUCTURES X(s) G 1 (s) Y(s) G 2 (s) Inverse response occurs when parallel paths have different signs for their steady-state gains and the path with the smaller magnitude gain is faster.
41 Principles of Single-Loop Feedback Control Performance Observation #6. Disturbance frequencies around and higher than the critical frequency cannot be controlled. Slow A B C medium fast T in v1 v2 TC Behavior of the tank temperature for three cases?
42 Principles of Single-Loop Feedback Control Performance Observation #6: Frequency Response Region 1 1 I: Control is needed, and it is effective 1 Region II: Control is needed, but it is not effective B Region III: Control is not needed, and it is not effective This is CV / D, small is good. Amplitude Ratio, CV / D A Recall, this is the disturbance Frequency, frequency, w (rad/time) low frequency = long period C
43 Performance Observation #6. Disturbance frequencies around and higher than the critical frequency cannot be controlled. Let s apply frequency response concepts to a practical example. Can we reduce this open-loop variation? T in v1 A v2 Feedback dynamics are: A(s) v(s) 2s 1. e = 2s + 1 We note that the variation has many frequencies, some much slower than the feedback dynamics.
44 Performance Observation #6. Disturbance frequencies around and higher than the critical frequency cannot be controlled. Yes, we can we reduce the variation substantially because of the dominant low frequency of the disturbance effects. T in v1 AC v2 Feedback dynamics are: A(s) v(s) = 1. e 2s 2s + 1 Low frequencies reduced a lot. Higher frequencies remain!
45 Principles of Single-Loop Feedback Control Performance Observation #7. Long controller execution periods degrade feedback control performance..8 S-LOOP plots deviation variables (IAE = ) T in v1 TC v2 Controlled Variable Time -.2 Manipulated Variable Time
46 Principles of Single-Loop Feedback Control Performance Observation #8. Use process understanding to provide a CV-MV pairing with good steady-state and dynamic behavior. Which valve Should be adjusted for good control performance? feed v1 AC A CSTR with A B v2 cooling
47 Principles of Single-Loop Feedback Control Performance Observation #8. Use process understanding to provide a CV-MV paring with good steady-state and dynamic behavior. v1 Component material balance v1 CSTR with A B v2 Energy balance Component material balance AC A v1 gives faster feedback dynamics v2 cooling
48 Principles of Single-Loop Feedback Control Performance Observation #9. Some designs require a loop to adjust two valves to achieve the desired range and precision. Two heating valves are available for manipulation. T1 T2 Split range T5 TC PC-1 Vapor product Adjust only one at a time. FC-1 T3 LC-1 F2 Process fluid F3 Steam L. Key AC-1 Liquid product
49 Principles of Single-Loop Feedback Control Performance Observation #1. Some designs require several loops to adjust the same manipulated variable to ensure that the highest priority objective is achieved. Control the effluent concentration of A but do not exceed a maximum reactor temperature. Only the cooling medium may be adjusted. signal select
50 Single-loop Control, Workshop #1 What if the % ethane is sometimes 2% and other times 5%? TC6 PC1 Feed Methane Ethane (LK) Propane Butane Pentane T5 T3 LC1 Vapor product, C2- F3 Steam A1 L. Key Liquid product, C3+
51 Single-loop Control, Workshop #2 The consumers vary and we must satisfy them by purchasing fuel gas. Therefore, we want to control the pressure in the gas distribution network. Design a control system. By the way, fuel A is less expensive.
52 Single-loop Control, Workshop #3 Design a controller that will control the level in the bottom of the distillation tower and send as much flow as possible to Stream A
53 Single-loop Control, Workshop #4 Stream A (cold) 2 1 Stream A (cold) Stream B (hot) TC TC Freedom to adjust flows Stream A Stream B 1. Constant Adjustable 2. Adjustable Constant 3. Constant Constant 3 Stream A (cold) TC Stream B (hot) Stream B (hot) You can add valve(s) and piping.
54 Single-loop Control, Workshop #5 Class exercise: Distillation overhead system. Design a pressure controller. (Think about affecting U, A and T) PC CW NC No vapor product You can add valve(s) and piping.
55 Single-loop Control, Workshop #6 Class exercise: Distillation overhead system. Design a pressure controller. (Think about affecting U, A and T) PC Refrigerant LC NC You can add valve(s) and piping.
56 Single-loop Control, Workshop #7 The following control system has a very large gain near ph = 7. For a strong acid/base, performance is likely to be poor. How can we improve the situation? F1 Strong base ph A1 C L Strong acid
57 Principles of Multivariable Dynamic Processes and MultiLoop Control Basically, multiloop designs are simply many single-loop (PID) controllers. Then, what is new? ** INTERACTION ** When we adjust one valve, how many measurements change and what are responses? Step valve v8 F2 F1 T1 T3 T4 v3 T5 F3 T6 F4 F5 P1 v1 v2 L1 v5 v7 v6 T7 L2 T2 T8 Hot Oil T9 Hot Oil F6
58 Principles of Multivariable Dynamic Processes and MultiLoop Control ** INTERACTION ** LESSON OUTLINE Interaction A brief definition Controllability Can desired performance be achieved? Integrity What happens to the system when a controller stops functioning? Directionality A key factor in control performance Class Exercises and Workshops throughout
59 INTERACTION: The difference in multivariable control Definition: A multivariable process has interaction when input (manipulated) variables affect more than one output (controlled) variable Step change to reflux with constant reboiler XD (mol frac) Time (min) x 1 4 XB (mol frac) Time (min) x R (mol/min) V (mol/min) Time (min) Time (min)
60 Multivariable Interaction, Workshop #1-3 m 3 /h -6 m 3 /h F A, x A =1 F S, x AS = F M, x AM Total flow, FM Composition (fraction A), x AM The ranges of the two mixing flows are given in the figure. Sketch the feasible steadystate operating window in the Figure. Note: You may assume no disturbances for this exercise.
61 Controllability: Is the desired performance achievable? Manipulated variables Disturbances T1 F1 T4 T2 T5 T5 T6 P1 L1 Vapor product Control Design Form 1. Safety 2. Environmental protection 3. Equipment protection 4. Smooth operation 5. Product quality 6. Profit 7. Monitoring and diagnosis Controlled F2 T3 F3 variables Process fluid Can we control the CVs with the MVs? If the required performance is not achievable, fix the process; don t design controllers!
62 Controllability: Is the desired performance achievable? CONTROLLABILITY -A characteristic of the process that determines whether a specified dynamic behavior can be achieved with a defined set of controlled and manipulated variables and a defined scenario. Various specifications for dynamic behavior are possible; we will review a few commonly used. We seek a fundamental property of the process independent of a specific control design or structure.
63 Controllability: Is the desired performance achievable? Interaction influences Controllability. Is this behavior possible? Goals: Maintain cold effluent T 1 and Maintain hot effluent at T 2 final steady-states = set points Freedom to adjust flows Stream A Stream B Adjustable Adjustable Stream A (cold) Stream B (hot) T2 T1
64 Controllability: Is the desired performance achievable? Interaction influences Controllability. Quick check for each loop T2 Can we control T2 with v2? YES Stream A (cold) v1 T1 Can we control T1 with v1? YES v2 Stream B (hot) Since each individual loop is OK, both loops are OK?
65 Controllability: Is the desired performance achievable? Interaction influences Controllability. Energy balance on each stream Q Q Hot Cold = = F Hot F C Cold p C H p ( T C Hout ( T Cout T Hin T Cin ) ) Stream A (cold) v1 T2 T1 v2 Stream B (hot)
66 Controllability: Is the desired performance achievable? A few typical controllability performance specifications Steady-state: Achieve desired steady-state when disturbances occur For processes that operate at steady-state, but no information about transition. Point-wise state (output): Move to specified initial values of states (or CVs) to final values in finite time Often in textbooks. Perhaps, useful for batch end-point control. Functional: Strictly follow any defined trajectory for CVs Useful for transition control between steady states and for batch processes. However, likely too restrictive (strictly, any).
67 Controllability: Is the desired performance achievable? Steady-state Controllable: A mathematical test. The system will be deemed controllable if the steady-state I/O gain matrix can be inverted, i.e., Det [ G() ] [G()] -1 exists This is only applicable to open-loop stable plants. It is a point-wise test that gives no (definitive) information about other conditions. No information about the transient behavior or the changes to MV s to achieve the desired CV s (set points).
68 Controllability: Is the desired performance achievable? Pointwise State Controllable: A process example for p.s. controllability. T 1 = x 1 T 2 = x 2 T 3 = x 3 T 4 = x 4 From Skogestad & Postlethwaite, 1996 u =T One MV and four CVs. The control performance can be achieved! Nothing is specified for MV at any time or for CV between t and t 1 or after the time t 1.
69 Controllability: Is the desired performance achievable? Functional Controllable* - A system is controllable if it is possible to adjust the manipulated variables MV(t) so that the system will follow a (smooth) defined path from CV(t ) to CV(t 1 ) in a finite time. A system G(s) is (output) functionally controllable when dimensions of CV and MV are the same (say n) The rank of G(jω) = n Stated differently, G -1 (jω ) exists for all ω Stated again, σ min (G(jω)) > [minimum singular value] Unfortunately, dead times and RHP zeros prevent the controller from implementing the inverse for most process. Also, no specification on the MV s.
70 Controllability: Is the desired performance achievable? Class Exercise: Let s define controllability with a test that is computable. MV 1 G 11 (s) + + CV 1 G 21 (s) G d1 (s) D G 12 (s) G d2 (s) MV 2 G 22 (s) + + CV 2
71 Controllability: Is the desired performance achievable? Controllability Test: Solve as open-loop optimization problem, which can be an LP or convex QP. If all violation slacks (s 2n ) on the performance specifications are zero, i.e. if f =, the system is controllable!, f u n o o n n n n n n n n n n n n n n SP n n n n SP n n n n n n n n n n n d y x u given s s s s s s u u u u u u u y s s y y s s y Cx y Dd Bu Ax x t s s s 2 2 max 1 1 max 1 1 max 1 min max min ,,, ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) (.. ) ( min = + + = + = Note that in the formulation, slacks s 1n define allowable deviation from desired output, and s 2n are violations for excessive deviation.
72 Controllability: Is the desired performance achievable? Example of controllability test for 2x2 Fluidized Catalytic Cracking Reactor Product riser Must be tightly controlled Large fluidized vessel regenerator Fcat T Tris Tubular reactor With short space time T Trgn Feed Keep in safe range Fair
73 Controllability: Is the desired performance achievable? Example of controllability test for 2x2 Fluidized Catalytic Cracking Reactor FCC Case 1 Tris SP = +1 F at t= With no bounds on the speed of adjustment, the set points can be tracked exactly. Is the system controllable? Trgn (F) Tris (F) time (sec) time (sec) Fair (lb air/lb feed) Fcat (lb cat/lb feed) time (sec) time (sec)
74 Controllability: Is the desired performance achievable? Example of controllability test for 2x2 Fluidized Catalytic Cracking Reactor FCC Case 2 Tris SP = +1 F at t= With bounds on the speed of adjustment, Fair.1 (lb air/lb feed)/2sec Fcat.1 (lb cat/lb feed)/2sec Is the system controllable? Trgn (F) Tris (F) time (sec) time (sec) Fair (lb air/lb feed) Fcat (lb cat/lb feed) time (sec) time (sec)
75 Controllability: Is the desired performance achievable? Evaluation of the proposed approach GOOD ASPECTS Can define relevant timedomain performance specifications - on CVs (or states) -on MVs Great flexibility on form of specifications Easily computed No limitation on disturbance Includes most other definitions/tests as special cases SHORTCOMINGS Linear Model No robustness measure No guarantee that any controller will achieve the performance Finite horizon True for all controllability tests
76 Controllability: Is the desired performance achievable? Determining controllability from process sight Lack of controllability when 1. One CV cannot be effected by any valve 2. One MV has no effect on CVs These are generally easy to determine. This requires care and process insight to determine. 3. Lack of independent effects. Look for contractions
77 CONTROLLABILITY : Workshop 1 We need to control the mixing tank effluent temperature and concentration. F 1 T 1 C A1 F 2 T 2 C A2 You have been asked to evaluate the steady-state controllability of the process in the figure. Discuss good and poor aspects and decide whether you would recommend the design. T A Controlled variables are the temperature and concentration in the tank effluent.
78 CONTROLLABILITY : Workshop 2 The sketch describes a simplified boiler for the production of steam. The boiler has two fuels that can be manipulated independently. We want to control the steam temperature and pressure. Analyze the controllability of this system and determine the loop pairing.
79 CONTROLLABILITY : Workshop 3 The sketch describes a simplified flash drum. A design is proposed to control the temperature and pressure of the vapor section. Analyze the controllability of this system and determine if the loop pairing is correct. Hot streams Feed Methane Ethane (LK) Propane Butane Pentane vapor liquid
80 CONTROLLABILITY : Workshop 4 A non-isothermal CSTR Does interaction exist? Are the CVs (concentrations) independently controllable? A B + 2C -r A = k e -E/RT C A G 11 (s) + + v1 G 21 (s) G d1 (s) C B A A G 12 (s) G d2 (s) C C G 22 (s) + + v2
81 Integrity: Is the design robust to changes in controller status? FC AC LC What is the effect of turning off AC (placing in manual)? TC We will first introduce the Relative Gain; then, we will apply it to the Integrity Question
82 RELATIVE GAIN: Definition The relative gain between MV j and CV i is λ ij. It is defined in the following equation. MV 1 (s) G 11 (s) G 21 (s) G 12 (s) + + G d1 (s) G d2 (s) CV 1 (s) D(s) Explain in words. MV 2 (s) G 22 (s) + + CV 2 (s) λ ij = CV MV i CV MV j i j MV CV k k = constant = constant = CV MV CV MV i i j j other loops other loops open closed What have we assumed about the other controllers?
83 RELATIVE GAIN: Properties 1. The RGA can be calculated from openloop gains (only). λ ij = CV MV i CV MV j i j MV = constant k CV = constant k = CV MV CV MV i i j j other loops other loops open closed [ CV ] = [ K][ MV ] 1 [ MV ] = [ K] [ CV ] k ij ki ij CV = i MV = MV i j CV The relative gain array is the element-by-element product of K with K -1. ( = product of ij elements) j MV i CV j Open-loop Closed-loop Λ ( ) T K ( k )( ki ) 1 λ = K = ij ij ji
84 RELATIVE GAIN: Properties 2. In some cases, the RGA is very sensitive to small errors in the gains, K ij. λ ij = CV MV i CV MV j i j MV = constant k CV = constant k = CV MV CV MV i i j j other loops other loops open closed λ 11 = 1 1 K K K K When this term is nearly equal 1. When is this equation very sensitive to errors in the individual gains?
85 RELATIVE GAIN: Properties 2. In some cases, the RGA is very sensitive to small errors in the gains, K ij. λ ij = CV MV i CV MV j i j MV = constant k CV = constant k We must perform a thorough study to ensure that numerical derivatives are sufficiently accurate! Change in F D used in finite difference for derivative λ 11 for a positive change in F D λ 11 for a negative change in F D Average λ 11 for positive and negative changes in F D 2% % % % Results for a distillation tower, from McAvoy, 1983 The δx must be sufficiently small (be careful about roundoff).
86 RELATIVE GAIN: Properties 2. In some cases, the RGA is very sensitive to small errors in the gains, K ij. λ ij = CV MV i CV MV j i j MV = constant k CV = constant k We must perform a thorough study to ensure that numerical derivatives are sufficiently accurate! Convergence tolerance of equations (some of all errors squared) λ 11 for a positive change in F D λ 11 for a negative change in F D Average λ 11 for positive and negative changes in F D Results for a distillation tower, from McAvoy, 1983 The convergence tolerance must be sufficiently small. Average gains from +/-
87 RELATIVE GAIN: Properties 3. We can evaluate the RGA of a system with integrating processes, such as levels. λ ij = CV MV i CV MV j i j MV = constant k CV = constant k = CV MV CV MV i i j j other loops other loops open closed m 1 Redefine the output as the derivative of the level; then, calculate as normal. ρ = density L A D = density solvent m 2 dl / D dt (1 m 1 D / ρ D / ρ) (1 m 2 D / ρ) D / ρ
88 RELATIVE GAIN: Properties Additional Properties, stated but not proved here 4. Rows and column of RGA sum to Elements in RGA are independent of variable scalings 6. Permutations of variables results in same permutation in RGA 7. RGA is independent of a specific I/O pairing it need be evaluated only once
89 RELATIVE GAIN: Interpretations MV j CV i λ ij = CV MV i CV MV j i j MV = constant k CV = constant k = CV MV CV MV i i j j other loops other loops open closed λ ij < In this case, the steady-state gains have different signs depending on the status (auto/manual) of the other loops. A Solvent v A v S A1 C A CSTR with A B Discuss interaction What sign is process gain of A2 loop with A1 C A A2 (a) in automatic (b) in manual.
90 RELATIVE GAIN: Interpretations MV j CV i λ ij = CV MV i CV MV j i j MV = constant k CV = constant k = CV MV CV MV i i j j other loops other loops open closed λ ij < In this case, the steady-state gains have different signs depending on the status (auto/manual) of other loops! We can achieve stable multiloop feedback by using the sign of the controller gain that stabilizes the multiloop system. Discuss what happens when the other interacting loop is placed in manual!
91 RELATIVE GAIN: Interpretations MV j CV i λ ij = CV MV i CV MV j i j MV = constant k CV = constant k = CV MV CV MV i i j j other loops other loops open closed λ ij < the steady-state gains have different signs For λ ij <, one of three BAD situations occurs 1. Multiloop is unstable with all in automatic. 2. Single-loop ij is unstable when others are in manual. 3. Multiloop is unstable when loop ij is manual and other loops are in automatic
92 RELATIVE GAIN: Interpretations MV j CV i λ ij = CV MV i CV MV j i j MV = constant k CV = constant k = CV MV CV MV i i j j other loops other loops open closed λ ij = In this case, the steady-state gain is zero when all other loops are open, in manual. LC FC Could this control system work? What would happen if one controller were in manual?
93 RELATIVE GAIN: Interpretations MV j CV i λ ij = CV MV i CV MV j i j MV = constant k CV = constant k = CV MV CV MV i i j j other loops other loops open closed <λ ij <1 In this case, the multiloop (ML) steady-state gain is larger than the single-loop (SL) gain. What would be the effect on tuning of opening/closing the other loop? Discuss the case of a 2x2 system paired on λ ij =.1
94 RELATIVE GAIN: Interpretations MV j CV i λ ij = CV MV i CV MV j i j MV = constant k CV = constant k = CV MV CV MV i i j j other loops other loops open closed λ ij = 1 In this case, the steady-state gains are identical in both the ML and the SL conditions. What is generally true when λ ij = 1? Does λ ij = 1 indicate no interaction? MV 1 (s) G 11 (s) G 21 (s) G 12 (s) + + G d1 (s) G d2 (s) CV 1 (s) D(s) MV 2 (s) G 22 (s) + + CV 2 (s)
95 RELATIVE GAIN: Interpretations λ ij = 1 In this case, the steady-state gains are identical in both the ML and the SL conditions. λ ij = CVi MV j CVi MV j MV = constant k CV = constant k = CVi MV CVi MV j j other loops open other loops closed Diagonal gain matrix Lower diagonal gain matrix K K = k 11 k k =.... k n1 k k Both give an RGA that is diagonal! 1 1 RGA = 1 = I 1 1
96 RELATIVE GAIN: Interpretations MV j CV i λ ij = CV MV i CV MV j i j MV = constant k CV = constant k = CV MV CV MV i i j j other loops other loops open closed 1<λ ij In this case, the steady-state multiloop (ML) gain is smaller than the single-loop (SL) gain. If a ML process has a smaller process gain, why not just increase the associated controller gain by λ ij? What would be the effect on tuning of opening/closing the other loop? Discuss a the case of a 2x2 system paired on λ ij = 1.
97 RELATIVE GAIN: Interpretations MV j CV i λ ij = CV MV i CV MV j i j MV = constant k CV = constant k = CV MV CV MV i i j j other loops other loops open closed λ ij = In this case, the gain in the ML situation is zero. We conclude that ML control is not possible. Have we seen this result before?
98 INTEGRITY Integral Stabilizability - Can be stabilized with feedback using same sign of controller gains as single-loop Increasingly restrictive Integral Controllability - Can be stabilized with feedback using same sign of controller gains as single-loop and all controllers can be detuned equally Integral Controllability with Integrity - Can be stabilized with feedback using same sign of controller gains as single-loop and some controllers can be placed off, on manual while retaining stability (with retuning) Decentralized Integral Controllability - Can be stabilized with feedback using same sign of controller gains as single-loop and any controller(s) can be detuned any amount
99 INTEGRITY INTEGRITY is strongly desired for a control design. SOME ASSUMPTIONS FOR RESULTS PRESENTED Limited to stable plants; if open-loop unstable plants, extensions to analysis are available All controllers have integral modes. They provide zero steady-state offset for asymptotically constant ( step-like ) inputs All simple loops ; variable structure (split range and signal select) are not considered unless explicitly noted. See references (Campo and Morari, Skogestad and Postlethwaite, etc.) for limitations on the transfer functions.
100 INTEGRITY Integral Stabilizability - Can be stabilized with feedback using same sign of controller gains as single-loop Integral Controllability - Can be stabilized with feedback using same sign of controller gains as single-loop and all controllers can be detuned equally Integral Controllability with Integrity - Can be stabilized with feedback using same sign of controller gains as single-loop and some controllers can be off ( manual ) while retaining stability (with retuning) Decentralized Integral Controllability - Can be stabilized with single-loop feedback using same sign of controller gains as any individual controller(s) can be detuned any amount Niederlinski Index Not very important Relative Gain Array* Important, no short-cut test * All possible sub-systems with controller(s) off
101 Integrity Workshop 1 The process in the figure is a simplified head box for a paper making process. The control objectives are to control the pressure at the bottom of the head box (P1) tightly and to control the slurry level (L) within a range. The manipulated variables are the slurry flow rate in (F lin ) and the air vent valve opening. 1. Determine the integrity of the two possible pairings based process insight. 2. Recommend which pairing should be used. 3. Discuss the integrity of the resulting system. Air Pulp and water slurry Paper mat on wire mesh
102 Integrity Workshop 2 The following transfer function matrix and RGA are given for a binary distillation tower. Discuss the integrity for the two loop pairings. AC XD( s) XB( s) = 3s.747e 12s e 11.75s + 1 s.667e 15s e 1.2s + 1 2s 2s F F R V ( s) ( s) FR FV AC XD XB
103 Integrity Workshop 3 The following transfer function matrix and RGA are given for a binary distillation tower. Discuss the integrity for the two loop pairings. XF AC XD F D XD( s) XB( s) =.747e 12s e 11.75s + 1 3s 3.3s.8e 5s + 1.8e 3s + 1 2s 2s F F D V ( s) ( s) F V XD FD.39 FV.61 AC XB XB.61.39
104 Integrity Workshop 4 We will consider a hypothetical 4 input, 4 output process. How many possible combinations are possible for the square mutliloop system? For the system with the RGA below, how many loop pairings have good integrity? mv1 mv2 mv3 mv4 CV1 1 CV CV CV 4 1
105 Integrity Workshop 5 The table presents RGA(1,1) for the same 2x2 process with different level controllers (considered part of the process ) and different operation conditions. What do you conclude about the effects of regulatory level controls and operating conditions on the RGA? Small distillation column XF F R F V AC XD AC XB Rel. vol = 1.2, R = 1.2 R min XD, XB Feed RGA RGA Comp..998, , , , , , , , , XF F V AC XD F D AC XB From McAvoy, 1983
106 Directionality: Its effect on Control Performance Let s gain insight about control performance and learn one short-cut metric Do we need more than AC Controllability Integrity (RGA) Before we design controls? XF AC XD F D F V AC AC XB λ(1,1) = 6.1 λ(1,1) =.39
107 Important Observation: Case 1 Design A FR XD FRB XB Design B FD XD FRB XB RGA = 6.9 AC RGA =.39 AC AC AC.988 IAE = ISE = IAE = ISE = IAE =.5956 ISE = IAE =.4577 ISE = e-5 XD, light key XB, light key XD, light key XB, light key SAM = SSM = SAM = SSM = SAM =.133 SSM = SAM = SSM =.1748 Reflux flow Time Reboiled vapor Time Reflux flow Time Reboiled vapor Time
108 Important Observation: Case 2 Design A FR XD Design B FD XD FRB XB FRB XB RGA = 6.9 AC RGA =.39 AC AC AC XD, light key IAE = ISE = XB, light key IAE = ISE =.3839 XD, light key IAE = ISE =.786 XB, light key IAE = ISE = Reflux flow SAM = SSM =.2517 Reboiled vapor SAM = SSM = Reflux flow SAM =.5154 SSM = Reboiled vapor SAM = SSM = Time Time Time Time
109 Directionality: Its effect on Control Performance Conclusion from the two observations (and much more evidence) The best performing loop pairing is not always the pairing with Relative gains elements nearest 1. The least interaction This should not be surprising; we have not established a direct connection between RGA and performance. So, what is going on?
110 Directionality: Its effect on Control Performance Strong directionality is a result of Interaction The best multivariable control design depends on the feedback and input! A key factor is the relationship between the feedback and disturbance directions. Disturbances in this direction are difficult to correct. Disturbances in this direction are easily corrected.
111 Directionality: Its effect on Control Performance Strong directionality is a result of Interaction Distillation with energy balance A Disturbance direction easily controlled A Disturbance direction not easily controlled
112 KEY MATHEMATICAL INSIGHT For Short-cut Metric Definition of the Laplace transform and take lim as s E( s) Directionality: Its effect on Control Performance = st E( t) e dt lim E( s) = E( t) dt s Measure of control performance, Large value = BAD By the way, this is an application of the method for evaluating the moment of a dynamic variable. n th moment : t n Y ( t) dt = ( 1) n d ds n n Y ( s) s=
113 Directionality: Its effect on Control Performance KEY MATHEMATICAL INSIGHT APPLIED FIRST TO A SINGLE-LOOP SYSTEM PI Controller 1/s D(s) G d (s) SP(s) E(s) + MV(s) + G C (s) G v (s) G P (s) - + CV m (s) G S (s) Apply this concept to a single-loop control system CV(s) Please verify E( s) Gd ( s) = D( s) 1+ Gp( s) Gc( s) E SL ( t) dt = K K D p T K I c
114 Directionality: Its effect on Control Performance Apply the approach just introduced to a 2x2 multiloop system Identify the key aspects - group terms! SP 1 (s) SP 2 (s) G c1 (s) MV 1 (s) MV 2 (s) G c2 (s) G 11 (s) G 21 (s) G 12 (s) G 22 (s) + + G d1 (s) G d2 (s) + + CV 1 (s) D(s) CV 2 (s) E iml ( t) dt = RDG f E ( t) dt ij tune isl
115 Directionality: Its effect on Control Performance E iml ( t) dt = RDG f E ( t) dt ij tune isl Relative Disturbance Gain dimensionless only s-s gains can be +/- and > or < 1. different for each disturbance Usually the dominant term for interaction Tune Factor Change in tuning for multi-loop Single-loop performance (dead times, large disturbances, etc. are bad)
116 Directionality: Its effect on Control Performance 1 K 1 K E iml Relative disturbance gain 1 K K d 2 K K d1 22 K11K22 ( t) dt = RDG f E ( t) dt 12 ij K K What unique information is here? What is this term? c c tune T T I I SL ML isl K K Typical range D p T K I c
117 Directionality: Its effect on Control Performance E iml ( t ) dt / E ( t ) dt isl = The dominant factor! RDG ij f tune The change in performance due to interaction in multiloop system RDG include feedback interaction (RGA) and disturbance direction
118 Directionality: Its effect on Control Performance E iml ( t) dt = RDG f E ( t) dt RDG is easily calculated ij tune 2x2 Multiloop General Square Multiloop isl 1 K 1 K 1 K K d 2 K K d1 22 K11K22 12 Multiloop : Single loop : RDG ij ( MV ) = D j ML [ MV ] [ MV ] ( MV ) j D ML SL SL = K = ( K 1 p P K d diagonal ) 1 K d
119 Directionality: Its effect on Control Performance Relative Disturbance Gain (RDG) Gives effects of Interaction on Performance E iml ( t Advantages ) dt Steady-state information Includes feedback and disturbance directions Direct measure of CV performance / E ( t ) dt isl = RDG ij f tune Shortcomings Allows +/- cancellation - large is bad performance - small might be good performance Represents only CV performance Measures only one aspect of CV behavior
120 Directionality: Its effect on Control Performance Some important results. You can prove them yourself. For a single set point change, RDG = RGA For a disturbance with same effect as an MV, the RDG = to 2. (depending on the output variable) For one-way interaction, RDG = 1 Decouple only for unfavorable directionality, i.e., large RDG Large RGA indicates poor performance for SP For these common disturbances, interaction is favorable and performance similar to SL! Performance similar to SL! Decoupling can make performance worse!
121 Process Example: FOSS packed bed chemical reactor O2 H2 TC FC A T C F Q T Q 1. Calculate the RGA and tuning 2. Select pairings and predict performance A. Temperature set point change (single SP) B. Quench pressure change (disturbance same as MV) = ) ( ) ( ) ( ) (.... s T s F s e s e s e s e s C s T Q Q s s s s A B Directionality: Its effect on Control Performance
122 Directionality: Its effect on Control Performance 1. Calculate the RGA and tuning O2 H2 For T - FQ and C - TQ pairing RGA = 13.7, Detune factor = 2. Kc T = -.115, TI T = 1.37 Kc C = -.61, TI C = Select pairings and predict performance A. For set point change, RDG = RGA = large!! Predict poor performance! B. For disturbance, RDG small (between and 2)! Predict good performance, near single-loop TC T Q F Q FC T A C
123 Directionality: Its effect on Control Performance EMLdt RGA ) fdetune ESLdt ( EMLdt ( RGA )( Kij / Kii ) fdetune ESLdt 1 IAE = ISE = IAE = ISE = 1.27 Set point change Looks like poor tuning! Discuss the performance Time Time
124 Directionality: Its effect on Control Performance Disturbance in quench pressure, which is through MV dynamics. Discuss the performance 1-1 IAE = ISE = E dt f ESLdt ML detune E ML dt = Time IAE = ISE = Time How can this good performance occur with high interaction?
125 ) (... ) ( ) ( ) ( ) (. s X s e s e s F s F s e s e s e s e s XB s XD F s s V R s s s s = AC AC Process Example: Binary distillation shown in figure. 1. Calculate the RGA, RDG and tuning 2. Predict performance A. XD set point change B. Feed composition disturbance XD XB F R F V XF Directionality: Its effect on Control Performance
126 Directionality: Its effect on Control Performance Distillation tower (R,V) with both controllers in automatic for XD set point change For input = SP XD, RDG = RGA = 6 RDG large indicates much worse than singleloop Transient confirms the short-cut prediction XD, light key Reflux flow IAE = ISE = XD.23 XB SAM = SSM = Time XB, light key Reboiled vapor IAE = ISE = SAM = SSM = Time Looks like poor tuning!
127 Directionality: Its effect on Control Performance Distillation for XD set point change Let s explain why the interaction is unfavorable From SP X D From FV X D F R AC F V Note that the interaction from the bottom loop tends to counteract the action of the XD controller! AC From F R X B Unfavorable interaction, poor performance
CONTROL OF MULTIVARIABLE PROCESSES
Process plants ( or complex experiments) have many variables that must be controlled. The engineer must. Provide the needed sensors 2. Provide adequate manipulated variables 3. Decide how the CVs and MVs
More informationCHAPTER 13: FEEDBACK PERFORMANCE
When I complete this chapter, I want to be able to do the following. Apply two methods for evaluating control performance: simulation and frequency response Apply general guidelines for the effect of -
More informationSolutions for Tutorial 10 Stability Analysis
Solutions for Tutorial 1 Stability Analysis 1.1 In this question, you will analyze the series of three isothermal CSTR s show in Figure 1.1. The model for each reactor is the same at presented in Textbook
More informationCHAPTER 10: STABILITY &TUNING
When I complete this chapter, I want to be able to do the following. Determine the stability of a process without control Determine the stability of a closed-loop feedback control system Use these approaches
More informationCHAPTER 15: FEEDFORWARD CONTROL
CHAPER 5: EEDORWARD CONROL When I complete this chapter, I want to be able to do the following. Identify situations for which feedforward is a good control enhancement Design feedforward control using
More informationSolutions for Tutorial 4 Modelling of Non-Linear Systems
Solutions for Tutorial 4 Modelling of Non-Linear Systems 4.1 Isothermal CSTR: The chemical reactor shown in textbook igure 3.1 and repeated in the following is considered in this question. The reaction
More informationProcess Unit Control System Design
Process Unit Control System Design 1. Introduction 2. Influence of process design 3. Control degrees of freedom 4. Selection of control system variables 5. Process safety Introduction Control system requirements»
More information3.1 Overview 3.2 Process and control-loop interactions
3. Multivariable 3.1 Overview 3.2 and control-loop interactions 3.2.1 Interaction analysis 3.2.2 Closed-loop stability 3.3 Decoupling control 3.3.1 Basic design principle 3.3.2 Complete decoupling 3.3.3
More informationMulti-Loop Control. Department of Chemical Engineering,
Interaction ti Analysis and Multi-Loop Control Sachin C. Patawardhan Department of Chemical Engineering, I.I.T. Bombay Outline Motivation Interactions in Multi-loop control Loop pairing using Relative
More informationIndex Accumulation, 53 Accuracy: numerical integration, sensor, 383, Adaptive tuning: expert system, 528 gain scheduling, 518, 529, 709,
Accumulation, 53 Accuracy: numerical integration, 83-84 sensor, 383, 772-773 Adaptive tuning: expert system, 528 gain scheduling, 518, 529, 709, 715 input conversion, 519 reasons for, 512-517 relay auto-tuning,
More informationMulti-Input Multi-output (MIMO) Processes CBE495 LECTURE III CONTROL OF MULTI INPUT MULTI OUTPUT PROCESSES. Professor Dae Ryook Yang
Multi-Input Multi-output (MIMO) Processes CBE495 LECTURE III CONTROL OF MULTI INPUT MULTI OUTPUT PROCESSES Professor Dae Ryook Yang Fall 2013 Dept. of Chemical and Biological Engineering Korea University
More informationOverview of Control System Design
Overview of Control System Design General Requirements 1. Safety. It is imperative that industrial plants operate safely so as to promote the well-being of people and equipment within the plant and in
More informationGuide to Selected Process Examples :ili3g eil;]iil
Guide to Selected Process Examples :ili3g eil;]iil Because of the strong interplay between process dynamics and control perfor mance, examples should begin with process equipment and operating conditions.
More informationSolutions for Tutorial 3 Modelling of Dynamic Systems
Solutions for Tutorial 3 Modelling of Dynamic Systems 3.1 Mixer: Dynamic model of a CSTR is derived in textbook Example 3.1. From the model, we know that the outlet concentration of, C, can be affected
More informationCHAPTER 3 : MATHEMATICAL MODELLING PRINCIPLES
CHAPTER 3 : MATHEMATICAL MODELLING PRINCIPLES When I complete this chapter, I want to be able to do the following. Formulate dynamic models based on fundamental balances Solve simple first-order linear
More informationOverview of Control System Design
Overview of Control System Design Introduction Degrees of Freedom for Process Control Selection of Controlled, Manipulated, and Measured Variables Process Safety and Process Control 1 General Requirements
More informationCHBE320 LECTURE XI CONTROLLER DESIGN AND PID CONTOLLER TUNING. Professor Dae Ryook Yang
CHBE320 LECTURE XI CONTROLLER DESIGN AND PID CONTOLLER TUNING Professor Dae Ryook Yang Spring 2018 Dept. of Chemical and Biological Engineering 11-1 Road Map of the Lecture XI Controller Design and PID
More informationProcess Control & Design
458.308 Process Control & Design Lecture 5: Feedback Control System Jong Min Lee Chemical & Biomolecular Engineering Seoul National University 1 / 29 Feedback Control Scheme: The Continuous Blending Process.1
More informationControl of MIMO processes. 1. Introduction. Control of MIMO processes. Control of Multiple-Input, Multiple Output (MIMO) Processes
Control of MIMO processes Control of Multiple-Input, Multiple Output (MIMO) Processes Statistical Process Control Feedforward and ratio control Cascade control Split range and selective control Control
More information= 1 τ (C Af C A ) kc A. τ = V q. k = k(t )=k. T0 e E 1. γ = H ρc p. β = (ρc p) c Vρc p. Ua (ρc p ) c. α =
TTK485 Robust Control Department of Engineering Cybernetics Spring 27 Solution - Assignment Exercise : Unstable chemical reactor a) A CSTR-reactor with heat exchange is shown in figure. Mass- and energy
More informationSolutions for Tutorial 5 Dynamic Behavior of Typical Dynamic Systems
olutions for Tutorial 5 Dynamic Behavior of Typical Dynamic ystems 5.1 First order ystem: A model for a first order system is given in the following equation. dy dt X in X out (5.1.1) What conditions have
More informationImproved Identification and Control of 2-by-2 MIMO System using Relay Feedback
CEAI, Vol.17, No.4 pp. 23-32, 2015 Printed in Romania Improved Identification and Control of 2-by-2 MIMO System using Relay Feedback D.Kalpana, T.Thyagarajan, R.Thenral Department of Instrumentation Engineering,
More informationCHAPTER 19: Single-Loop IMC Control
When I coplete this chapter, I want to be able to do the following. Recognize that other feedback algoriths are possible Understand the IMC structure and how it provides the essential control features
More informationDYNAMIC SIMULATOR-BASED APC DESIGN FOR A NAPHTHA REDISTILLATION COLUMN
HUNGARIAN JOURNAL OF INDUSTRY AND CHEMISTRY Vol. 45(1) pp. 17 22 (2017) hjic.mk.uni-pannon.hu DOI: 10.1515/hjic-2017-0004 DYNAMIC SIMULATOR-BASED APC DESIGN FOR A NAPHTHA REDISTILLATION COLUMN LÁSZLÓ SZABÓ,
More informationOverview of Control System Design
Overview of Control System Design Chapter 10 General Requirements 1. Safety. It is imperative that industrial plants operate safely so as to promote the well-being of people and equipment within the plant
More informationIndex. INDEX_p /15/02 3:08 PM Page 765
INDEX_p.765-770 11/15/02 3:08 PM Page 765 Index N A Adaptive control, 144 Adiabatic reactors, 465 Algorithm, control, 5 All-pass factorization, 257 All-pass, frequency response, 225 Amplitude, 216 Amplitude
More informationEnhanced Single-Loop Control Strategies Chapter 16
Enhanced Single-Loop Control Strategies Chapter 16 1. Cascade control 2. Time-delay compensation 3. Inferential control 4. Selective and override control 5. Nonlinear control 6. Adaptive control 1 Chapter
More informationProcess Solutions. Process Dynamics. The Fundamental Principle of Process Control. APC Techniques Dynamics 2-1. Page 2-1
Process Dynamics The Fundamental Principle of Process Control APC Techniques Dynamics 2-1 Page 2-1 Process Dynamics (1) All Processes are dynamic i.e. they change with time. If a plant were totally static
More informationCHAPTER 2 CONTINUOUS STIRRED TANK REACTOR PROCESS DESCRIPTION
11 CHAPTER 2 CONTINUOUS STIRRED TANK REACTOR PROCESS DESCRIPTION 2.1 INTRODUCTION This chapter deals with the process description and analysis of CSTR. The process inputs, states and outputs are identified
More informationEnhanced Single-Loop Control Strategies (Advanced Control) Cascade Control Time-Delay Compensation Inferential Control Selective and Override Control
Enhanced Single-Loop Control Strategies (Advanced Control) Cascade Control Time-Delay Compensation Inferential Control Selective and Override Control 1 Cascade Control A disadvantage of conventional feedback
More informationAnalysis and Design of Control Systems in the Time Domain
Chapter 6 Analysis and Design of Control Systems in the Time Domain 6. Concepts of feedback control Given a system, we can classify it as an open loop or a closed loop depends on the usage of the feedback.
More informationReal-Time Optimization (RTO)
Real-Time Optimization (RTO) In previous chapters we have emphasized control system performance for disturbance and set-point changes. Now we will be concerned with how the set points are specified. In
More informationSELECTION OF VARIABLES FOR REGULATORY CONTROL USING POLE VECTORS. Kjetil Havre 1 Sigurd Skogestad 2
SELECTION OF VARIABLES FOR REGULATORY CONTROL USING POLE VECTORS Kjetil Havre 1 Sigurd Skogestad 2 Chemical Engineering, Norwegian University of Science and Technology N-734 Trondheim, Norway. Abstract:
More informationFeedback Control of Linear SISO systems. Process Dynamics and Control
Feedback Control of Linear SISO systems Process Dynamics and Control 1 Open-Loop Process The study of dynamics was limited to open-loop systems Observe process behavior as a result of specific input signals
More informationTwin-Screw Food Extruder: A Multivariable Case Study for a Process Control Course
Twin-Screw Food Extruder: A Multivariable Case Study for a Process Control Course Process Control Course Overview Case Studies Realistic Industrial Problems Motivating to Students Integrate Techniques
More informationThe dos and don ts of distillation column control
The dos and don ts of distillation column control Sigurd Skogestad * Department of Chemical Engineering Norwegian University of Science and Technology N-7491 Trondheim, Norway Abstract The paper discusses
More informationThe dos and don ts of distillation column control
The dos and don ts of distillation column control Sigurd Skogestad * Department of Chemical Engineering Norwegian University of Science and Technology N-7491 Trondheim, Norway Abstract The paper discusses
More informationCBE495 LECTURE IV MODEL PREDICTIVE CONTROL
What is Model Predictive Control (MPC)? CBE495 LECTURE IV MODEL PREDICTIVE CONTROL Professor Dae Ryook Yang Fall 2013 Dept. of Chemical and Biological Engineering Korea University * Some parts are from
More informationAdvanced Chemical Reaction Engineering Prof. H. S. Shankar Department of Chemical Engineering IIT Bombay. Lecture - 03 Design Equations-1
(Refer Slide Time: 00:19) Advanced Chemical Reaction Engineering Prof. H. S. Shankar Department of Chemical Engineering IIT Bombay Lecture - 03 Design Equations-1 We are looking at advanced reaction engineering;
More informationMultiloop Control: Performance Analysis /n-i
Multiloop Control: Performance Analysis /n-i 21.1 m INTRODUCTION Multiloop process control systems were introduced in the previous chapter, where some important effects of interaction on steady-state and
More informationEconomic Model Predictive Control Historical Perspective and Recent Developments and Industrial Examples
1 Economic Model Predictive Control Historical Perspective and Recent Developments and Industrial Examples Public Trial Lecture Candidate: Vinicius de Oliveira Department of Chemical Engineering, Faculty
More informationFigure 4-1: Pretreatment schematic
GAS TREATMENT The pretreatment process consists of four main stages. First, CO 2 and H 2 S removal stage which is constructed to assure that CO 2 would not exceed 50 ppm in the natural gas feed. If the
More informationChemical Engineering 3P04 Process Control Tutorial # 1 Learning goals
Chemical Engineering 3P04 Process Control Tutorial # 1 Learning goals 1. Sensor Principles with the flow sensor example 2. The typical manipulated variable: flow through a conduit Sensors: We need them
More informationTHE DOS AND DON TS OF DISTILLATION COLUMN CONTROL
THE DOS AND DON TS OF DISTILLATION COLUMN CONTROL Sigurd Skogestad Department of Chemical Engineering, Norwegian University of Science and Technology, N-7491 Trondheim, Norway The paper discusses distillation
More informationSimulation of Quadruple Tank Process for Liquid Level Control
Simulation of Quadruple Tank Process for Liquid Level Control Ritika Thusoo 1, Sakshi Bangia 2 1 M.Tech Student, Electronics Engg, Department, YMCA University of Science and Technology, Faridabad 2 Assistant
More informationBITS-Pilani Dubai, International Academic City, Dubai Second Semester. Academic Year
BITS-Pilani Dubai, International Academic City, Dubai Second Semester. Academic Year 2007-2008 Evaluation Com anent: Com rehensive Examination Closed Book CHE UC441/11NSTR UC 45'1 PROCESS CONTROL Date:
More informationChapter 8. Feedback Controllers. Figure 8.1 Schematic diagram for a stirred-tank blending system.
Feedback Controllers Figure 8.1 Schematic diagram for a stirred-tank blending system. 1 Basic Control Modes Next we consider the three basic control modes starting with the simplest mode, proportional
More informationSpring 2006 Process Dynamics, Operations, and Control Lesson 5: Operability of Processes
5.0 context and direction In esson 4, we encountered instability. We think of stability as a mathematical property of our linear system models. Now we will embed this mathematical notion within the practical
More informationAnalyzing Control Problems and Improving Control Loop Performance
OptiControls Inc. Houston, TX Ph: 713-459-6291 www.opticontrols.com info@opticontrols.com Analyzing Control s and Improving Control Loop Performance -by Jacques F. Smuts Page: 1 Presenter Principal Consultant
More informationDesign of Decentralised PI Controller using Model Reference Adaptive Control for Quadruple Tank Process
Design of Decentralised PI Controller using Model Reference Adaptive Control for Quadruple Tank Process D.Angeline Vijula #, Dr.N.Devarajan * # Electronics and Instrumentation Engineering Sri Ramakrishna
More informationSubject: Introduction to Process Control. Week 01, Lectures 01 02, Spring Content
v CHEG 461 : Process Dynamics and Control Subject: Introduction to Process Control Week 01, Lectures 01 02, Spring 2014 Dr. Costas Kiparissides Content 1. Introduction to Process Dynamics and Control 2.
More informationSystems Engineering Spring Group Project #1: Process Flowsheeting Calculations for Acetic Anhydride Plant. Date: 2/25/00 Due: 3/3/00
10.551 Systems Engineering Spring 2000 Group Project #1: Process Flowsheeting Calculations for Acetic Anhydride Plant Date: 2/25/00 Due: 3/3/00 c Paul I. Barton, 14th February 2000 At our Nowhere City
More informationEECE 460. Decentralized Control of MIMO Systems. Guy A. Dumont. Department of Electrical and Computer Engineering University of British Columbia
EECE 460 Decentralized Control of MIMO Systems Guy A. Dumont Department of Electrical and Computer Engineering University of British Columbia January 2011 Guy A. Dumont (UBC EECE) EECE 460 - Decentralized
More informationChapter 1 Basic Characteristics of Control Systems and their Representation Process and Instrument Diagrams
Chapter 1 Basic Characteristics of Control Systems and their Representation Process and Instrument Diagrams We need ways to describe process control systems. We will learn several ways in this course.
More informationCONTROL OF REACTIVE DISTILLATION COLUMNS FOR AMYL ACETATE PRODUCTION USING DILUTE ACETIC ACID
Journal of the Chinese Institute of Engineers, Vol. 9, No., pp. 39-33 () 39 CONTROL OF REACTIVE DISTILLATION COLUMNS FOR AMYL ACETATE PRODUCTION USING DILUTE ACETIC ACID Wan-Jen Hung, I-Kuan Lai, Shih-Bo
More informationProcess Control Exercise 2
Process Control Exercise 2 1 Distillation Case Study Distillation is a method of separating liquid mixtures by means of partial evaporation. The volatility α of a compound determines how enriched the liquid
More informationApplication of Decomposition Methodology to Solve Integrated Process Design and Controller Design Problems for Reactor-Separator-Recycle Systems
Proceedings of the 9th International Symposium on Dynamics and Control of Process Systems (DYCOPS 2010), Leuven, Belgium, July 5-7, 2010 Mayuresh Kothare, Moses Tade, Alain Vande Wouwer, Ilse Smets (Eds.)
More informationProcess Classification
Process Classification Before writing a material balance (MB) you must first identify the type of process in question. Batch no material (mass) is transferred into or out of the system over the time period
More informationControl Systems I. Lecture 7: Feedback and the Root Locus method. Readings: Jacopo Tani. Institute for Dynamic Systems and Control D-MAVT ETH Zürich
Control Systems I Lecture 7: Feedback and the Root Locus method Readings: Jacopo Tani Institute for Dynamic Systems and Control D-MAVT ETH Zürich November 2, 2018 J. Tani, E. Frazzoli (ETH) Lecture 7:
More informationDetermining Controller Constants to Satisfy Performance Specifications
Determining Controller Constants to Satisfy Performance This appendix presents a procedure for determining the tuning constants for feed back controllers that satisfy robust, time-domain performance specifications.
More informationOptimizing Economic Performance using Model Predictive Control
Optimizing Economic Performance using Model Predictive Control James B. Rawlings Department of Chemical and Biological Engineering Second Workshop on Computational Issues in Nonlinear Control Monterey,
More informationProcess Control, 3P4 Assignment 6
Process Control, 3P4 Assignment 6 Kevin Dunn, kevin.dunn@mcmaster.ca Due date: 28 March 204 This assignment gives you practice with cascade control and feedforward control. Question [0 = 6 + 4] The outlet
More informationTime scale separation and the link between open-loop and closed-loop dynamics
Time scale separation and the link between open-loop and closed-loop dynamics Antonio Araújo a Michael Baldea b Sigurd Skogestad a,1 Prodromos Daoutidis b a Department of Chemical Engineering Norwegian
More information(Refer Slide Time: 1:42)
Control Engineering Prof. Madan Gopal Department of Electrical Engineering Indian Institute of Technology, Delhi Lecture - 21 Basic Principles of Feedback Control (Contd..) Friends, let me get started
More informationDesign of Multivariable Neural Controllers Using a Classical Approach
Design of Multivariable Neural Controllers Using a Classical Approach Seshu K. Damarla & Madhusree Kundu Abstract In the present study, the neural network (NN) based multivariable controllers were designed
More informationA Tuning of the Nonlinear PI Controller and Its Experimental Application
Korean J. Chem. Eng., 18(4), 451-455 (2001) A Tuning of the Nonlinear PI Controller and Its Experimental Application Doe Gyoon Koo*, Jietae Lee*, Dong Kwon Lee**, Chonghun Han**, Lyu Sung Gyu, Jae Hak
More informationUCLA Chemical Engineering. Process & Control Systems Engineering Laboratory
Constrained Innite-time Optimal Control Donald J. Chmielewski Chemical Engineering Department University of California Los Angeles February 23, 2000 Stochastic Formulation - Min Max Formulation - UCLA
More informationRecovering from a Gradual Degradation in MPC Performance
Preprints of the The International Federation of Automatic Control Furama Riverfront, Singapore, July 1-13, 212 Recovering from a Gradual Degradation in MPC Performance Mohammed Jimoh*, John Howell** *School
More informationTheoretical Models of Chemical Processes
Theoretical Models of Chemical Processes Dr. M. A. A. Shoukat Choudhury 1 Rationale for Dynamic Models 1. Improve understanding of the process 2. Train Plant operating personnel 3. Develop control strategy
More informationPlantwide Control of Chemical Processes Prof. Nitin Kaistha Department of Chemical Engineering Indian Institute of Technology, Kanpur
Plantwide Control of Chemical Processes Prof. Nitin Kaistha Department of Chemical Engineering Indian Institute of Technology, Kanpur Lecture - 41 Cumene Process Plantwide Control (Refer Slide Time: 00:18)
More informationDesign of Decentralized Fuzzy Controllers for Quadruple tank Process
IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.11, November 2008 163 Design of Fuzzy Controllers for Quadruple tank Process R.Suja Mani Malar1 and T.Thyagarajan2, 1 Assistant
More informationChE 6303 Advanced Process Control
ChE 6303 Advanced Process Control Teacher: Dr. M. A. A. Shoukat Choudhury, Email: shoukat@buet.ac.bd Syllabus: 1. SISO control systems: Review of the concepts of process dynamics and control, process models,
More informationCONTROLLER PERFORMANCE ASSESSMENT IN SET POINT TRACKING AND REGULATORY CONTROL
ADCHEM 2, Pisa Italy June 14-16 th 2 CONTROLLER PERFORMANCE ASSESSMENT IN SET POINT TRACKING AND REGULATORY CONTROL N.F. Thornhill *, S.L. Shah + and B. Huang + * Department of Electronic and Electrical
More informationPROPORTIONAL-Integral-Derivative (PID) controllers
Multiple Model and Neural based Adaptive Multi-loop PID Controller for a CSTR Process R.Vinodha S. Abraham Lincoln and J. Prakash Abstract Multi-loop (De-centralized) Proportional-Integral- Derivative
More informationDynamic simulation and Control of a CO 2 Compression and Purification Unit for Oxy-Coal-Fired Power Plants
Dynamic simulation and Control of a CO 2 Compression and Purification Unit for Oxy-Coal-Fired Power Plants Authors A. Chansomwong, K.E. Zanganeh, A. Shafeen, P.L. Douglas,E. Croiset, L.A. Ricardez-Sandoval,
More informationLecture 25: Manufacture of Maleic Anhydride and DDT
Lecture 25: Manufacture of Maleic Anhydride and DDT 25.1 Introduction - In this last lecture for the petrochemicals module, we demonstrate the process technology for Maleic anhydride and DDT. - Maleic
More informationFundamental Principles of Process Control
Fundamental Principles of Process Control Motivation for Process Control Safety First: people, environment, equipment The Profit Motive: meeting final product specs minimizing waste production minimizing
More informationChapter 4. Problem SM.7 Ethylbenzene/Styrene Column
Background Chapter 4. Problem SM.7 Ethylbenzene/Styrene Column In Problem SM.6 of the HYSYS manual, a modified form of successive substitution, called the Wegstein method, was used to close the material
More informationNGL EXTRACTION SPE - BACK TO BASICS. Ed Wichert Sogapro Engineering Ltd. September 27, 2011
NGL EXTRACTION SPE - BACK TO BASICS Ed Wichert Sogapro Engineering Ltd. September 27, 2011 DEFINITIONS NGL - C 2+ LPG - C 3 and C 4 LNG - C 1 Condensate - plant inlet separator hydrocarbon liquid Pentanes-Plus
More informationMIMO Identification and Controller design for Distillation Column
MIMO Identification and Controller design for Distillation Column S.Meenakshi 1, A.Almusthaliba 2, V.Vijayageetha 3 Assistant Professor, EIE Dept, Sethu Institute of Technology, Tamilnadu, India 1 PG Student,
More informationCHAPTER 5 ROBUSTNESS ANALYSIS OF THE CONTROLLER
114 CHAPTER 5 ROBUSTNESS ANALYSIS OF THE CONTROLLER 5.1 INTRODUCTION Robust control is a branch of control theory that explicitly deals with uncertainty in its approach to controller design. It also refers
More informationAutomatic Control 2. Loop shaping. Prof. Alberto Bemporad. University of Trento. Academic year
Automatic Control 2 Loop shaping Prof. Alberto Bemporad University of Trento Academic year 21-211 Prof. Alberto Bemporad (University of Trento) Automatic Control 2 Academic year 21-211 1 / 39 Feedback
More informationGoodwin, Graebe, Salgado, Prentice Hall Chapter 11. Chapter 11. Dealing with Constraints
Chapter 11 Dealing with Constraints Topics to be covered An ubiquitous problem in control is that all real actuators have limited authority. This implies that they are constrained in amplitude and/or rate
More informationEXAMINATION INFORMATION PAGE Written examination
EXAMINATION INFORMATION PAGE Written examination Course code: Course name: PEF3006 Process Control Examination date: 30 November 2018 Examination time from/to: 09:00-13:00 Total hours: 4 Responsible course
More informationProcess design decisions and project economics Dr. V. S. Moholkar Department of chemical engineering Indian Institute of Technology, Guwahati
Process design decisions and project economics Dr. V. S. Moholkar Department of chemical engineering Indian Institute of Technology, Guwahati Module - 02 Flowsheet Synthesis (Conceptual Design of a Chemical
More informationOnline monitoring of MPC disturbance models using closed-loop data
Online monitoring of MPC disturbance models using closed-loop data Brian J. Odelson and James B. Rawlings Department of Chemical Engineering University of Wisconsin-Madison Online Optimization Based Identification
More informationControl of Electromechanical Systems
Control of Electromechanical Systems November 3, 27 Exercise Consider the feedback control scheme of the motor speed ω in Fig., where the torque actuation includes a time constant τ A =. s and a disturbance
More informationEconomic Plant-wide Control. Sigurd Skogestad
JWST-c JWST-Rangaiah November, 0 : Printer: Yet to come 0 0 0 Economic Plant-wide Control. Introduction Sigurd Skogestad Department of Chemical Engineering, Norwegian University of Science and Technology
More informationAdvanced control If single-loop feedback control (PID) alone is not good enough
The decision hierarchy is based on time scale separation setpoints Advanced control (MPC) setpoints Fast regulatory control (PID) PROCESS Advanced control If single-loop feedback control (PID) alone is
More informationDesign and analysis of the prototype of boiler for steam pressure control
Design and analysis of the prototype of boiler for steam pressure control Akanksha Bhoursae, 2 Jalpa Shah, 3 Nishith Bhatt Institute of Technology, Nirma University, SG highway, Ahmedabad-38248,India 3
More informationIV Distillation Sequencing
IV Distillation Sequencing Outline 1. Basic Concepts of Distillation Sequence Design 2. Choice of Sequence and its Operating Pressure. 3. Performance of Distillation Column (Sieve tray and packed tower)
More informationControl System Design
ELEC ENG 4CL4: Control System Design Notes for Lecture #24 Wednesday, March 10, 2004 Dr. Ian C. Bruce Room: CRL-229 Phone ext.: 26984 Email: ibruce@mail.ece.mcmaster.ca Remedies We next turn to the question
More informationControl Lab. Thermal Plant. Chriss Grimholt
Control Lab Thermal Plant Chriss Grimholt Process System Engineering Department of Chemical Engineering Norwegian University of Science and Technology October 3, 23 C. Grimholt (NTNU) Thermal Plant October
More informationMULTILOOP PI CONTROLLER FOR ACHIEVING SIMULTANEOUS TIME AND FREQUENCY DOMAIN SPECIFICATIONS
Journal of Engineering Science and Technology Vol. 1, No. 8 (215) 113-1115 School of Engineering, Taylor s University MULTILOOP PI CONTROLLER FOR ACHIEVING SIMULTANEOUS TIME AND FREQUENCY DOMAIN SPECIFICATIONS
More informationImplementation issues for real-time optimization of a crude unit heat exchanger network
1 Implementation issues for real-time optimization of a crude unit heat exchanger network Tore Lid a, Sigurd Skogestad b a Statoil Mongstad, N-5954 Mongstad b Department of Chemical Engineering, NTNU,
More informationSystems Engineering/Process Control L1
Systems Engineering/Process Control L1 What is Systems Engineering/Process Control? Graphical system representations Fundamental control principles Reading: Systems Engineering and Process Control: 1.1
More informationCascade Control of a Continuous Stirred Tank Reactor (CSTR)
Journal of Applied and Industrial Sciences, 213, 1 (4): 16-23, ISSN: 2328-4595 (PRINT), ISSN: 2328-469 (ONLINE) Research Article Cascade Control of a Continuous Stirred Tank Reactor (CSTR) 16 A. O. Ahmed
More informationAppendix A MoReRT Controllers Design Demo Software
Appendix A MoReRT Controllers Design Demo Software The use of the proposed Model-Reference Robust Tuning (MoReRT) design methodology, described in Chap. 4, to tune a two-degree-of-freedom (2DoF) proportional
More informationMULTILOOP CONTROL APPLIED TO INTEGRATOR MIMO. PROCESSES. A Preliminary Study
MULTILOOP CONTROL APPLIED TO INTEGRATOR MIMO PROCESSES. A Preliminary Study Eduardo J. Adam 1,2*, Carlos J. Valsecchi 2 1 Instituto de Desarrollo Tecnológico para la Industria Química (INTEC) (Universidad
More informationReal-Time Feasibility of Nonlinear Predictive Control for Semi-batch Reactors
European Symposium on Computer Arded Aided Process Engineering 15 L. Puigjaner and A. Espuña (Editors) 2005 Elsevier Science B.V. All rights reserved. Real-Time Feasibility of Nonlinear Predictive Control
More information