1

MELT FLOW INDEX ESTIMATION USING NEURAL NETWORK

MODELS FOR PROPYLENE POLYMERIZATION PROCESS

NUR FAZIRAH BINTI JUMARI

UNIVERSITI TEKNOLOGI MALAYSIA

i

MELT FLOW INDEX ESTIMATION USING NEURAL NETWORK MODELS

FOR PROPYLENE POLYMERIZATION PROCESS

NUR FAZIRAH BINTI JUMARI

A thesis submitted in partial fulfilment of the

requirements for the award of the degree of

Master of Engineering (Chemical)

Faculty of Chemical Engineering

Universiti Teknologi Malaysia

DECEMBER 2013

iii

To Allah~ for guiding me to the straight path,

To my other half~ for love, understanding, and support

To my sunshine~ for being with me along this journey

To family members, lecturers, close friends~ for endless care and assistances

and

Those people who have guided and inspired me throughout my journey of

education and life

Thank you

iv

ACKNOWLEDGEMENTS

Alhamdulillah, thank to Him for guiding me, and leading me to the straight

path. In completing this research, there were lots of hurdles, challenges and

tremendous obstacles that I had to endure. This research would not have been

possible without the support of many people. So, I would like to express my

heartfelt appreciation to all those who have contributed directly and indirectly

towards my completion of this research.

I am very indebted to my research project supervisor, Associate Professor

Dr. Khairiyah Mohd Yusof, thanks to her detailed and constructive comments, and

for her important supports throughout this work. My warmest gratitude extends to

the Ministry of Science, Technology and the Innovation (MOSTI) for funding my

research through National Science Fellowship (NSF) scholarship.

My sincere appreciation also extends to my other half, Mohammad Nazri

bin Ngadinin for his endless love, understanding, and never ending support through

the duration of my studies. A load of my love goes to my dear sunshine, Khaulah

Mumtazah for her existence in my life. I owe my loving thanks to my parents and

families. Thank you for their loving support and being patient with me. My special

gratitude is due to my study circle mates, without their encouragement and pray, it

would have been impossible for me to finish this work. Lastly, I offer my regards

and blessings to all of those who supported me in any respect during the

completion of the research.

v

ABSTRACT

One of the major challenges in polymerization industry is the lack of online

instruments to measure polymer end-used properties such as melt flow index

(MFI). As an alternative to the online instruments and conventional laboratory

tests, these properties can be estimated by using a model based-soft sensor. This

research presents models for soft sensors to measure MFI in industrial

polypropylene loop reactors by using the artificial neural network (ANN), hybrid

FP-ANN (HNN) and stacked neural network (SNN) models. The ANN model of

the two loop reactors was developed by employing the concept of Feed-Forward

Back Propagation (FFBP) network architecture using Levenberg-Marquardt

training method. Serial hybrid FP-ANN (HNN) models were developed in this

study. The error between actual MFI and simulation MFI from FP model was fed

into the HNN model as one of the input variables. To construct the stacked neural

network (SNN) model, two layers were needed: 1) level-0 generalizer output

comes from a number of diverse ANN models and 2) level-1 generalizer was

developed using the results of level-0 generalizer with additional input variables.

All models were developed and simulated in MATLAB 2009a environment. The

simulation results of the MFI based on the ANN, HNN, and SNN models were

compared and analyzed. The HNN model is the best model in predicting all range

of MFI with the lowest root mean square error (RMSE) value, 0.010848, followed

by ANN model (RMSE=0.019366) and SNN model (RMSE=0.059132). When

these three models (ANN, HNN, and SNN) were compared, the SNN model shows

the lower RMSE for each type of MFI studied.

vi

ABSTRAK

Salah satu cabaran utama dalam industri pempolimeran ialah kekurangan

instrumen dalam talian untuk mengukur sifat-sifat akhir polimer seperti indeks

aliran lebur (MFI). Sebagai alternatif kepada instrumen dalam talian dan ujian

makmal konvensional, sifat-sifat ini boleh dianggarkan menggunakan model

berasaskan soft sensor. Kajian ini mempersembahkan model yang digunakan di

dalam soft sensor untuk mengukur MFI di dalam reaktor gegelung industri

pempolimeran propena menggunakan model Artificial Neural Network (ANN),

model hibrid FP-ANN (HNN) dan model Stacked Neural Network (SNN). Model

ANN bagi dua reaktor gegelung telah dibangunkan dengan menggunakan konsep

Feed-Forward Back Propagation (FFBP) melalui kaedah latihan Levenberg-

Marquardt. Model HNN bersiri telah dibangunkan juga dalam kajian ini. Ralat

antara MFI yang dijana oleh model FP dan nilai MFI yang sebenar digunakan

sebagai salah satu masukan untuk model HNN. Untuk membangunkan model

SNN, dua tahap diperlukan: 1) keluaran dari level-0 generalizer datang dari

pelbagai jenis model ANN dan 2) generalizer level-1 dibangunkan menggunakan

keputusan dari level-0 yang dibina. Kesemua model telah dibangunkan dan

disimulasikan di dalam persekitaran MATLAB 2009a. Keputusan simulasi MFI

dari model ANN, HNN, dan SNN dibandingkan dan dianalisis. Model HNN adalah

model yang terbaik jika mengukur semua nilai MFI dengan nilai root mean square

error (RMSE) sebanyak 0.010848, diikuti dengan model ANN (RMSE=0.019366)

dan model SNN (RMSE=0.059132). Jika ketiga-tiga model (ANN, HNN dan SNN)

ini dibandingkan, model SNN menunjukkan nilai RMSE yang rendah bagi

mengukur setiap jenis MFI yang dikaji.

vii

TABLE OF CONTENTS

CHAPTER TITLE PAGE

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENT iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENTS vii

LIST OF TABLES x

LIST OF FIGURES xii

LIST OF ABBREVIATION xiv

LIST OF APPENDICES xvii

1 INTRODUCTION 1

1.1 Project Background 1

1.2 Problem Statement 3

1.3 Research Objective 4

1.4 Research Scope 4

1.5 Research Contribution 5

1.6 Summary of Chapter 1 6

2 LITERATURE REVIEW ON PROPYLENE

POLYMERIZATION

7

2.1 Introduction 7

2.2 What Is Polymer? 8

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2.3 What Is Polypropylene? 8

2.4 The Resin Characteristics 9

2.5 Commercial Types of Polypropylenes 10

2.6 Industrial Manufacturing of Polypropylene 12

2.6.1 Spheripol Process 17

2.6.2 Mechanism of Polypropylene Polymer 19

2.7 Summary of Chapter 2 24

3 LITERATURE REVIEW ON ARTIFICIAL NEURAL

NETWORK, HYBRID NEURAL NETWORK AND

STACKED NEURAL NETWORKS MODELS

25

3.1 Introduction 25

3.2 Soft Sensor 26

3.3 Artificial Neural Network Model (Black box

Model)

27

3.3.1 Basic Elements of Neural Network 28

3.3.2 Network Topology 31

3.3.3 Network Training and Validation 32

3.3.4 Neural Network Model in Polymerization

Process

34

3.3.5 Limitation of Neural Network 35

3.4 Hybrid Neural Network Model (Grey box Model) 36

3.4.1 The Need of Hybrid Modeling 38

3.4.2 Hybrid Model in Polymerization Process 39

3.5 Stacked Neural Network Model 42

3.5.1 Stacked Neural Network Model in

Polymerization Process

44

3.6 Summary of Chapter 3 45

4 RESEARCH METHODOLOGY 46

4.1 Stages in Research 46

4.2 Process Description 50

4.3 Data Filtering 51

ix

4.4 Development, simulation and validation of ANN

Model

52

4. 5 Development, simulation and validation of Hybrid

Neural Network Model

59

4.6 Stacked Neural Network 64

4.7 Estimation MFI using different models (ANN,

HNN, and SNN Models)

68

4.8 Summary of Chapter 4 69

5 RESULT AND DISCUSSION 70

5.1 Introduction 70

5.2 Artificial Neural Network Model to Predict MFI 71

5.2.1 Pruning The Input Variables 73

5.3 Hybrid FP-ANN Model to Predict MFI 75

5.4 Comparison of FP Model, ANN Model and Hybrid

Model

78

5.5 Single model to predict all types of MFI 84

5.5.1 Comparison of ANN, HNN, and SNN model 86

5.6. Summary of Chapter 5 88

6 CONCLUSION AND RECOMMENDATION 90

6.1 Conclusion 90

6.2 Recommendations 91

REFERENCES 93

Appendices A-D 100-126

x

LIST OF TABLES

TABLE NO. TITLE PAGE

2.1 Industrial Process Technologies for PP Manufacture 15

2.2 Kinetic Model (Zacca and Ray, 1993) 23

4.1 Variables required in developing all the models 49

4.2 Input and Output Variables of the Network 54

4.3 Data distributions for ANN and HNN 57

4.4 Summary of model equations 61

4.5 Input and Output Variables of the Network for HNN

Model

62

4.6 Data distribution for Stacked Neural Network 64

4.7 RMSE of level-0 neural network model 66

4.8 Input and output variables for level-1 Stacked Neural

Network

67

5.1 The MFI Grades 70

xi

5.2 The Performance of the ANN Model 72

5.3 The Performance of the ANN Model after

Pruning Input Variables

74

5.4 The Performance of Hybrid FP-ANN Models 77

5.5 Comparison between ANN Model and HNN Model 79

5.6 Data distributions to develop single ANN and HNN

models

84

5.7 RMSE of level-0 neural network model 85

5.8 The performance of single model 86

5.9 New input and output variables for Level-1 Stacked

neural network

87

5.10 The performance of the models 88

xii

LIST OF FIGURES

FIGURE NO TITLE PAGE

2.1 Classification of PP manufacturing processes 16

2.2 Process Flow Diagram of Industrial Loop Reactor

(Jamaludin,2009 and Harun, 2009)

18

2.3 The mechanism of Ziegler-Natta System 21

3.1 Product quality measurement 26

3.2 Architecture of neural network 29

3.3 The example of artificial neurons 30

3.4 Different types of transfer function 31

3.5 Topology of feed forward and recurrent network 32

3.6 Two approaches used in hybrid neural network with

First principle (Ng and Hussain, 2004)

38

3.7 Stacked generalization (Sridhar and Seagrave,1996) 43

4.1 Overall research methodology 47

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4.2 Process Flow Diagram of Industrial Loop Reactor

with modelling boundary (Harun, 2009 and

Jamaludin,2009)

50

4.3 Effect of a moving average filter 52

4.4 The steps in neural network model development 53

4.5 Feed-forward neural network 55

4.6 Building a Neural Network Model 56

4.7 The steps in hybrid neural network model

development

59

4.8 Serial hybrid neural network (HNN) model 60

4.9 Stacked neural network 67

5.1 The result of the best model to predict MFI A 80

5.2 The result of the best model to predict MFI B 81

5.3 The result of the best model to predict MFI C 82

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LIST OF ABBREVIATIONS

ai - Input vector

ANN - Artificial neural network

bj - Output vector

C3 - Propylene

iHC - hydrogen concentration in reactor i (kgmol/m

3)

iMC - monomer concentration in reactor i (kgmol/m

3)

iPC

0 - catalyst active site concentration in reactor i (kgmol/m

3)

iSD

C - catalyst dead site concentration in reactor i (kgmol/m3)

iTC - cocatalyst concentration in reactor i (kgmol/m

3)

CSTR - continuous stirred-tank reactor

Dr - dead polymer chains

E - activation energy (J/mol)

i

MF - monomer mass flowrate into reactor i (kg/hr)

FP - First principle

H2 - Hydrogen

ICA - independent combining analysis

kA - site activation kinetic constant (m3/kgmol/s)

kP - polymer propagation kinetic constant (m3/kgmol/s)

kTH - chain transfer to hydrogen kinetic constant (m3/kgmol/s)

kTM - chain transfer to monomer kinetic constant (m3/kgmol/s)

kTT - chain transfer to cocatalyst kinetic constant (m3/kgmol/s)

kTS - spontaneous chain transfer to hydrogen kinetic constant

(1/s)

kD - site deactivation kinetic constant (1/s)

xv

K buffer factor of the reactors

M - number of points used in the moving average

MFI - melt flow index (g/10min)

MSA - multi-scale analysis

MMW - molecular weight of monomer (kgmol/kg)

MWD - molecular weight distribution

NAMW - number average molecular weight

PET - polyethylene terephthalate

P0/P0f - Outlet/ inlet active catalyst site concentration

PP - polypropylene

iPPPR - production rate of polypropylene in reactor i (kg/hr)

Pr - live polymer chains

PSD - Particle size distribution

iinQ - inlet volumetric flowrate into reactor i (m

3/hr)

ioutQ - outlet volumetric flowrate from reactor i (m

3/hr)

RAE - Rate of site activation by electron donor

RAM - Rate of site activation by monomer

RAS - Rate of spontaneous site activation

RP0 - Rate of initiation

RPR - Rate of propagation

RTM - Rate of chain transfer to monomer

RTH - Rate of chain transfer to hydrogen

RTS - Rate of spontaneous chain transfer

RD - Rate of site deactivation

RTC - Rate of chain transfer to co-catalyst

RTE - Rate of chain transfer to electron donor

RD - Rate of spontaneous site decativation

RDH - Rate of site deactivation by hydrogen

SP - Pure catalyst site

TEAL - Triethylaluminium

iρ - polymer slurry density inside reactor i (kg/m3)

iinρ - inlet polymer slurry density inside reactor i (kg/m

3)

xvi

iMρ - density of monomer in reactor i (kg/m

3)

iPPρ - density of polypropylene in reactor i (kg/m

3)

iPPγ - specific gravity of polypropylene in recator i

iψ0 - zeroth moment of live polymer chains in reactor i

iψ1 - first moment of live polymer chains in reactor i

iψ2 - second moment of live polymer chains in reactor i

iλ0 - zeroth moment of dead polymer chains in reactor i

iλ1 - first moment of dead polymer chains in reactor i

iλ2 - second moment of dead polymer chains in reactor i

i - reactor, i = 1, 2

j - polymer moments, j = 0th

, 1st, 2

nd

H - hydrogen

in - inlet

M - monomer

out - outlet

PP - polypropylene

T - cocatalyst

1

CHAPTER 1

INTRODUCTION

1.1 Project Background

Product quality is an important feature in the globally competitive polymer

industry (Jianli et. al, 2002). With the skyrocketing cost of raw material and energy,

stringent measures must be taken to meet quality requirements to remain

competitive. According to Ray (1986), product quality is a much more complex issue

in polymerization than in more conventional short reactions. Polymers are produced

in various grades, according to customer needs. To meet customer requirements at an

economically attractive cost, product quality measurement is therefore a necessity.

However, not all variables can be measured directly. Due to the limitations of

measurement device, it is often difficult to estimate important process variables such

as product concentration and melt flow index (MFI). Polymer qualities often have to

be evaluated in a time consuming and manpower intensive lab analysis. Thus, there

is a need to find methods to estimate the product quality in real time.

2

Polymerization reactors are very well known for their complexity especially

with regards to reactor design, polymerization recipes, uncertain reaction kinetics,

and highly exothermic reaction, which lead to difficulty in product quality control.

Developing suitable models are therefore necessary to estimate the essential

variables necessary for measurement, control or optimization. For example,

polypropylene (PP) which is normally produced in the loop reactors is one of the

most complex process and varied because it involves the types of catalyst, the kind of

reactor, and the effect of the recycle loop (M. Kim et al., 2005). The articles reported

by Uvarov and Tsevetkova (1974), Lepski and Inkov (1977) and a number of other

authors (Zacca and Ray, 1993; Debling et al., 1994; Soares, 2001; Jiang et al., 2002;

Wei et al., 2002; Reginato et al., 2003; Luo et al., 2007; and Lucca et al., 2008) are

some the significant researches in the polymerization modeling for loop reactors.

The Himont Spheripol process is one of the most widely used polymerization

process that produce polyolefin using the loop reactors. This process takes place in

the loop reactors filled with liquid propylene. A small loop reactor is used to

prepolymerize the catalyst; the main polymerization, for homopolymer or random

copolymer, takes place in one or two loop reactors. For impact copolymer production

a gas phase reactor is requested after the loop reactors.

The ability to produce polymer resins which meet customer quality demands

is the primary aim of the polymer industry. To meet customer requirements at an

economically attractive cost, product quality measurement is therefore a necessity.

Soft sensor is one of the possible solutions where the values of the desired variables

which cannot be measured are inferred from the measured variables using the

process model. Development of inferential systems for polymer properties is a very

active research area in polymerization reactor control (Chan et al., 1993; Chan and

Nascimento, 1994; McAuley et al.,1990; McAuley and MacGregor, 1991;

Kiparissides et al., 1993; Zabisky et al., 1992; Skagerberg et al., 1992, Gonzaga

et.al., 2009 etc.).

3

According to R. Sharmin et. al.,(2006), the model used to estimate polymer

properties can be roughly categorized into three groups: (1) mechanistic model

developed from first principles (for example, Chan et al., 1993; McAuley et al.,1990,

Kiparissides et al., 1993; Zabisky et al., 1992), (2) black-box model using neural

networks (Bhat and McAvoy, 1990; Chan and Nascimento, 1994; Qin and McAvoy,

1992; Rallo et al., 2002; Zhang et al., 1997, etc.), and (3) statistical model using

multivariate statistical tools (Jaeckle and MacGregor, 1998; Kiparissides et al., 1993;

MacGregor et al., 1994; Martin et al., 1999; Skagerberg et al., 1992).

M. Kim et al., (2005) developed a soft sensor in polypropylene process based

on hybrid modelling of novel clustering and black-box as well as mechanistic

models. Meanwhile, T. Yiagopoulos et al., (2004) also developed a soft sensor with a

neural network approach for monitoring polymer melt flow index (MFI) at the Basell

Spheripol process.

1.2 Problem Statement

The major problem faced by the polymerization industry is that the resin

characteristics that define polymer quality, such as melt flow index (MFI) and

density cannot be measured on-line. Properties, such as MFI, are difficult to measure

and usually unavailable in real time since it requires close human intervention (R.

Sharmin et al., 2006). They can only be measured off-line in the laboratory, which

leads to difficulty in controlling product quality in polymerization processes because

of the delay involved before the product quality is known. Consequently, in most

plants, MFI is measured only several times a day using a manual analytical test.

Products which do not meet the specifications must either be sold off at a reduced

price or wasted. This does not only cause loss of revenue, but also resources, such as

raw material, production time and energy.

4

Therefore, an on-line product quality measurement, such as MFI, is essential

in fulfilling customer requirements and preventing losses. Since sensors are not

available to measure MFI, developing a soft sensor is the next best alternative. Soft

sensors are inferential estimators, drawing conclusions from process observations

when hardware sensors are unavailable or unsuitable. A suitable, fast and robust

process model for the polymerization reactor is required so that the MFI can be

estimated from the model.

1.3 Research Objective

The purpose of this research is to develop the Himont Spheripol Process

model for propylene polymerization that can be a model as a soft sensor. The Himont

Spheripol process model is developed to predict end-use product quality, melt flow

index (MFI), by using the artificial neural network (ANN) model, hybrid neural

network (HNN) model and stacked neural network (SNN) model.

1.4 Research Scope

The study in developing the models for a soft sensor for polypropylene loop

reactors to produce homopolymer of Himont Spheripol Process has been identified as

follows:

a) Develop and simulate the different types of neural network models (ANN,HNN

and SNN) for polyproplylene loop reactors in MATLAB 2009a environment

using industrial data collected from polypropylene plant in Johor.

5

b) Use the models developed (ANN, HNN, and SNN model) to estimate the different

grades of MFI.

c) Validate the simulation results with MFI from industrial polypropylene plant data.

d) Compare the performance of the models developed (ANN, HNN, and SNN

model) in predicting the MFI values.

1.5 Research Contribution

Major contribution in this study is the development of the models (ANN,

HNN and SNN model) for a soft sensor to measure melt flow index of propylene

polymerization process in loop reactors. The development of a comprehensive

mathematical model is an essential task in understanding the polymerization

processes and translating the best of our knowledge about the interactions of

different factors that affect the system. The developed, programmed and simulated

process model for the soft sensors provides a basic study that can be used and

improved in further study which related to bulk propylene polymerization, such as

process optimization and process control. The process model also provides an

understanding of the dynamic behaviour of propylene polymerization in loop

reactors from an industry perspective.

Another contribution is to compare and determine the best models for a soft

sensor that can be utilized in describing the propylene polymerization process. The

models are very useful as a guide in industry to help the engineers and operators

understand the process clearly for the purpose of plant monitoring and

troubleshooting as well as operator training.

6

1.6 Summary of Chapter 1

Producing polymer is a complex and difficult process. The polymer is

produced in various grades according to customer requirement at an economically

attractive cost. However, it is often difficult to estimate or measure important process

variables such as melt flow index (MFI). MFI always often have to be evaluated in a

time consuming and manpower intensive lab analysis. They can only be measured

off-line in the laboratory, which leads to difficulty in controlling product quality in

polymerization processes because of the delay involved before the MFI value is

known. Consequently, products which do not meet the specifications must either be

sold off at a reduced price or wasted. This does not only cause loss of revenue, but

also resources, such as raw material, production time and energy. Thus, there is a

need to find methods to estimate the melt flow index in real time. Therefore, an on-

line MFI measurement is essential in fulfilling customer requirements and preventing

losses. The purpose of this research is to develop the Himont Spheripol Process

model for propylene polymerization that can be a model as a soft sensor. The Himont

Spheripol process takes place in the loop reactors filled with liquid propylene. The

Himont Spheripol process model is developed to predict end-use product quality,

melt flow index (MFI), by using artificial neural network (ANN) model, hybrid

neural network (HNN) model and stacked neural network (SNN) model.

99

99

Zacca, J. J. & Ray, W. H. (1993). Modelling of the liquid phase polymerization of

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Zahedi, G., Elkamel, A., Lohi, A., Jahanmiri, A., & Rahimpor, M. R. (2005). Hybrid

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Zhang, J., Martin, E.B., Morris, A.J., & Kiparissides, C., (1997).Inferential

estimation of polymer quality using stacked neural networks. Computers

and Chemical Engineering 21 (Suppl), S1025–S1030.

Zhang, J. (2008). Batch-to-batch optimal control of a batch polymerisation process

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