Process Solutions. Process Dynamics. The Fundamental Principle of Process Control. APC Techniques Dynamics 2-1. Page 2-1

Save this PDF as:

Size: px
Start display at page:

Download "Process Solutions. Process Dynamics. The Fundamental Principle of Process Control. APC Techniques Dynamics 2-1. Page 2-1"

Transcription

1 Process Dynamics The Fundamental Principle of Process Control APC Techniques Dynamics 2-1 Page 2-1

2 Process Dynamics (1) All Processes are dynamic i.e. they change with time. If a plant were totally static with respect to all it s variables, then control would be easy But in reality the dynamics are constantly changing We will define the basic terms and show how they relate to process response APC Techniques Dynamics 2-2 This is the most fundamental subject in the course. It is vital to understand process dynamics to understand process control This is the REAL difference between process engineers and control engineers. Process engineers think in terms of steady-state conditions (e.g. mass balances across units). Control engineers think in terms of dynamics as well as steady-state Page 2-2

3 Process Dynamics, subjects covered Process Response Process Gain Process Deadtime Process Lag Order Linearity Non-self regulating systems Plant tests APC Techniques Dynamics 2-3 We will discuss the above Response is the response of a system to a change Gain, deadtime and lag are used to define the dynamics Order is a measure of the complexity of the process, we will discuss this later. We will cover each subject in turn Page 2-3

4 Process Dynamics (2) The change with respect to time of the process variables such as temperature, pressure, flow, composition etc, due to controlled changes e.g. feed rate, temperature, pressure etc uncontrolled changes e.g. ambient temperature, feed composition etc Understanding process dynamics is fundamental for achieving good control APC Techniques Dynamics 2-4 All processes are constantly changing with time. We will define the basic terms We will show how these terms relate to the process We will see how to obtain the dynamics from plant data Page 2-4

5 PID open loop tutorial Coil Outlet temperature Feed flow rate Time Fuel gas valve position Time Fuel gas APC Techniques Dynamics 2-5 Gain is due to the cause and effect. A certain amount of fuel gas affects the temperature of the furnace by a certain amount. This resulting effect is the process gain of the system When you change the fuel gas, does the COT go up immediately? Why not? Because there is some distance to travel before the change BEGINS to affect the COT. This distance velocity lag is the deadtime. When the change begins to affect the COT does it shoot up immediately to its final resting value? Why not? Because there is some thermal inertia that the tubes in the furnace have to overcome before they heat up. This causes a LAG in the system. The lag is actually defined as the time to reach 63.2% of the final steadystate value. We will see why this is in a minute. Page 2-5

6 Dynamic response of a process can usually be characterized by 3 parameters : Process Gain APC Techniques Dynamics 2-6 Dynamics arise due to the length of the process path flow Process Solutions Characterizing Process Dynamics Response Kp or G and is the Change in the process variable divided by the change in the manipulated variable. Expressed in Engineering units Deadtime DT or θ, the time between MV changing and a noticeable change in PV. Expressed in minutes Lag T1 or τ Effects the rate at which the PV responds to an MV change. Expressed in minutes So the 3 parameters are expressed as above Note that all must be expressed in engineering units Note that as Deadtime increases, the control problem becomes harder to solve Lag is time to reach 63.2% of the final value we will see why in the next slide. Note that also as Lag increases, the control problem becomes harder Page 2-6

7 Laplace Transforms Laplace Transforms are simply a mathematical technique which can express equations in the time domain This allows straight-forward calculations to be carried out instead of solving complex differential equations Format is easily understood Best illustrated by some examples APC Techniques Dynamics 2-7 Page 2-7

8 Laplace Format Explained (1) e 20s + 1 5s Process Gain of 3.5 Process lag is 1st order and 20 min Process Deadtime of 5 min APC Techniques Dynamics 2-8 Page 2-8

9 Laplace Format Explained (2) Process Lead of 5 min s + 2s ( 10s + 1)( 10s + 1) e Process Gain of 0.5 Process lag is 2nd order and consists of 2 lags each 10 min long Process Deadtime of 2 min APC Techniques Dynamics 2-9 Page 2-9

10 Laplace Format Explained (3) Process Lead of 0.5 min e 2 100s + 20s + 1 s + 0s Process Gain of 20 Process lag is 2nd order and consists of 2 lags each 10 min long (alternative representation) No Process Deadtime APC Techniques Dynamics 2-10 We will explain Lead during the feedforward section of the course, but basically it gives a different characteristic response. Page 2-10

11 First order system At time, t, h = height of liquid in vessel h = H exp (-t/τ) When t = τ h = H.exp(-1) = H i.e. level has fallen by ( ) = H H H 1 Time APC Techniques Dynamics 2-11 bucket chemistry Now we will see why a lag is defined as 63.2% of the final value Here we have a cylinder emptying and following an exponential decay curve We can show that the level falls by or 63% of it s initial value at time t=t1 because exp(-1) is so the level has fallen by for a unit step change In practice the equation is more complex and non-linear, but this approximation is good enough in practice Page 2-11

12 Second order system of equal time constants, H τ 1 = τ 2 1 Top tank is 1st order as before H Notice Apparent deadtime Time 2 Time APC Techniques Dynamics 2-12 This system of buckets can be useful to explain other phenomena in process control Now consider 2 cylinders, one emptying into the other. The top tank behaves as before. The lower tank however is slightly buffered by the action of the upper tank and empties more slowly Do you see the apparent deadtime at the start (which is actually due to the combination of the 2 lags) Page 2-12

13 Second order system τ 1 > τ 2 SLOW 1 H Top tank is much slower Time H Looks like 1st order FAST 2 t 2 is the dominant time constant Time APC Techniques Dynamics 2-13 Now consider again the situation of two tanks. Now the top tank has a big hole and the bottom tank a small hole. The effect approximates to 1st order dynamics i.e. one lag Notice that we are now saying that for 2 lags, T1>T2, T1 has little effect Page 2-13

14 H Second order system τ 1 < τ 2 FAST 1 Imagine Top tank now empties very fast Time SLOW 2 H Notice inverse response, level actually rises initially. This confuses a simple feedback controller system Time APC Techniques Dynamics 2-14 The other scenario is the reverse, the bottom tank is now slow. It gets an initial boost from the top tank that temporarily actually increases the level beyond its initial starting point. This inverse response actually occurs in real systems in plants e.g. in pressure control systems and gives us problems in tuning. We will look at this later in more detail in a minute Page 2-14

15 H Imagine many tanks in series, one filling the next No. 6 Looks like DT+1st order Lag! due to series of lags Process Solutions 5 6 Time APC Techniques Dynamics 2-15 Page 2-15

16 First order model approximation of a 20th order system Process Solutions Deadtime + 1st order lag model of system 20th order system Time APC Techniques Dynamics 2-16 What does a real plant response look like? Real plants exhibit very high order dynamics (i.e. many lags in series) We can approximate a high order system such as the 20 order one above as a 1st order system with deadtime with a pretty good fit Page 2-16

17 Deriving Process Dynamics Process Solutions 100% % of final value High-order system Approximated to first order deadtime plus lag 63.2% 0% Time APC Techniques Dynamics 2-17 The dynamics may be derived in two ways The first is to approximate the curve to one of deadtime and 1st order lag and to draw a tangent at the point of steepest slope. The point of intersection on the X- axis marks the end of the deadtime and start of the lag period Page 2-17

18 100% Deriving Process Dynamics with the 20%/80% method Steady-state value Process Solutions 80% Deadtime = t (t 80 -t 20 ) Lag = (t 80 -t 20 ) 20% 0% T20 Time APC Techniques Dynamics 2-18 Another and more easy method is the 80/20 method developed by Honeywell Hi- Spec. It uses a simple empirical formula to calculate the dynamics directly. Simply measure 20% and 80% of the final value on the Y-axis and then determine the corresponding times. Then use the formula above T80 Page 2-18

19 Effect of varying dynamic parameters Increasing Gain Process Solutions Increasing Time Constant θ = 0 Order = 0 Increasing deadtime Increasing Order APC Techniques Dynamics 2-19 The above curves show the variation of shape of response with varying dynamics. Be sure you understand why each occurs? Page 2-19

20 Inverse Response (1) PC APC Techniques Dynamics 2-20 Inverse response is the situation where a variable first moves in the opposite direction to that desired for control and then moves back in the correct direction to a final steady-state value A Distillation Column with hot-bypass provides a good example We will see how inverse response can occur in practice 1) Pressure controller opens the valve 2) There is less pressure drop across the valve so more flow through the valve and the pressure in the column drops 3) Also less flow through the condenser so the duty drops 4) The temperature in the drum slowly starts to rise (more hot gas is bypassed) hence the pressure in the drum rises 5) There is less pressure drop across the valve and the condenser so the column pressure starts to rise 6) The pressure and therefore the temperature continues to rise but this increases the duty in the fin-fans which finally settles out at a new steady-state position Page 2-20

21 Valve Position Inverse Response (2) Pressure Time Inverse Response APC Techniques Dynamics 2-21 The trend above shows the inverse response Time Page 2-21

22 Self-regulating Process Process Solutions Non-Self Regulating Process FC LI Ti Fuel gas If increase opening of fuel gas control valve, then coil outlet temperature will rise and move to a new steady-state value If increase opening of outlet valve, then level will fall and continue to fall without reaching a new steady-state value APC Techniques Dynamics 2-22 A self-regulating system is one that will come to a new steady-state following a disturbance. Most control systems are self-regulating with one notable exception, level control A non-self regulating system (such as level control) is one that will not reach a new steady-state following a disturbance but will continue in one direction or the other either filling or emptying the vessel. This may make it harder to control Most control systems ARE self-regulating Page 2-22

23 Linearity A linear system has a constant process gain Most processes in refining, chemicals etc are at least slightly non-linear This has implications for controller tuning (need to tune for different conditions) Implications for plant tests or step tests Some processes are known to be highly non-linear e.g. ph control APC Techniques Dynamics 2-23 What are the implications for tuning? Use compromise tuning. Need to tune for different conditions since process results in different ways for different changes in MV What are the implications for plant tests? Do tests in both directions How can we solve highly non-linear control problems? Need to use some form of adaptive control Page 2-23

24 Process Variable, PV Linear Highly non-linear e.g. ph ph = -log10 [H + ] (Note a system is linear if dpv/dmv is constant) Manipulated Variable, MV APC Techniques Dynamics 2-24 This is a typical ph control scenario Process responds very non-linearly Control is very difficult using normal methods (PID) How to solve? Use adaptive control, or linearize the process by controlling log ph instead of straight ph? Page 2-24

25 Plant Tests, Guidelines (1) Choose the right time Not at a shift changeover Not during a feed switch Not at dawn or dusk Not on Monday morning! Not on Friday afternoon! Set up sampling/lab analysis if required Talk about test with operations and get approval for test Ensure instrumentation is OK Ensure process is steady APC Techniques Dynamics 2-25 Why right time? to not cause hassle for operators Why not at dawn or dusk? temperature changes due to ambient change Why set up sampling? to get all the data we need Why approval? not to get operators annoyed/follow correct procedures, be safe, make large moves rather than small moves, too small moves might not be visible! Why instrumentation OK? to ensure tests are not a waste of time! Page 2-25

26 Plant Tests, Guidelines (2) Open required loop, make step change of say 2-5% valve movement Record trend of OP and PV Reach new steady state some people use θ+5τ Carry out test again in opposite direction, double amount if possible Ensure Manipulated variable really changes Document results Do tests under all modes of operations Step away from any constraints on the unit for safety APC Techniques Dynamics 2-26 Why other direction? to check linearity Why twice amount? bigger step gives better estimate of steady-state gain Why all modes? different dynamics! Why away from constraints? Not to give operators/the plant a hard time! Page 2-26

27 Plant Tests Process Solutions MV CV Time Time APC Techniques Dynamics 2-27 Page 2-27

28 Conclusion Understanding dynamics is the key to controlling any process Simple dynamics can be represented by gain, deadtime and lag These may be determined by carrying out plant step tests APC Techniques Dynamics 2-28 Page 2-28

29 Process Dynamics Exercises APC Techniques Dynamics 2-29 Page 2-29

30 Exercise in Process Dynamics Process Solutions Using both of the methods in the lecture (63.2% and 80/20 method) calculate the process dynamics for the responses in fig 1. The response is that of a heater coil outlet temperature to a step change in fuel gas flow of 50 m3/h Fig 2a and 2b show the results of tests carried out on the same heater. The fuel gas flow change was 40m3/h. Calculate dynamics using the 80/20 method, comment on results Comment on fig 3a,b,c,d process responses Carry out PID tutorial exercise on dynamics APC Techniques Dynamics 2-30 Page 2-30

31 COT 500 Process Solutions Figure 1 Time (Mins) APC Techniques Dynamics 2-31 Page 2-31

32 COT 500 Process Solutions Figure 2a Time (Mins) APC Techniques Dynamics 2-32 Page 2-32

33 COT 500 Process Solutions Figure 2b Time (Mins) APC Techniques Dynamics 2-33 Page 2-33

34 COT 500 Process Solutions Figure 3a Time (Mins) APC Techniques Dynamics 2-34 Page 2-34

35 COT 500 Process Solutions Figure 3b Time (Mins) APC Techniques Dynamics 2-35 Page 2-35

36 COT 500 Process Solutions Figure 3c Time (Mins) APC Techniques Dynamics 2-36 Page 2-36

37 COT 500 Process Solutions Figure 3d Time (Mins) APC Techniques Dynamics 2-37 Page 2-37

38 Tutorial Software Interactive Exercises Tutorial Menu PID Algorithm Lesson Menu Introduction Open Loop Closed Loop On-Off Control Definitions APC Techniques Dynamics 2-38 Use the arrow keys to navigate to the next / previous screen. Use the <backspace> key to change tuning constants, etc. Do not use the numeric keypad. Page 2-38

Fundamental 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

Control 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

Process Control, 3P4 Assignment 5

Process Control, 3P4 Assignment 5 Kevin Dunn, kevin.dunn@mcmaster.ca Due date: 12 March 2014 This assignment is due on Wednesday, 12 March 2014. Late hand-ins are not allowed. Since it is posted mainly

Enhanced 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

Dr Ian R. Manchester Dr Ian R. Manchester AMME 3500 : Review

Week Date Content Notes 1 6 Mar Introduction 2 13 Mar Frequency Domain Modelling 3 20 Mar Transient Performance and the s-plane 4 27 Mar Block Diagrams Assign 1 Due 5 3 Apr Feedback System Characteristics

Process 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 - 17 Feedback Control Schemes (Contd.) In the last class we discussed

Index. 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

Model based control design

Model based control design Alf Isaksson September, 999 Supplied as supplement to course book in Automatic Control Basic course (Reglerteknik AK) Objective: To introduce some general approaches to model

Systems 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

Calculus concepts and applications

Calculus concepts and applications This worksheet and all related files are licensed under the Creative Commons Attribution License, version 1.0. To view a copy of this license, visit http://creativecommons.org/licenses/by/1.0/,

Spring 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

Analysis 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.

Solutions 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

A Holistic Approach to the Application of Model Predictive Control to Batch Reactors

A Holistic Approach to the Application of Model Predictive Control to Batch Reactors A Singh*, P.G.R de Villiers**, P Rambalee***, G Gous J de Klerk, G Humphries * Lead Process Control Engineer, Anglo

Transient response of RC and RL circuits ENGR 40M lecture notes July 26, 2017 Chuan-Zheng Lee, Stanford University

Transient response of C and L circuits ENG 40M lecture notes July 26, 2017 Chuan-Zheng Lee, Stanford University esistor capacitor (C) and resistor inductor (L) circuits are the two types of first-order

CONTROL MODE SETTINGS. The quality of control obtained from a particular system depends largely on the adj ustments made to the various mode

Instrumentation & Control - Course 136 CONTROL MODE SETTINGS The quality of control obtained from a particular system depends largely on the adj ustments made to the various mode settings. Many control

Chapter 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.

K 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

Open 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

Experiment 1. Measurement of Thermal Conductivity of a Metal (Brass) Bar

Experiment 1 Measurement of Thermal Conductivity of a Metal (Brass) Bar Introduction: Thermal conductivity is a measure of the ability of a substance to conduct heat, determined by the rate of heat flow

Introduction to Feedback Control

Introduction to Feedback Control Control System Design Why Control? Open-Loop vs Closed-Loop (Feedback) Why Use Feedback Control? Closed-Loop Control System Structure Elements of a Feedback Control System

Uniform Circular Motion

Uniform Circular Motion Introduction Earlier we defined acceleration as being the change in velocity with time: = Until now we have only talked about changes in the magnitude of the acceleration: the speeding

Control System Design

ELEC ENG 4CL4: Control System Design Notes for Lecture #1 Monday, January 6, 2003 Instructor: Dr. Ian C. Bruce Room CRL-229, Ext. 26984 ibruce@mail.ece.mcmaster.ca Office Hours: TBA Teaching Assistants:

Control 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

4.1. Physics Module Form 4 Chapter 4 - Heat GCKL UNDERSTANDING THERMAL EQUILIBRIUM. What is thermal equilibrium?

Physics Module Form 4 Chapter 4 - Heat GCKL 2010 4.1 4 UNDERSTANDING THERMAL EQUILIBRIUM What is thermal equilibrium? 1. (, Temperature ) is a form of energy that flows from a hot body to a cold body.

Alireza Mousavi Brunel University

Alireza Mousavi Brunel University 1 » Control Process» Control Systems Design & Analysis 2 Open-Loop Control: Is normally a simple switch on and switch off process, for example a light in a room is switched

Solutions 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

Controlled variable- T (house interior temperature) Manipulated variable- Q (heat from the furnace) 1-1

1.1 a) True b) True c) True d) False e) True 1.2 Q L Q T TC ON/OFF SWITCH Controlled variable- T (house interior temperature) Manipulated variable- Q (heat from the furnace) Solution Manual for Process

Feedback 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

Capacitance. Resources and methods for learning about these subjects (list a few here, in preparation for your research):

Capacitance This worksheet and all related files are licensed under the Creative Commons Attribution License, version 1.0. To view a copy of this license, visit http://creativecommons.org/licenses/by/1.0/,

FEEDBACK CONTROL SYSTEMS

FEEDBAC CONTROL SYSTEMS. Control System Design. Open and Closed-Loop Control Systems 3. Why Closed-Loop Control? 4. Case Study --- Speed Control of a DC Motor 5. Steady-State Errors in Unity Feedback Control

Lecture 12. AO Control Theory

Lecture 12 AO Control Theory Claire Max with many thanks to Don Gavel and Don Wiberg UC Santa Cruz February 18, 2016 Page 1 What are control systems? Control is the process of making a system variable

6.302 Feedback Systems Recitation 16: Compensation Prof. Joel L. Dawson

Bode Obstacle Course is one technique for doing compensation, or designing a feedback system to make the closed-loop behavior what we want it to be. To review: - G c (s) G(s) H(s) you are here! plant For

1 Page. Uniform Circular Motion Introduction. Earlier we defined acceleration as being the change in velocity with time:

Uniform Circular Motion Introduction Earlier we defined acceleration as being the change in velocity with time: a=δv/t Until now we have only talked about changes in the magnitude of the acceleration:

ENZYME SCIENCE AND ENGINEERING PROF. SUBHASH CHAND DEPARTMENT OF BIOCHEMICAL ENGINEERING AND BIOTECHNOLOGY IIT DELHI LECTURE 7

ENZYME SCIENCE AND ENGINEERING PROF. SUBHASH CHAND DEPARTMENT OF BIOCHEMICAL ENGINEERING AND BIOTECHNOLOGY IIT DELHI LECTURE 7 KINETICS OF ENZYME CATALYSED REACTIONS (CONTD.) So in the last lecture we

DEMONSTRATION 4.1 MOLECULES HITTING EACH OTHER

DEMONSTRATION 4.1 MOLECULES HITTING EACH OTHER Directions for doing the demonstration are in the Science Book Teacher's Guide. 1. Students should include these ideas: The air moving out of the hair dryer

CHEE 319 Process Dynamics and Control

CHEE 319 Process Dynamics and Control Winter 2012 Instructor: M.Guay TAs: S. Dougherty, D. Park and E. Moshksar 1 Organization Instructor: Dr. Martin Guay Office: Dupuis 406 Phone: 533-2788 Email: guaym@chee.queensu.ca

Competences. 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

= A x (t) + B utt), by d{ _-=-=-

M.Tech. [ 24 103 ] Degree Examination ndustrial Process nstrumentation First Semester COMPUTER CONTROL OF PROCESSES (Effective from the Admitted Batch of2003-2004) Time: 3 Hours Maximum marks: 100 Answer

MAT01A1: Functions and Mathematical Models

MAT01A1: Functions and Mathematical Models Dr Craig 21 February 2017 Introduction Who: Dr Craig What: Lecturer & course coordinator for MAT01A1 Where: C-Ring 508 acraig@uj.ac.za Web: http://andrewcraigmaths.wordpress.com

Chemical Engineering & Process Techniques

emical Engineering & Process Tecniques eview Article eedback ontrol for Liquid Level in a Gravity-Drained Multi-Tank System Larry K. Jang* Department of emical Engineering, alifornia State University,

Quanser NI-ELVIS Trainer (QNET) Series: QNET Experiment #02: DC Motor Position Control. DC Motor Control Trainer (DCMCT) Student Manual

Quanser NI-ELVIS Trainer (QNET) Series: QNET Experiment #02: DC Motor Position Control DC Motor Control Trainer (DCMCT) Student Manual Table of Contents 1 Laboratory Objectives1 2 References1 3 DCMCT Plant

Turbines and speed governors

ELEC0047 - Power system dynamics, control and stability Turbines and speed governors Thierry Van Cutsem t.vancutsem@ulg.ac.be www.montefiore.ulg.ac.be/~vct November 2017 1 / 31 2 / 31 Steam turbines Turbines

Chapter 5. Mass and Energy Analysis of Control Volumes. by Asst. Prof. Dr.Woranee Paengjuntuek and Asst. Prof. Dr.Worarattana Pattaraprakorn

Chapter 5 Mass and Energy Analysis of Control Volumes by Asst. Prof. Dr.Woranee Paengjuntuek and Asst. Prof. Dr.Worarattana Pattaraprakorn Reference: Cengel, Yunus A. and Michael A. Boles, Thermodynamics:

reality is complex process

ISS0080 Automation and Process Control Lecture 5 1 Process models the desire to describe reality Model of the process, model simplication, identication. model reality is complex process Replaces the original;

PROCESS CONTROL DESIGN PASI 2005

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

Unit 1 Maths Methods (CAS) Exam 2014 Thursday June pm

Name: Teacher: Unit 1 Maths Methods (CAS) Exam 2014 Thursday June 5-1.50 pm Reading time: 10 Minutes Writing time: 80 Minutes Instruction to candidates: Students are permitted to bring into the examination

Survey of Methods of Combining Velocity Profiles with Position control

Survey of Methods of Combining Profiles with control Petter Karlsson Mälardalen University P.O. Box 883 713 Västerås, Sweden pkn91@student.mdh.se ABSTRACT In many applications where some kind of motion

Robotics. Dynamics. Marc Toussaint U Stuttgart

Robotics Dynamics 1D point mass, damping & oscillation, PID, dynamics of mechanical systems, Euler-Lagrange equation, Newton-Euler recursion, general robot dynamics, joint space control, reference trajectory

CONTROL 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

Advanced 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

Lecture «Robot Dynamics»: Dynamics 2

Lecture «Robot Dynamics»: Dynamics 2 151-0851-00 V lecture: CAB G11 Tuesday 10:15 12:00, every week exercise: HG E1.2 Wednesday 8:15 10:00, according to schedule (about every 2nd week) office hour: LEE

Solids, liquids and gases

Solids, liquids and gases Duration 60 minutes Lesson overview Students share what they know about the three states of matter solid, liquid and gas and consider some of their scientific properties. They

INTRODUCTION TO ISA SYMBOLOGY

ERT 213/3 Process instrumentations INTRODUCTION TO ISA SYMBOLOGY DR. RAHIMAH BINTI OTHMAN (Email: rahimah@unimap.edu.my) COURSE OUTCOMES COs 3. Able to design process instruments in bioprocess engineering

Lecture 50 Changing Closed Loop Dynamic Response with Feedback and Compensation

Lecture 50 Changing Closed Loop Dynamic Response with Feedback and Compensation 1 A. Closed Loop Transient Response Waveforms 1. Standard Quadratic T(s) Step Response a. Q > 1/2 Oscillatory decay to a

Dynamics 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

What's Up, Earth? Header Insert Image 1 here, right justified to wrap. Grade Level. 3rd. Time Required: 60 minutes

What's Up, Earth? Header Insert Image 1 here, right justified to wrap Image 1 ADA Description:? Caption:? Image file path:? Source/Rights: Copyright? Grade Level 3rd Time Required: 60 minutes Group Size:

Application Note #3413

Application Note #3413 Manual Tuning Methods Tuning the controller seems to be a difficult task to some users; however, after getting familiar with the theories and tricks behind it, one might find the

What Is Air Temperature?

2.2 Read What Is Air Temperature? In Learning Set 1, you used a thermometer to measure air temperature. But what exactly was the thermometer measuring? What is different about cold air and warm air that

Lecture 28 Reactor Kinetics-IV

Objectives In this lecture you will learn the following In this lecture we will understand the transient build up of Xenon. This can lead to dead time in reactors. Xenon also induces power oscillations

The First Law of Thermodynamics. By: Yidnekachew Messele

The First Law of Thermodynamics By: Yidnekachew Messele It is the law that relates the various forms of energies for system of different types. It is simply the expression of the conservation of energy

Chapter 3, Lesson 1: What is Density?

Chapter 3, Lesson 1: What is Density? Key Concepts Density is a characteristic property of a substance. The density of a substance is the relationship between the mass of the substance and how much space

Atoms and molecules are in motion and have energy

Atoms and molecules are in motion and have energy By now you know that substances are made of atoms and molecules. These atoms and molecules are always in motion and have attractions to each other. When

fr>uafcji *> \E % jw r"'''f^,""'i;- ~^H^^

NAME DATE Carolina Transpiration Kit for AP Biology Imagine that your family has received a bouquet of cut flowers as a gift. You place the flowers in a vase with a small volume of water, and return the

Electrical Engineering Fundamentals for Non-Electrical Engineers

Electrical Engineering Fundamentals for Non-Electrical Engineers by Brad Meyer, PE Contents Introduction... 3 Definitions... 3 Power Sources... 4 Series vs. Parallel... 9 Current Behavior at a Node...

Physics 2112 Unit 11

Physics 2112 Unit 11 Today s oncept: ircuits Unit 11, Slide 1 Stuff you asked about.. what happens when one resistor is in parallel and one is in series with the capacitor Differential equations are tough

Appendix 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

0 t < 0 1 t 1. u(t) =

A. M. Niknejad University of California, Berkeley EE 100 / 42 Lecture 13 p. 22/33 Step Response A unit step function is described by u(t) = ( 0 t < 0 1 t 1 While the waveform has an artificial jump (difficult

y=5 y=1+x 2 AP Calculus Chapter 5 Testbank Part I. Multiple-Choice Questions

AP Calculus Chapter 5 Testbank Part I. Multiple-Choice Questions. Which of the following integrals correctly corresponds to the area of the shaded region in the figure to the right? (A) (B) (C) (D) (E)

Thermochemistry/Calorimetry. Determination of the enthalpy of vaporization of liquids LEC 02. What you need: What you can learn about

LEC 02 Thermochemistry/Calorimetry Determination of the enthalpy of vaporization of liquids What you can learn about Enthalpy of vaporisation Entropy of vaporisation Trouton s rule Calorimetry Heat capacity

School of Mechanical Engineering Purdue University. ME375 Feedback Control - 1

Introduction to Feedback Control Control System Design Why Control? Open-Loop vs Closed-Loop (Feedback) Why Use Feedback Control? Closed-Loop Control System Structure Elements of a Feedback Control System

Simulation: Density FOR THE TEACHER

Simulation: Density FOR THE TEACHER Summary In this simulation, students will investigate the effect of changing variables on both the volume and the density of a solid, a liquid and a gas sample. Students

Circular Motion and Gravitation Notes 1 Centripetal Acceleration and Force

Circular Motion and Gravitation Notes 1 Centripetal Acceleration and Force This unit we will investigate the special case of kinematics and dynamics of objects in uniform circular motion. First let s consider

2. A proton is traveling with velocity v, to the right, through a magnetic field pointing into the page as indicated in the figure below.

1. An electron has a mass of 9.11 x 10-31 kg and its charge is -1.6 x 10-19 C. The electron is released from rest in a vacuum between two flat, parallel metal plates that are 10 cm apart. The potential

Electricity & Optics

Physics 241 Electricity & Optics Lecture 12 Chapter 25 sec. 6, 26 sec. 1 Fall 217 Semester Professor Koltick Circuits With Capacitors C Q = C V V = Q C + V R C, Q Kirchhoff s Loop Rule: V I R V = V I R

The Fundamental Theorem of Calculus

The Fundamental Theorem of Calculus Objectives Evaluate a definite integral using the Fundamental Theorem of Calculus. Understand and use the Mean Value Theorem for Integrals. Find the average value of

Lecture 47 Switch Mode Converter Transfer Functions: Tvd(s) and Tvg(s) A. Guesstimating Roots of Complex Polynomials( this section is optional)

Lecture 47 Switch Mode Converter Transfer Functions: T vd (s) and T vg (s) A. Guesstimating Roots of Complex Polynomials( this section is optional). Quick Insight n=. n th order case. Cuk example 4. Forth

Equilibrium, Positive and Negative Feedback

Equilibrium, Positive and Negative Feedback Most ecosystems are very complex. There are many flows and storages. A high level of complexity makes for a more stable system which can withstand stress and

Cover Page: Entropy Summary

Cover Page: Entropy Summary Heat goes where the ratio of heat to absolute temperature can increase. That ratio (Q/T) is used to define a quantity called entropy. The useful application of this idea shows

Experiment 3: Radiative Lifetime Determination by Laser-Induced Fluorescence

Physical Measurements - Core I GENERAL REFERENCES Experiment 3: Radiative Lifetime Determination by Laser-Induced Fluorescence 1. Barrow, G. M. Physical Chemistry McGraw-Hill Book Company: New York, 1988;

The First Derivative and Second Derivative Test

The First Derivative and Second Derivative Test James K. Peterson Department of Biological Sciences and Department of Mathematical Sciences Clemson University November 8, 2017 Outline Extremal Values The

Direct and Indirect Gradient Control for Static Optimisation

International Journal of Automation and Computing 1 (2005) 60-66 Direct and Indirect Gradient Control for Static Optimisation Yi Cao School of Engineering, Cranfield University, UK Abstract: Static self-optimising

Control Systems I. Lecture 2: Modeling. Suggested Readings: Åström & Murray Ch. 2-3, Guzzella Ch Emilio Frazzoli

Control Systems I Lecture 2: Modeling Suggested Readings: Åström & Murray Ch. 2-3, Guzzella Ch. 2-3 Emilio Frazzoli Institute for Dynamic Systems and Control D-MAVT ETH Zürich September 29, 2017 E. Frazzoli

Lesson 3 The Behavior of Gases

Lesson 3 The Behavior of Gases Student Labs and Activities Page Launch Lab 46 Content Vocabulary 47 Lesson Outline 48 MiniLab 50 Content Practice A 51 Content Practice B 52 Math Skills 53 School to Home

QNET Experiment #05: HVAC System Identification. Heating, Ventilation, and Air Conditioning Trainer (HVACT) Student Manual

Quanser NI-ELVIS Trainer (QNET) Series: QNET Experiment #05: HVAC System Identification Heating, Ventilation, and Air Conditioning Trainer (HVACT) Student Manual Table of Contents 1. Laboratory Objectives...1

Lecture 7: Anti-windup and friction compensation

Lecture 7: Anti-windup and friction compensation Compensation for saturations (anti-windup) Friction models Friction compensation Material Lecture slides Course Outline Lecture 1-3 Lecture 2-6 Lecture

Multiple Model Based Adaptive Control for Shell and Tube Heat Exchanger Process

Multiple Model Based Adaptive Control for Shell and Tube Heat Exchanger Process R. Manikandan Assistant Professor, Department of Electronics and Instrumentation Engineering, Annamalai University, Annamalai

Reinforcement Learning, Neural Networks and PI Control Applied to a Heating Coil

Reinforcement Learning, Neural Networks and PI Control Applied to a Heating Coil Charles W. Anderson 1, Douglas C. Hittle 2, Alon D. Katz 2, and R. Matt Kretchmar 1 1 Department of Computer Science Colorado

Learn2Control Laboratory

Learn2Control Laboratory Version 3.2 Summer Term 2014 1 This Script is for use in the scope of the Process Control lab. It is in no way claimed to be in any scientific way complete or unique. Errors should

Control Systems I. Lecture 1: Introduction. Suggested Readings: Åström & Murray Ch. 1, Guzzella Ch. 1. Emilio Frazzoli

Control Systems I Lecture 1: Introduction Suggested Readings: Åström & Murray Ch. 1, Guzzella Ch. 1 Emilio Frazzoli Institute for Dynamic Systems and Control D-MAVT ETH Zürich September 22, 2017 E. Frazzoli

Corrections to Maxwell s equations for free space Invalidating the theory of relativity?!

Corrections to Maxwell s equations for free space Invalidating the theory of relativity?! December 01, 2012 Henok Tadesse, Electrical Engineer, B.Sc Ethiopia e-mail: entkidmt@yahoo.com or wchmar@gmail.com

Simple criteria for controller performance monitoring

Simple criteria for controller performance monitoring Kostas Tsakalis (ASU) and Sachi Dash (Honeywell) Introduction/motivation Performance monitoring philosophy Simple criteria based on small-gain arguments

Dynamics, Simulation, and Control of a Batch Distillation Column using Labview

International Journal of Current Engineering and Technology E-ISSN 2277 416, P-ISSN 2347 5161 216 INPRESSCO, All Rights Reserved Available at http://inpressco.com/category/ijcet Research Article Dynamics,

A New Generation of Adaptive Model Based Predictive Controllers Applied in Batch Reactor Temperature Control

A New Generation of Adaptive Model Based Predictive Controllers Applied in Batch Reactor emperature Control Mihai Huzmezan University of British Columbia Pulp and Paper Centre 385 East Mall Vancouver,

Engineering Models For Inferential Controls

Petrocontrol Leader in inferential control technology Engineering Models For Inferential Controls By Y Zak Friedman, PhD Principal consultant INTRODUCTION This paper describes a class of inferential control

Figure 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

Solar Flat Plate Thermal Collector

Solar Flat Plate Thermal Collector INTRODUCTION: Solar heater is one of the simplest and basic technologies in the solar energy field. Collector is the heart of any solar heating system. It absorbs and

Astronomy 102 Lab: Hubble Law

Name: Astronomy 102 Lab: Hubble Law Part of today s lab will involve the use of laptops. If you own one, please bring it to class. Pre-Lab Assignment: In this week's lab, you will study the expansion of