REAL-TIME CONTROL OF INDUSTRIAL UREA EVAPORATION PROCESS …

REAL-TIME CONTROL OF INDUSTRIAL UREA EVAPORATION

PROCESS USING MODEL PREDICTIVE CONTROL

By

Ismail Mohamed Mahmoud Fahmy

A Thesis Submitted to the

Faculty of Engineering at Cairo University

in Partial Fulfillment of the

Requirements for the Degree of

MASTER OF SCIENCE

in

ELECTRICAL POWER AND MACHINES ENGINEERING

FACULTY OF ENGINEERING, CAIRO UNIVERSITY

GIZA, EGYPT

2016



By






MASTER OF SCIENCE

in


Under the Supervision of

Prof. Dr. Khaled Ali El-Metwally

Assoc. Prof. Dr. Ahmed M. Kamel

Electrical Power And Machines Eng.

Faculty of Engineering, Cairo University

Electrical Power And Machines Eng.


Assoc. Prof. Dr. Ahmed Fayez Nassar

Chemical Engineering



GIZA, EGYPT

2016



By






MASTER OF SCIENCE

in


Approved by the

Examining Committee

____________________________

Prof. Dr. Khaled Ali El-Metwally, Thesis Main Advisor

__________________________

Assoc. Prof. Dr. Ahmed Mohamed Kamel, Member

__________________________

Prof. Dr. Abdel-Latif Mohamed El-Shafie, Internal Examiner

____________________________

Dr. Mahmoud Gamal El-Din Badran, External Examiner

Chairman of Egyptian Company for Gas Services (ECGS)


GIZA, EGYPT

2016

Engineer’s Name: Ismail Mohamed Mahmoud Fahmy Date of Birth: 14 / 07 / 1983

Nationality: Egyptian

E-mail: [email protected]

Phone: 01022044026

Address: 21 St. Youssef Abbas, Naser City

Registration Date: 01 / 10 / 2011

Awarding Date: / / 2016

Degree: Master of Science

Department: Electrical Power and Machines Engineering

Supervisors:

Prof. Dr. Khaled Ali El-Metwally

Assoc. Prof. Dr. Ahmed Mohamed Kamel

Assoc. Prof. Dr. Ahmed Fayez Nassar

Examiners:

Prof. Dr. Khaled Ali El-Metwally (Thesis main advisor)

Assoc. Prof. Dr. Ahmed Mohamed Kamel (Member)

Prof. Dr. Abdel-Latif ElShafie (Internal examiner)

Dr. Mahmoud Gamal El-Din Badran (Ext. examiner)

Chairman of Egyptian Company for Gas Services

Title of Thesis:

Real-Time Control of Industrial Urea Evaporation Process Using Model

Predictive Control

Key Words:

Dynamic Modelling; Real-Time Simulation; Multivariable System Identification;

Model Predictive Control

Summary:

A wide range of processes in chemical industry are characterized by the existence

of nonlinear, multivariable interacting, time delay, and constraints properties. The

evaporation process in urea industry is considered one of these processes and

represents a challenge for the traditional control strategy. Model predictive control

(MPC) technique provides the best solution to improve the control performance for

optimum process operation.

This Thesis presents the dynamic modeling, identification and real-time simulation

of urea evaporation process control in a fertilizer plant using MPC technology. The

results showed a significant improvement of the control performance using MPC

compared to the traditional control strategy especially during the plant load variation.

i

ACKNOWLEDGEMENTS

All praise and glory goes to Almighty Allah who gave me the courage and

patience to carry out this work.

My deep appreciation and profound gratitude goes to my thesis supervisors

Prof. Dr. Khaled El-Metwally, Prof. Dr. Ahmed Mohamed Kamel and Dr. Ahmed

Fayez Nassar for their help, continuous encouragement and support throughout this

work.

Special appreciation goes to Urea Process Engineers in Helwan Fertilizer

Company for their support to provide the technical data of Urea solution evaporation

process.

Finally, I wish to record my sincere appreciation and thanks to my father, mother

and my wife who formed part of my vision, support, prayers and encouragement.

ii

ABSTRACT

In urea industry, the evaporation stage is an essential process to obtain higher

concentration of urea solution. This needs certain conditions of vacuum and

temperature to ensure optimum operation. The modelling and control of industrial

urea evaporation process is considered a challenge due to the natural existence of

non-linear, time delay, multivariable interactions and constrained process

characteristics. The traditional PI control strategy has limitations to control the

processes which have the previous characteristics at optimum operation conditions.

Advanced Process Control (APC) is expected to provide an optimized solution to

improve the control performance. One of the most efficient APC techniques is

model predictive control.

In this work, a dynamic simulation of urea evaporation process based on real-time

control system is presented. Traditional PI control and linear model predictive

control strategies were applied on the process based on industrial real-time control

system, which makes the theory more applicable.

The results obtained showed a significant improvement of the control performance

after applying Model Predictive Control technology compared to traditional PI

control scheme in both the disturbance rejection and set-point tracking.

iii

TABLE OF CONTENTS

ACKNOWLEGEMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

TABLE OF CONTENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

ABBREVIATONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

SYMBOLS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

I

II

III

V

VI

VIII

IX

Chapter 1 INTRODUCTION

1.1

1.2

1.3

1.4

1.5

1.6

1.7

Process Control Background . . . . . . . . . . . . . . . . . . . . . . . . . .

Model Predictive Control . . . . . . . . . . . . . . . . . . . . . . . . .

Process Model Identification . . . . . . . . . . . . . . . . . . . . . .

Problem Statement of Industrial Urea Evaporation Process. .

Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Thesis Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Thesis Layout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1

3

4

4

5

5

6

Chapter 2 EVAPORATION DYNAMIC MODELING

2.1

2.2

2.3

2.4

2.4.1

2.4.2

2.4.3

2.5

2.5.1

2.5.2

2.6

2.7

2.7

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Evaporation Process Description . . . . . . . . . . . . . . . . . . .

Evaporation Process Control . . . . . . . . . . . . . . . . . . . . . .

UWS Characteristic. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

UWS Density . . . . . . . . . . . . . . . . . . . . . . . . . . . .

UWS Specific Heat Capacity . . . . . . . . . . . . . . . .

Equilibrium Phase Of UWS . . . . . . . . . . . . . . . . .

Evaporation Mathematical Model . . . . . . . . . . . . . . . . . .

The Evaporation Material Balance . . . . . . . . . . . .

The Evaporation Energy Balance . . . . . . . . . . . . .

Model Implementation . . . . . . . . . . . . . . . . . . . .

Model Verification . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7

7

9

11

11

11

11

13

13

13

16

16

20

iv

Chapter 3 SYSTEM IDENTIFICATION

3.1

3.2

3.3

3.3.1

3.3.2

3.4

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

System Identification Methodology . . . . . . . . . . . . . . . . . . . .

Evaporation Process Identification. . . . . . . . . . . . . . . . . . . . .

System Identification Experiment . . . . . . . . . . . . . . . . . . . . .

Model Estimation And Validation. . . . . . . . . . . . . . . . . . . .

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

21

21

26

26

28

31

Chapter 4 MODEL PREDICTIVE CONTROL

4.1

4..2

4.3

4.4

4.5

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Model Predictive Control Concept . . . . . . . . . . . . . . . . . . . . .

Multivariable MPC Formulation . . . . . . . . . . . . . . . . . . . . . . .

MPC with Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

32

32

34

38

41

Chapter 5 REAL-TIME SIMULATION AND RESULTS

5.1

5.2

5.3

5.4

5.5

5.6

5.6.1

5.6.2

5.7

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Control Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Proportional and Integral (PI) Controller. . . . . . . . . . . . . . . . .

PI/MPC Strategies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Real-Time Simulator Structure. . . . . . . . . . . . . . . . . . . . . . . .

The Simulation Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Disturbance Rejection . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Set-Point Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

42

42

42

44

45

49

49

49

53

Chapter 6 Conclusion And Future Work

6.1

6.2

Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Future Work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

54

55

REFERENCES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

APPENDICES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

56

58

v

LIST OF FIGURES

Figure 1.1

Figure 2.1

Figure 2.2

Figure 2.3

Figure 2.4

Figure 2.5

Figure 3.1

Figure 3.2

Figure 3.3

Figure 3.4a

Figure 3.4b

Figure 3.5

Figure 4.1

Figure 4.2

Figure 5.1

Figure 5.2

Figure 5.3

Figure 5.4

Figure 5.5

Figure 5.6

Figure 5.7

Figure 5.8

Modern Control System Hierarchy. . . . . . . . . . . . . . . . . . . . . . .

Long-Tube Vertical Evaporator Diagram. . . . . . . . . . . . . . . . .

The Evaporator Process P&ID Diagram. . . . . . . . . . . . . . . . . .

Equilibrium Phase Of (Water/Urea) System. . . . . . . . . . . . . . .

Evaporation Model Function Blocks Relation Diagram. . . . . .

Inputs Impulse Response Of The Evaporation Mode. . . . . . . . . .

The System Identification Loop. . . . . . . . . . . . . . . . . . . . . . . . . .

Identification Test Strategy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

The Input/ Output Results Of GBN Test Signals. . . . . . . . . . . . .

The Measured And Simulated Model For Y1. . . . . . . . . . . . . . . .

The Measured And Simulated Model For Y2. . . . . . . . . . . . . . . .

Step Responses Of The Identified MIMO [3x2] Model. . . . . . . .

Block Diagram For Model Predictive Control. . . . . . . . . . . . . . .

Basic Concept For Model Predictive Control. . . . . . . . . . . . . . . .

PI controllers mode selection Structure. . . . . . . . . . . . . . . . . . . .

HMI (800xa ) PI Controller Faceplate. . . . . . . . . . . . . . . . . . . . .

Comparison Between PI and MPC Control Strategy. . . . . . . . . .

Real Time Control System Structure. . . . . . . . . . . . . . . . . . . . . . .

HMI 800xa System Graphic Display . . . . . . . . . . . . . . . . . . . . . .

The Closed-Loop Dynamic Responses Of UWS Feed Flow As

A Measured Disturbance Rejection . . . . . . . . . . . . . . . . . . . . . . .

The Closed-Loop Dynamic Responses Of UWS Product

Temperature Set-Point Tracking. . . . . . . . . . . . . . . . . . . . . . . . . .

The Closed-Loop Dynamic Responses Of Vacuum Set-Point

Tracking. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3

8

10

12

18

19

22

26

27

28

29

31

33

34

43

44

45

46

48

50

51

52

vi

Figure B1.1

Figure B1.2

Figure B1.3

Figure B1.4

Figure B1.5

Figure B1.6

Figure C1

Figure D1

Figure E1.1

Figure E1.2

Figure E1.3

Figure E3.1

Figure E3.2

Figure E3.3

Figure E3.4

Figure E3.5

Figure E4

Main Evaporation Simulink System. . . . . . . . . . . . . . . . . . . . . . .

UWS Feed Valve (V1) Sub-System. . . . . . . . . . . . . . . . . . . . . . .

Steam Valve (V2) Sub-System. . . . . . . . . . . . . . . . . . . . . . . . . . .

Steam/Ejector Valve (V3) Sub-System. . . . . . . . . . . . . . . . . . . . .

UWS Product Temperature Differential Equation Sub-System. .

Separator Vacuum Pressure Differential Equation Sub-System. .

Simulink Identification Experiment. . . . . . . . . . . . . . . . . . . . . . .

Simulink MPC Controller. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

NI USB-6008 DAQ Device. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

NI DAQ Simulink Analog Input Interface. . . . . . . . . . . . . . . . . .

NI DAQ Simulink Analog Output Interface. . . . . . . . . . . . . . . . .

AC450 Controller Rack. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

DCS Application Program FIC-1 Controller. . . . . . . . . . . . . . . . .

DCS Application Program TIC-001 Controller. . . . . . . . . . . . . .

DCS Application Program FIC-2 Controller. . . . . . . . . . . . . . . . .

DCS Application Program PIC-001 Controller . . . . . . . . . . . . . .

MATLAB/OPC Toolbox . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

60

61

61

61

61

61

69

72

75

76

76

78

79

80

81

82

83

vii

LIST OF TABLES

Table 2.1

Table 2.2

Table 3.1

Table 3.2

Table 5.1

Table 5.2

Table A1

Table A2

Table A3

Table E1

Evaporation Process Streams Properties At 100% Load . . . . . .

List Of The Model Variables Description . . . . . . . . . . . . . . . . .

Test Signals Parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

The Validation Best Fit [%]. . . . . . . . . . . . . . . . . . . . . . . . . . . . .

PI/MPC Strategies Control Modes . . . . . . . . . . . . . . . . . . . . . . .

PI Controller’s Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Saturated Steam Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Superheated Steam Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Steam Valve (V2) Vol. Flow C/CS Curve Table . . . . . . . . . . . .

List of Input/Output Signals Of DCS Controller . . . . . . . . . . .

9

16

26

28

46

47

60

61

61

75

viii

ABBREVIATIONS

APC Advanced process control

ARX Auto regressive with external input

CVs Controlled variables

DCS Distributed control system

GBN Generalized binary

LPS Low pressure steam

LTI Linear time invariant

MD Measured disturbance

MIMO Multi input multi output

MPC Model predictive Control

MVs Measured variables

OLE Object linking embedded

OPC OLE for process control

PI Proportional and integral

PID Proportional, integral and derivative

QP Quadratic programming

RTO Real Time Optimization

SP Set-point

SS State space

TF Transfer Function

UWS Urea/water solution

ix

SYMBOLS

Symbol Description unit

Steam density kg/m3

Urea/water solution density kg/m3

Specific heat capacity of Urea kJ/(kg. )

Specific heat capacity of water = 4.18 kJ/(kg. )

Total Specific heat capacity of UWS kJ/(kg. )

, UWS temperature feed , product

, Urea feed , product concentration %

Separator vacuum Bar a

Ps Steam pressure Bar a

s Enthalpy of evaporated steam (Latent heat) kJ/kg

Steam heat transfer rate kW

, The urea, water solution feed , product energy kW

Vapour energy kW

M Mass of UWS in tubes = V[m3] [Kg/m3] kg

ms Steam mass flow rate kg/s

UWS mass flow rate feed , product kg/s

, vo Vapour mass flow rate kg/s

Ps Steam pressure Bar a

UWS volumetric flow rate feed m3/h

23

CHAPTER 1

1

CHAPTER 1

INTRODUCTION

1.1 Process Control Background

In recent years, the process control role has been extended due to technology

development. The primary objective of process control is to maintain the process at the

operational conditions and set-points. The control systems are continuously developed

to satisfy the process control requirements like production safety, quality and

flexibility, energy and material consumption reduction as well as environmental

pollution.

The industrial chemical processes are becoming larger, more complicated and also

have common characteristics such as nonlinear, with multiple inputs and outputs, time

delays and input constraints. These make the process control so challenging using

conventional control strategies.

A modern industrial control system hierarchy is shown in Figure 1.1 and consists of

the following main layers [1]:

1- Basic Process Control Layer:

This layer includes the primary control system which gathers process measurements,

performs regulatory process control and monitoring function. Traditionally, the

industrial processes are operated using Distributed Control Systems (DCS) which

providing a tool easy implementation of existing control strategies such as feed

forward, cascade, ratio , split range and etc. These are control schemes based on the

proportional-integral-derivative (PID) single-loop feedback controller.

The PID controller is the most common control algorithm used in industry and has

been universally accepted in industrial control. The reasons why PID control dominant

in the industry lies in their robust performance and functional simplicity, which allows

REAL-TIME CONTROL OF INDUSTRIAL UREA EVAPORATION PROCESS …

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