Feedback Control of a Water Heater
|
|
- Elvin Wilkinson
- 5 years ago
- Views:
Transcription
1 Feedback Control of a Water Heater
2 Introduction v A simple feeback control system involving a stirred tank, temperature measurement, controller and manipulated heater is shown in Figure T 0, F Measurement T R, F Water Tank T R Q T R Controller T RSet Heater Figure. Feedback control of a simple continuous water heater.
3 Model Ø The energy balance for the tank is dt dt R F = ( T 0 -TR ) V Q + VrCp Ø Where Q is the heat input from the controlled heater. A proportional-integral feedback controller can be modelled as Q Kp = Q + Kp + ò 0 ε εdt t Ø Where the control error is given by ε = TRSet -T R I
4 Model v Nomenclature Symbols C p Specific heat Kcal/(Kg C) f Frequency of oscillations l/h F Flow rate m 3 /h K P Controller constant Kcal/C K D Controller constant for differential term (Kcal h)/c Q Heat input Kcal/h T Temperature C t I Controller cinstant h V Reactor volume m 3 є Error C ρ Density Kg /m 3 Indices M Refers to measured value R Refers to reactor Set Refers to setpoint 0 Refers to inlet
5 Program Ø Initial condition Variables Unit Value Inlet temperature C Set point C 80 Reactor Volume m Inlet flow rate m 3 /hr 10 Density Kg/m 3 1 Specific heat capacity Kcal/Kg C 1 Initial Heat duty Kcal/hr 6 Ø Initial condition parameters Variables Symbols Unit Value Controller constant K P Kcal/C 10 First order lag τ Q hr 1 τ S hr 2 Time constant τ I hr 5 Error ε C 0
6 Program v M-file
7 Program v M-file
8 Exercises 1 vdisconnect the controller (set K P =0), allow the temperature to reach steady state, and measure the temperature response to changes in water flow. Water flow rate (5 m 3 /hr)
9 Exercises 1 vdisconnect the controller (set K P =0), allow the temperature to reach steady state, and measure the temperature response to changes in water flow. Water flow rate (15 m 3 /hr)
10 Exercises 2 vrepeat exercise 1 for a change in inlet temperature Inlet temperature (15 C)
11 Exercises 2 vrepeat exercise 1 for a change in inlet temperature Inlet temperature (25 C)
12 Exercises 3 vmeasure the controlled response to a step change in F with proportional rtional control only (set t Ivery high). Notice the offset error є,, and change t I to a low value. Dose the offset disappear? Proportional control only
13 Exercises 3 vmeasure the controlled response to a step change in F with proportional rtional control only (set t Ivery high). Notice the offset error є,, and change t I to a low value. Dose the offset disappear? Step change in F with proportional control ( F: 20 à 25 m 3 /hr, after 80 hr)
14 Exercises 3 vmeasure the controlled response to a step change in F with proportional rtional control only (set t Ivery high). Notice the offset error є,, and change t I to a low value. Dose the offset disappear? Step change in F with proportional control ( F: 20 à 15 m 3 /hr, after 80 hr)
15 Exercises 3 vmeasure the controlled response to a step change in F with proportional rtional control only (set t Ivery high). Notice the offset error є,, and change t I to a low value. Dose the offset disappear? Proportional-integral control ( F: 20 m 3 /hr)
16 Exercises 3 vmeasure the controlled response to a step change in F with proportional rtional control only (set t Ivery high). Notice the offset error є,, and change t I to a low value. Dose the offset disappear? Step change in F with proportional-integral control ( F: 20 à 25 m 3 /hr, after 80 hr)
17 Exercises 3 vmeasure the controlled response to a step change in F with proportional rtional control only (set t Ivery high). Notice the offset error є,, and change t I to a low value. Dose the offset disappear? Step change in F with proportional-integral control ( F: 20 à 15 m 3 /hr, after 80 hr)
18 Exercises 4 vdisconnect the controller and by forcing the system with a stepwise change in water flow rate, tobtain I the process reaction curve as explained in Ch. t I 7. Analyze this curve to obtain the parameters for the Ziegler-Nichols Method. Use Table 7.1 to obtain the best controller setting for P and d PI control. Try these out in a simulation. Water flow change (process reaction curve) S = Slope A = =
19 Exercises 4 vdisconnect the controller and by forcing the system with a stepwise change in water flow rate, tobtain I the process reaction curve as explained in Ch. t I 7. Analyze this curve to obtain the parameters for the Ziegler-Nichols Method. Use Table 7.1 to obtain the best controller setting for P and d PI control. Try these out in a simulation. Table. Controller settings based on process responses (Zigler-Nichols) Cont roller Kp t I t D P 1/T L S PI 0.9/T L S 3.33T L PID 1.2/T L S 2T / 2 L T L
20 Exercises 4 P controller
21 Exercises 4 PI controller
22 Exercises 4 PID controller
23 Exercises 5 vproceed as in Example 4, but use the Cohen-Coon Coon settings from Table 7.1 A=0.5 B=0.03 T L =2.5 S= K = A B = 0.06 = S B t = 5.63 Table. Controller settings based on process responses (Cohen-Coon) Cont roller Kp t I t D P PI PID t KT t KT L t KT L L (1+ TL ) 3t TL (0.9 + ) 12t 4 ( 3 TL + ) 12t T T L L T T T 8T L L L L / t / t / t / t T L T L /t
24 Exercises 5 P controller
25 Exercises 5 PI controller
26 Exercises 5 PID controller
27 Exercises 6 vas explained in Ch. 7, try to establish the controller settings by trial and error. Trial and error method Optimal parameter Kp t I t D = 50 = 8 = 2
28 Exercises 7 vwith proportional control only, increase K to K P P0, until oscillations in the response occur. Use this frequency, f 0, to set the controller according to the Ultimate Gain Method (K =0.45 K P P0, t =f 0 /1.2), where f I 0 is the frequency of the oscillations at K =K P P0 (see Ch. 7). Measure the response to a change in T 0 =K P0 Table. Controller settings based on process responses (Ultimate Gain) Cont roller Kp t I t D P 0.5K P0 PI 0.45K P0 f 0 /1.2 PID 0.6K P0 f 0 / 2 / 8 f 0
29 Exercises 7 vwith proportional control only, increase K to K P P0, until oscillations in the response occur. Use this frequency, f 0, to set the controller according to the Ultimate Gain Method (K =0.45 K P P0, t =f 0 /1.2), where f I 0 is the frequency of the oscillations at K =K P P0 (see Ch. 7). Measure the response to a change in T 0 =K P0 increase K P to K P0
30 Exercises 7 vwith proportional control only, increase K to K P P0, until oscillations in the response occur. Use this frequency, f 0, to set the controller according to the Ultimate Gain Method (K =0.45 K P P0, t =f 0 /1.2), where f I 0 is the frequency of the oscillations at K =K P P0 (see Ch. 7). Measure the response to a change in T 0 =K P0 Ultimate Gain method Kp = 45 t I = 4.17 f 0 = 4
31 Exercises 7 vwith proportional control only, increase K to K P P0, until oscillations in the response occur. Use this frequency, f 0, to set the controller according to the Ultimate Gain Method (K =0.45 K P P0, t =f 0 /1.2), where f I 0 is the frequency of the oscillations at K =K P P0 (see Ch. 7). Measure the response to a change in T 0 =K P0 T 0 (20 à 15) with PI controller (Ultimate Gain method)
32 Exercises 7 vwith proportional control only, increase K to K P P0, until oscillations in the response occur. Use this frequency, f 0, to set the controller according to the Ultimate Gain Method (K =0.45 K P P0, t =f 0 /1.2), where f I 0 is the frequency of the oscillations at K =K P P0 (see Ch. 7). Measure the response to a change in T 0 =K P0 T 0 (20 à 25) with PI controller (Ultimate Gain method)
33 Exercises 8 vadjust the controller by trial and error to obtain the best results to a change in set point. Set point change (80 ºC à 90 ºC )
34 Exercises 8 vadjust the controller by trial and error to obtain the best results to a change in set point. Set point change (80 ºC à 70 ºC )
35 Exercises 9 vinclude a loss term in the energy balance. Trial and error method (Q loss = 0)
36 Exercises 9 vinclude a loss term in the energy balance. Trial and error method (Q loss = 10%)
37 Exercises 9 vinclude a loss term in the energy balance. Trial and error method (Q loss = 20%)
38 Exercises 10 v Change the controller to give PID-control by adding a differential term,
39 Exercises 11 v Include a first order time lag in the temperature measurement using u the equations : dt dt m = T R - t m T m ε = TRSet -T m
40 Conclusions Ø A simple feedback control system involving a stirred tank, temperature measurement, controller and manipulated heater is shown. Ø The Cohen-coon method were also compared with the predicted values by using the controllers of P, PI and PID. Among them, the P controller gave the best results. Ø The best results of Ziegler-Nichols method follows in the order of PID > PI > P controller. Ø Trial and error method of controllers can be adjusted by changing the values of gain K P, reset time and derivative time.
Class 27: Block Diagrams
Class 7: Block Diagrams Dynamic Behavior and Stability of Closed-Loop Control Systems We no ant to consider the dynamic behavior of processes that are operated using feedback control. The combination of
More informationOpen Loop Tuning Rules
Open Loop Tuning Rules Based on approximate process models Process Reaction Curve: The process reaction curve is an approximate model of the process, assuming the process behaves as a first order plus
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 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 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 informationLecture 5 Classical Control Overview III. Dr. Radhakant Padhi Asst. Professor Dept. of Aerospace Engineering Indian Institute of Science - Bangalore
Lecture 5 Classical Control Overview III Dr. Radhakant Padhi Asst. Professor Dept. of Aerospace Engineering Indian Institute of Science - Bangalore A Fundamental Problem in Control Systems Poles of open
More informationSRI VENKATESWARA COLLEGE OF ENGINEERING
COURSE DELIVERY PLAN - THEORY Page 1 of 7 Department of Chemical Engineering B.E/B.Tech/M.E/M.Tech : Chemical Engineering Regulation:2013 PG Specialisation : NA Sub. Code / Sub. Name : CH 6605 - Process
More information( ) ( = ) = ( ) ( ) ( )
( ) Vρ C st s T t 0 wc Ti s T s Q s (8) K T ( s) Q ( s) + Ti ( s) (0) τs+ τs+ V ρ K and τ wc w T (s)g (s)q (s) + G (s)t(s) i G and G are transfer functions and independent of the inputs, Q and T i. Note
More informationCONSIM - MS EXCEL BASED STUDENT FRIENDLY SIMULATOR FOR TEACHING PROCESS CONTROL THEORY
CONSIM - MS EXCEL BASED STUDENT FRIENDLY SIMULATOR FOR TEACHING PROCESS CONTROL THEORY S. Lakshminarayanan, 1 Rao Raghuraj K 1 and S. Balaji 1 1 Department of Chemical and Biomolecular Engineering, 4 Engineering
More informationTask 1 (24%): PID-control, the SIMC method
Final Exam Course SCE1106 Control theory with implementation (theory part) Wednesday December 18, 2014 kl. 9.00-12.00 SKIP THIS PAGE AND REPLACE WITH STANDARD EXAM FRONT PAGE IN WORD FILE December 16,
More informationK c < K u K c = K u K c > K u step 4 Calculate and implement PID parameters using the the Ziegler-Nichols tuning tables: 30
1.5 QUANTITIVE PID TUNING METHODS Tuning PID parameters is not a trivial task in general. Various tuning methods have been proposed for dierent model descriptions and performance criteria. 1.5.1 CONTINUOUS
More informationH-Infinity Controller Design for a Continuous Stirred Tank Reactor
International Journal of Electronic and Electrical Engineering. ISSN 974-2174 Volume 7, Number 8 (214), pp. 767-772 International Research Publication House http://www.irphouse.com H-Infinity Controller
More informationECE Introduction to Artificial Neural Network and Fuzzy Systems
ECE 39 - Introduction to Artificial Neural Network and Fuzzy Systems Wavelet Neural Network control of two Continuous Stirred Tank Reactors in Series using MATLAB Tariq Ahamed Abstract. With the rapid
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 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 informationExercise 8: Level Control and PID Tuning. CHEM-E7140 Process Automation
Exercise 8: Level Control and PID Tuning CHEM-E740 Process Automation . Level Control Tank, level h is controlled. Constant set point. Flow in q i is control variable q 0 q 0 depends linearly: R h . a)
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 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 informationCM 3310 Process Control, Spring Lecture 21
CM 331 Process Control, Spring 217 Instructor: Dr. om Co Lecture 21 (Back to Process Control opics ) General Control Configurations and Schemes. a) Basic Single-Input/Single-Output (SISO) Feedback Figure
More informationDynamic Behavior. Chapter 5
1 Dynamic Behavior In analyzing process dynamic and process control systems, it is important to know how the process responds to changes in the process inputs. A number of standard types of input changes
More informationDesign and Implementation of PI and PIFL Controllers for Continuous Stirred Tank Reactor System
International Journal of omputer Science and Electronics Engineering (IJSEE olume, Issue (4 ISSN 3 48 (Online Design and Implementation of PI and PIFL ontrollers for ontinuous Stirred Tank Reactor System
More informationModelling and Control of Dynamic Systems. PID Controllers. Sven Laur University of Tartu
Modelling and Control of Dynamic Systems PID Controllers Sven Laur University of Tartu Basic structure of a PID controller r[k] + e[k] P I D + u[k] System ĝ[z] y[k] -1 PID controllers are unity feedback
More informationDynamics and PID control. Process dynamics
Dynamics and PID control Sigurd Skogestad Process dynamics Things take time Step response (response of output y to step in input u): k = Δy( )/ Δu process gain - process time constant (63%) - process time
More informationSIMULATION SUITE CHEMCAD SOFTWARE PROCESS CONTROL SYSTEMS PROCESS CONTROL SYSTEMS COURSE WITH CHEMCAD MODELS. Application > Design > Adjustment
COURSE WITH CHEMCAD MODELS PROCESS CONTROL SYSTEMS Application > Design > Adjustment Based on F.G. Shinskey s 1967 Edition Presenter John Edwards P & I Design Ltd, UK Contact: jee@pidesign.co.uk COURSE
More informationWorld Journal of Engineering Research and Technology WJERT
wjert, 218, Vol. 4, Issue 1, 165-175. Original Article ISSN 2454-695X Pandimadevi et al. WJERT www.wjert.org SJIF Impact Factor: 4.326 DESIGN OF PI CONTROLLER FOR A CONICAL TANK SYSTEM *G. Pandimadevi
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 informationDevelopment of Dynamic Models. Chapter 2. Illustrative Example: A Blending Process
Development of Dynamic Models Illustrative Example: A Blending Process An unsteady-state mass balance for the blending system: rate of accumulation rate of rate of = of mass in the tank mass in mass out
More informationTuning PI controllers in non-linear uncertain closed-loop systems with interval analysis
Tuning PI controllers in non-linear uncertain closed-loop systems with interval analysis J. Alexandre dit Sandretto, A. Chapoutot and O. Mullier U2IS, ENSTA ParisTech SYNCOP April 11, 2015 Closed-loop
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 informationMIDTERM EXAMINATION Chemical Engineering 140 Fall 2002 Professors Jay D. Keasling and Jeffrey A. Reimer. Your SID #:
MIDTERM EXAMINATION Chemical Engineering 140 Fall 2002 Professors Jay D. Keasling and Jeffrey A. Reimer Your SID #: DO NOT PRINT YOUR NAME ANYWHERE ON THIS EXAM!! INSTRUCTIONS Read over the whole exam
More informationLaboratory Exercise 1 DC servo
Laboratory Exercise DC servo Per-Olof Källén ø 0,8 POWER SAT. OVL.RESET POS.RESET Moment Reference ø 0,5 ø 0,5 ø 0,5 ø 0,65 ø 0,65 Int ø 0,8 ø 0,8 Σ k Js + d ø 0,8 s ø 0 8 Off Off ø 0,8 Ext. Int. + x0,
More information(1) This reaction mechanism includes several undesired side reactions that produce toluene and benzene:
HYSYS Multiple Reactions - Styrene Prepared by Robert P. Hesketh Spring 005 Styrene Reactor System You have been studying how to use HYSYS using the example of a Styrene reactor system. In this session
More information7.2 Controller tuning from specified characteristic polynomial
192 Finn Haugen: PID Control 7.2 Controller tuning from specified characteristic polynomial 7.2.1 Introduction The subsequent sections explain controller tuning based on specifications of the characteristic
More informationUniversity of Science and Technology, Sudan Department of Chemical Engineering.
ISO 91:28 Certified Volume 3, Issue 6, November 214 Design and Decoupling of Control System for a Continuous Stirred Tank Reactor (CSTR) Georgeous, N.B *1 and Gasmalseed, G.A, Abdalla, B.K (1-2) University
More informationRoot Locus. Motivation Sketching Root Locus Examples. School of Mechanical Engineering Purdue University. ME375 Root Locus - 1
Root Locus Motivation Sketching Root Locus Examples ME375 Root Locus - 1 Servo Table Example DC Motor Position Control The block diagram for position control of the servo table is given by: D 0.09 Position
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 informationCompetences. The smart choice of Fluid Control Systems
Competences The smart choice of Fluid Control Systems Contents 1. Open-loop and closed-loop control Page 4 1.1. Function and sequence of an open-loop control system Page 4 1.2. Function and sequence of
More informationCHAPTER 3 TUNING METHODS OF CONTROLLER
57 CHAPTER 3 TUNING METHODS OF CONTROLLER 3.1 INTRODUCTION This chapter deals with a simple method of designing PI and PID controllers for first order plus time delay with integrator systems (FOPTDI).
More information6.1 Sketch the z-domain root locus and find the critical gain for the following systems K., the closed-loop characteristic equation is K + z 0.
6. Sketch the z-domain root locus and find the critical gain for the following systems K (i) Gz () z 4. (ii) Gz K () ( z+ 9. )( z 9. ) (iii) Gz () Kz ( z. )( z ) (iv) Gz () Kz ( + 9. ) ( z. )( z 8. ) (i)
More informationAppendix A: Exercise Problems on Classical Feedback Control Theory (Chaps. 1 and 2)
Appendix A: Exercise Problems on Classical Feedback Control Theory (Chaps. 1 and 2) For all calculations in this book, you can use the MathCad software or any other mathematical software that you are familiar
More informationControl of Neutralization Process in Continuous Stirred Tank Reactor (CSTR)
Control of Neutralization Process in Continuous Stirred Tank Reactor (CSTR) Dr. Magan P. Ghatule Department of Computer Science, Sinhgad College of Science, Ambegaon (Bk), Pune-41. gmagan@rediffmail.com
More informationCh 14: Feedback Control systems
Ch 4: Feedback Control systems Part IV A is concerned with sinle loop control The followin topics are covered in chapter 4: The concept of feedback control Block diaram development Classical feedback controllers
More informationA Method for PID Controller Tuning Using Nonlinear Control Techniques*
A Method for PID Controller Tuning Using Nonlinear Control Techniques* Prashant Mhaskar, Nael H. El-Farra and Panagiotis D. Christofides Department of Chemical Engineering University of California, Los
More informationIan G. Horn, Jeffery R. Arulandu, Christopher J. Gombas, Jeremy G. VanAntwerp, and Richard D. Braatz*
Ind. Eng. Chem. Res. 996, 35, 3437-344 3437 PROCESS DESIGN AND CONTROL Improved Filter Design in Internal Model Control Ian G. Horn, Jeffery R. Arulandu, Christopher J. Gombas, Jeremy G. VanAntwerp, and
More informationPROCESS CONTROL (IT62) SEMESTER: VI BRANCH: INSTRUMENTATION TECHNOLOGY
PROCESS CONTROL (IT62) SEMESTER: VI BRANCH: INSTRUMENTATION TECHNOLOGY by, Dr. Mallikarjun S. Holi Professor & Head Department of Biomedical Engineering Bapuji Institute of Engineering & Technology Davangere-577004
More informationFIND: (a) Sketch temperature distribution, T(x,t), (b) Sketch the heat flux at the outer surface, q L,t as a function of time.
PROBLEM 5.1 NOWN: Electrical heater attached to backside of plate while front surface is exposed to convection process (T,h); initially plate is at a uniform temperature of the ambient air and suddenly
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 informationAutomatic Control III (Reglerteknik III) fall Nonlinear systems, Part 3
Automatic Control III (Reglerteknik III) fall 20 4. Nonlinear systems, Part 3 (Chapter 4) Hans Norlander Systems and Control Department of Information Technology Uppsala University OSCILLATIONS AND DESCRIBING
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 informationDistributed Parameter Systems
Distributed Parameter Systems Introduction All the apparatus dynamic experiments in the laboratory exhibit the effect known as "minimum phase dynamics". Process control loops are often based on simulations
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 informationPrinciples of Instability and Stability in Digital PID Control Strategies and Analysis for a Continuous Alcoholic Fermentation Tank Process Start-up
Principles of Instability and Stability in Digital PID Control Strategies and Analysis for a Continuous Alcoholic Fermentation Tank Process Start-up Alexandre César Balbino Barbosa Filho a a Faculty of
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 informationCHAPTER 6 CLOSED LOOP STUDIES
180 CHAPTER 6 CLOSED LOOP STUDIES Improvement of closed-loop performance needs proper tuning of controller parameters that requires process model structure and the estimation of respective parameters which
More informationSolution to exam in PEF3006 Process Control at Telemark University College
Solution to exam in PEF3006 Process Control at Telemark University College Exam date: 7. December 2015. Duration: 4 hours. Weight in final grade of the course: 100%. Teacher: Finn Aakre Haugen (finn.haugen@hit.no).
More informationEE3CL4: Introduction to Linear Control Systems
1 / 17 EE3CL4: Introduction to Linear Control Systems Section 7: McMaster University Winter 2018 2 / 17 Outline 1 4 / 17 Cascade compensation Throughout this lecture we consider the case of H(s) = 1. We
More informationEEL2216 Control Theory CT1: PID Controller Design
EEL6 Control Theory CT: PID Controller Design. Objectives (i) To design proportional-integral-derivative (PID) controller for closed loop control. (ii) To evaluate the performance of different controllers
More informationProcess Control and Instrumentation Prof. A. K. Jana Department of Chemical Engineering Indian Institute of Technology, Kharagpur
Process Control and Instrumentation Prof. A. K. Jana Department of Chemical Engineering Indian Institute of Technology, Kharagpur Lecture - 8 Dynamic Behavior of Chemical Processes (Contd.) (Refer Slide
More informationChE 344 Winter 2011 Final Exam + Solution. Open Book, Notes, and Web
ChE 344 Winter 011 Final Exam + Solution Monday, April 5, 011 Open Book, Notes, and Web Name Honor Code (Please sign in the space provided below) I have neither given nor received unauthorized aid on this
More informationSimulation based Modeling and Implementation of Adaptive Control Technique for Non Linear Process Tank
Simulation based Modeling and Implementation of Adaptive Control Technique for Non Linear Process Tank P.Aravind PG Scholar, Department of Control and Instrumentation Engineering, JJ College of Engineering
More informationModelling of a simple continuous neutralisation reactor
Modelling of a simple continuous neutralisation reactor Serge Zihlmann ThirdWay February 23, 205 Foreword When I was browsing the web for a decent and intuitive model of a neutralisation reactor, I could
More informationEE 422G - Signals and Systems Laboratory
EE 4G - Signals and Systems Laboratory Lab 9 PID Control Kevin D. Donohue Department of Electrical and Computer Engineering University of Kentucky Lexington, KY 40506 April, 04 Objectives: Identify the
More informationStep input, ramp input, parabolic input and impulse input signals. 2. What is the initial slope of a step response of a first order system?
IC6501 CONTROL SYSTEM UNIT-II TIME RESPONSE PART-A 1. What are the standard test signals employed for time domain studies?(or) List the standard test signals used in analysis of control systems? (April
More informationExample 8: CSTR with Multiple Solutions
Example 8: CSTR with Multiple Solutions This model studies the multiple steady-states of exothermic reactions. The example is from Parulekar (27) which in turn was modified from one by Fogler (1999). The
More informationPrinciples and Practice of Automatic Process Control
Principles and Practice of Automatic Process Control Third Edition Carlos A. Smith, Ph.D., P.E. Department of Chemical Engineering University of South Florida Armando B. Corripio, Ph.D., P.E. Gordon A.
More informationApplication of feedforward artificial neural networks to improve process control of PID-based control algorithms
ELSEVIER Computers and Chemical Engineering 24 (2000) 853-858 Computers & Chemical Engineering www.elsevier.com/locate/compchemeng Application of feedforward artificial neural networks to improve process
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) Motion of Fluid Particles and Streams
Chapter (4) Motion of Fluid Particles and Streams Read all Theoretical subjects from (slides Dr.K.AlASTAL) Patterns of Flow Reynolds Number (R e ): A dimensionless number used to identify the type of flow.
More informationI will not be at Reed this Thursday (12/4) There will be a lecture next week (12/8) LV Assginment 4 is due on Friday
I will not be at Reed this Thursday (12/4) There will be a lecture next week (12/8) LV Assginment 4 is due on Friday Final Project: Wed. 12/10 [Friday12/19] I am there to help with the TE device/ PID VI
More informationProblem 1 (From the reservoir to the grid)
ÈÖÓ º ĺ ÙÞÞ ÐÐ ÈÖÓ º ʺ ³ Ò Ö ½ ½¹¼ ¹¼¼ ËÝ Ø Ñ ÅÓ Ð Ò ÀË ¾¼½ µ Ü Ö ËÓÐÙØ ÓÒ ÌÓÔ ÀÝ ÖÓ Ð ØÖ ÔÓÛ Ö ÔÐ ÒØ À Èȵ ¹ È ÖØ ÁÁ Ð ÖÒ Ø Þº ÇØÓ Ö ¾ ¾¼½ Problem 1 (From the reservoir to the grid) The causality diagram
More informationTemperature Control of CSTR Using Fuzzy Logic Control and IMC Control
Vo1ume 1, No. 04, December 2014 936 Temperature Control of CSTR Using Fuzzy Logic Control and Control Aravind R Varma and Dr.V.O. Rejini Abstract--- Fuzzy logic controllers are useful in chemical processes
More informationECE 388 Automatic Control
Lead Compensator and PID Control Associate Prof. Dr. of Mechatronics Engineeering Çankaya University Compulsory Course in Electronic and Communication Engineering Credits (2/2/3) Course Webpage: http://ece388.cankaya.edu.tr
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 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 informationDevelopment of Dynamic Models. Chapter 2. Illustrative Example: A Blending Process
Development of Dynamic Models Illustrative Example: A Blending Process An unsteady-state mass balance for the blending system: rate of accumulation rate of rate of = of mass in the tank mass in mass out
More informationImproved Autotuning Using the Shape Factor from Relay Feedback
Article Subscriber access provided by NATIONAL TAIWAN UNIV Improved Autotuning Using the Shape Factor from Relay Feedback T. Thyagarajan, and Cheng-Ching Yu Ind. Eng. Chem. Res., 2003, 42 (20), 4425-4440
More informationA population balance approach for continuous fluidized bed dryers
A population balance approach for continuous fluidized bed dryers M. Peglow, U. Cunäus, C. Kettner, T. Metzger, E. Tsotsas, Thermal Process Engineering, University Magdeburg, 396 Magdeburg, ermany Abstract
More informationTuning Method of PI Controller with Desired Damping Coefficient for a First-order Lag Plus Deadtime System
PID' Brescia (Italy), March 8-0, 0 FrA. Tuning Method of PI Controller with Desired Damping Coefficient for a First-order Lag Plus Deadtime System Yuji Yamakawa*. Yohei Okada** Takanori Yamazaki***. Shigeru
More informationFeedforward Control Feedforward Compensation
Feedforward Control Feedforward Compensation Compensation Feedforward Control Feedforward Control of a Heat Exchanger Implementation Issues Comments Nomenclature The inherent limitation of feedback control
More informationSolutions. .5 = e k k = ln(.5) Now that we know k we find t for which the exponential function is = e kt
MATH 1220-03 Exponential Growth and Decay Spring 08 Solutions 1. (#15 from 6.5.) Cesium 137 and strontium 90 were two radioactive chemicals released at the Chernobyl nuclear reactor in April 1986. The
More informationName. Chemical Engineering 150A Mid-term Examination 1 Spring 2013
Name Chemical Engineering 150A Mid-term Examination 1 Spring 2013 Show your work. Clearly identify any assumptions you are making, indicate your reasoning, and clearly identify any variables that are not
More informationDRINKING WATER - LAB EXPERIMENTS LAB EXPERIMENTS. Adsorption
DRINKING WATER - LAB EXPERIMENTS LAB EXPERIMENTS Adsorption adsorption lab experiments Framework This module explains the lab experiments on adsorption. Contents This module has the following contents:
More informationPart II. Advanced PID Design Methods
Part II Advanced PID Design Methods 54 Controller transfer function C(s) = k p (1 + 1 T i s + T d s) (71) Many extensions known to the basic design methods introduced in RT I. Four advanced approaches
More informationPre GATE Pre-GATE 2018
Pre GATE-018 Chemical Engineering CH 1 Pre-GATE 018 Duration : 180 minutes Total Marks : 100 CODE: GATE18-1B Classroom Postal Course Test Series (Add : 61C, Kalusarai Near HauzKhas Metro, Delhi 9990657855)
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 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 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 informationChE 400: Applied Chemical Engineering Calculations. Tutorial 5: Use of Matlab to Calculate Laplace Transforms
ChE 400: Applied Chemical Engineering Calculations Gerardine G. Botte This handout contains information about: How to calculate Laplace transform using Matlab How to calculate Inverse Laplace transform
More informationSoft Computing Technique and Conventional Controller for Conical Tank Level Control
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol. 4, No. 1, March 016, pp. 65~73 ISSN: 089-37, DOI: 10.11591/ijeei.v4i1.196 65 Soft Computing Technique and Conventional Controller
More informationProblem 1 (From the reservoir to the grid)
ÈÖÓ º ĺ ÙÞÞ ÐÐ ÈÖÓ º ʺ ³ Ò Ö ½ ½¹¼ ¼¹¼¼ ËÝ Ø Ñ ÅÓ Ð Ò ÀË ¾¼½ µ Ü Ö ÌÓÔ ÀÝ ÖÓ Ð ØÖ ÔÓÛ Ö ÔÐ ÒØ À Èȵ ¹ È ÖØ ÁÁ Ð ÖÒ Ø Þº ÇØÓ Ö ½ ¾¼½ Problem (From the reservoir to the grid) The causality diagram of the
More informationControl System Design
ELEC ENG 4CL4: Control System Design Notes for Lecture #13 Monday, February 3, 2003 Dr. Ian C. Bruce Room: CRL-229 Phone ext.: 26984 Email: ibruce@mail.ece.mcmaster.ca (3) Cohen-Coon Reaction Curve Method
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 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 informationControl for. Maarten Steinbuch Dept. Mechanical Engineering Control Systems Technology Group TU/e
Control for Maarten Steinbuch Dept. Mechanical Engineering Control Systems Technology Group TU/e Motion Systems m F Introduction Timedomain tuning Frequency domain & stability Filters Feedforward Servo-oriented
More informationFeedback Control Systems
ME Homework #0 Feedback Control Systems Last Updated November 06 Text problem 67 (Revised Chapter 6 Homework Problems- attached) 65 Chapter 6 Homework Problems 65 Transient Response of a Second Order Model
More informationBasic Concepts in Reactor Design
Basic Concepts in Reactor Design Lecture # 01 KBK (ChE) Ch. 8 1 / 32 Introduction Objectives Learning Objectives 1 Different types of reactors 2 Fundamental concepts used in reactor design 3 Design equations
More informationProblem 2 Distillation of air (20%) Solution
Problem 2 Distillation of air (20%) A feed with two components (79 mole% N 2 and 21% O 2 ) is to be separated by continuous distillation. (a) Compute equilibrium data (y,x) at 1 atm for N 2 -O 2 at x=0,
More informationInvestigation of adiabatic batch reactor
Investigation of adiabatic batch reactor Introduction The theory of chemical reactors is summarized in instructions to Investigation of chemical reactors. If a reactor operates adiabatically then no heat
More informationLecture 10: Proportional, Integral and Derivative Actions
MCE441: Intr. Linear Control Systems Lecture 10: Proportional, Integral and Derivative Actions Stability Concepts BIBO Stability and The Routh-Hurwitz Criterion Dorf, Sections 6.1, 6.2, 7.6 Cleveland State
More informationT i t l e o f t h e w o r k : L a M a r e a Y o k o h a m a. A r t i s t : M a r i a n o P e n s o t t i ( P l a y w r i g h t, D i r e c t o r )
v e r. E N G O u t l i n e T i t l e o f t h e w o r k : L a M a r e a Y o k o h a m a A r t i s t : M a r i a n o P e n s o t t i ( P l a y w r i g h t, D i r e c t o r ) C o n t e n t s : T h i s w o
More information