NEURO-FUZZY TECHNIQUE APPLICATION FOR IDENTIFYING FLOW

REGIMES OF PARTICLES CONVEYING IN PNEUMATIC PIPELINE

MUTAZ MOHAMED ELHASSAN ELSAWI KHAIRALLA

UNIVERSITI TEKNOLOGI MALAYSIA

NEURO-FUZZY TECHNIQUE APPLICATION FOR IDENTIFYING FLOW

REGIMES OF PARTICLES CONVEYING IN PNEUMATIC PIPELINE

MUTAZ MOHAMED ELHASSAN ELSAWI KHAIRALLA

A project report submitted in partial fulfilment of the

requirements for the award of the degree of

Master of Engineering (Electrical – Mechatronics & Automatic Control)

Faculty of Electrical Engineering

Universiti Teknologi Malaysia

JULY 2012

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To my beloved mother and father

To my brothers and sisters

To the Islamic nation

iv

ACKNOWLEDGEMENT

In The Name Of Allah, Most Gracious, Most Merciful

First and foremost, I must be thankful to Allah SWT on His blessing for

finishing the research.

I would like to express my sincere thanks and appreciation to my supervisor

Prof Dr Mohd Fua'ad bin Hj Rahmat, for his precious guidance, encouragement,

constructive criticisms, advice and motivation. Without his continual support and

interest, this project report would not have been that same as presented here.

Besides that, my highly appreciation extended to my parents for their

supporting, patience, love, prayer and sacrifice are unbounded during my study. My

special thanks must be extended to All my friends who supports me and help me.

Last but not least, thanks to all those who give helps and support to me directly or

indirectly for the success of this thesis

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ABSTRACT

The desire to satisfy demand of industrial sector by improving product quality

and reducing environmental emission leads up to identify and monitor the behaving

of the internal flows inside pipelines. The flow of solid particles through pipeline in

vertical gravity flow rig system has been monitored by 16-electrodynamic sensors

that measure the charge carried by solid particles. The identification model has been

built and developed based on the training of the captured data at different flow

patterns. The final identification model consists of four ANFIS based fuzzy C-means

clustering where every ANFIS is able to identify the presence of the flow inside

specific quarter in the cross section of the pipe. It is shown that the four ANFIS

models are able to work simultaneously to provide the expected output after applying

simple thresholding for the ANFIS’ output. The identification model has been

evaluated by ten different types of flow patterns. The accuracy of the identification

model has improved at higher flow rate. As a result, the identified flow pattern has

been used to acquire the concentration profile by using filtered back projection. The

successful ANFIS model can be extended for horizontal pipeline to present the

percentage of flow inside the pipe.

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ABSTRAK

Keperluan untuk memenuhi permintaan sektor perindustrian dengan

meningkatkan kualiti produk dan mengurangkan pelepasan gas beracun ke alam

sekitar telah membawa kepada teknologi identifikasi dan pemantauan aliran di dalam

saluran paip. Aliran zarah pepejal melalui saluran paip di dalam sistem pelantar

minyak yang beraliran tegak telah dipantau melalaui 16 penderia elektronik yang

mengukur cas yang dibawa oleh zarah pepejal tersebut. Model identifikasi telah

dibangunkan dan dibina berdasarkan latihan data yang diperolehi daripada corak

aliran yang berbeza. Model identifikasi ini terdiri daripada empat ANFIS yang

berdasarkan konsep Fuzzy C-Means pengelompokan, di mana setiap ANFIS mampu

mengenal pasti kehadiran aliran di dalam bahagian tertentu di dalam keratan rentas

paip. Kesemua model ANFIS dinyatakan mampu beroperasi secara serentak dalam

menghasilkan output terjangka selepas proses pengambangan untuk mendapatkan

output ANFIS. Model identifikasi ini telah dinilai mengunakan 10 jenis corak aliran

yang berlainan. Terdapat peningkatan di dalam ketepatan model identifikasi pada

kadar aliran yang lebih tinggi. Model ANFIS ini boleh dilanjutkan untuk digunakan

di saluran paip mendatar untuk menilai peratusan aliran di dalam paip.

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TABLE OF CONTENTS

CHAPTER TITLE PAGE

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENT iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENTS vii

LIST OF TABLES ix

LIST OF FIGURES x

LIST OF SYMBOLS xii

LIST OF APPENDICES xiii

1 INTRODUCTION 1

1.1 Background 1

1.2 Problem Statement 2

1.3 Project objectives 3

1.4 Scope of project 3

2 LITERATURE REVIEW 4

2.1 Introduction 4

2.2 Process tomography 5

2.2.1 Electrical capacitance tomography 5

2.2.2 Optical Tomography 6

2.2.3 Radiometric sensor 7

2.2.4 Electrodynamic sensor 8

2.3 Flow regime identification 9

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2.3.1 Identification using fuzzy logic 10

2.3.2 Identification using neural network 11

2.3.3 Identification using neuro-fuzzy 12

3 METHODOLOGY 14

3.1 Introduction 14

3.2 Methodology 14

3.3 Experimental setup 16

3.4 Adaptive-Network-based Fuzzy Inference System 19

4 RESULTS AND DISCUSSION 23

4.1 Introduction 23

4.2 Experimental results of the captured data 23

4.3 Training of the identification model 28

4.4 Evaluation of the identification model 30

4.5 Concentration profile of the flow 33

5 CONCLUSION AND FUTURE WORKS 35

5.1 Introduction 35

5.2 Conclusion 35

5.3 Future Work 36

REFERENCES 38

Appendices A – D 41-66

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LIST OF TABLES

TABLE NO. TITLE PAGE

4.1 Specification of the trained ANFIS 28

‎4.2 Accuracy of the identification model 32

4.3 The accuracy of the complete identification model 33

A.1 Samples from the Training data 44

A.2 Samples from the Testing data 46

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LIST OF FIGURES

TABLE NO. TITLE PAGE

‎2.1 Electrical Capacitance Tomographic system 6

‎2.2 Block diagram for single optical fiber sensor 6

‎2.3 -Ray sensors for solids flow imaging 8

‎2.4 Electrodynamic tomographic system for solid imaging 9

‎2.5 Membership functions for the fuzzy outputs 11

‎2.6 Neural Network model for flow regime identification 12

‎2.7 Flow regime identification using neuro fuzzy 13

‎3.1 Flow Chart of Research Methodology 15

‎3.2 Vertical flow rig system 16

‎3.3 Electrodynamic sensor 17

‎3.4 Different baffles shape 18

‎3.5 Effect of introducing half flow baffles 18

‎3.6 Equivalent ANFIS architecture 19

‎3.7 ANFIS model for flow regime identification 22

‎4.1 Sensor output without flow 24

‎4.2 Captured voltages of 16-electrodynamic sensors at 332 g/s 25

‎4.3 The flow patterns for different type of flow at 290 g/s 27

‎4.4 structure for the trained ANFIS of Quarter 1 29

‎4.5 Mean square error of the ANFIS models 29

‎4.6 Output of the ANFIS models during the evaluation process 31

‎4.7 Output of the Identification model after thresholding 31

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‎4.8 Example of concentration profile by using linearized filtered

back-projection 34

‎4.9 Concentration profile after by using filtered back-projection

algorithm 34

‎B.1 The sensitivity model for sensor 1 48

‎B.2 The sensitivity map for sensor 1 49

‎B.3 The half flow filter mask 50

‎D.1 KUSB-3116 Module 64

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LIST OF SYMBOLS

- Electrical charge

- Distance

- Permittivity of Free Space

- Uniform Radial Field

- Neural-Fuzzy Weight

- Neural-Fuzzy Membership

- Neural-Fuzzy Node Output

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LIST OF APPENDICES

APPENDIX TITLE PAGE

A Training and Testing Data 40

B Concentration Profile 46

C MATLAB Codes 50

CHAPTER 1

1 INTRODUCTION

1.1 Background

Tomographic imaging systems are designed to analyze the structure and

compositions of objects by examining them with waves, radiation or other electrical

techniques and by calculating virtual cross section through them (Grangeat, 2010).

Tomography means that the process to obtain the cross sectional image of a body or

a process. The development of tomographic instrumentation, started in the 1950s,

has led to the widespread availability of body scanners, which are so much a part of

modern medicine (Beck & Williams, 1996). The first whole-body computerized

tomography (CT) was been introduced in 1975.

Since 1990s, industry has become under pressure to utilize resources more

efficiently and to satisfy demand and legislation for product quality and reduced

environmental emissions. Hence there is an increasing need to know more about the

exact way the internal flows in process equipment are behaving. Often this must be

done non-invasively by tomographic instrumentation because conventional

measuring instruments may either be unsuitable for exposure to the harsh internal

conditions of the process, or by their presence upset the operation of the process.

There is now a widespread appreciation of the need for the direct analysis of the

internal characteristics of process equipment; the measuring instruments for such

applications must use robust, non-invasive sensors which can operate in the

proximity of aggressive and fast-moving fluids and multiphase mixtures.

2

Tomographic imaging systems also involve using tomographic imaging methods to

manipulate the data from remote sensors in order to obtain precise quantitative

information from inaccessible locations (Beck & Williams, 1996).

The process tomography is basically consist of set of sensors mounted around

the cross section of the pipeline to sense some characteristics of the flow particles

that will be used to reconstruct an image for the cross section of the pipe by using

some reconstruction algorithms. There are a lot of reconstruction algorithm that are

been used to obtain tomogram of the flow like back projection, iterative

reconstruction and there are some analytical reconstruction approaches. But some

reconstruction algorithms need to identify the type of flow for the conveying

particles to better obtain the final tomogram of the flow.

Industrial processes use various methods for tomographic imaging and the

selection between these techniques is affected by:

1. The nature of components contained in the pipeline, vessel, reactor or

material being examined.

2. The information sought from the process and its intended purpose.

3. The size of the process equipment and the length scale of the phenomena

being investigated.

4. The process environment (Williams & Beck, 1995).

1.2 Problem Statement

Electrodynamic sensors have been used to obtain the tomogram of the

conveyed solid particles in the pneumatic pipeline. The collected data has been used

to reconstruct the image of the flow in the cross section of the pipe by using filtered

back-projection algorithm. Filtered back projection algorithm needs to know the

type of the flow regime conveying inside the pipeline before it be able to calculate

the filter mask which has been used to obtain the tomogram of the flow. Neuro-

fuzzy system has been trained to classify the type of flow into quarter, half, three

3

quarter, inverse quarter, inverse half, inverse three quarter, center, full flow in order

to improve the previous researcher results using neural network and fuzzy logic.

1.3 Project objectives

The specific objectives of this project are:

To use the outputs of the electrodynamic sensors, which are mounted around

the cross section of the pipeline to obtain the tomography of the flow.

To identify flow regime of particles inside the pipe of vertical gravity flow rig

system by using neuro-fuzzy technique.

To use feuro-fuzzy identification model and electrodynamic sensors’ outputs

to obtain the tomogram of the flow.

1.4 Scope of project

The scope of this project begins with collecting the output of the 16-

electrodynamic sensors around the circumstance of the pipe at different flow regimes

of particles in vertical flow rig system. The Neuro-Fuzzy model will be trained by

using set of the output of the electrodynamic sensors as inputs for the model at

different flow regimes, which is determined by using baffles of different shapes, and

then the Neuro-Fuzzy model will be able to predict the type of flow inside the

pipeline for other sets of inputs.

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