KINETIC MODELING APPROACH AND DESIGN OF A FULL-SCALE COMMERCIAL MODEL FROM A LAB-BATCH SCALE SLURRY POLYMERIZATION PROCESS

HDPE Bimodal Technology Development, STC Geleen The Netherlands

Francesco Bertola

Nic Friederichs

Miran Milosevic

ChemProcessInnovation

Ramanathan Sundaram

14th October 2013 , Advances in PO Santa Rosa, California

No. 1

• Introduction / SABIC

• Objective

• Approach

• Model and Kinetics

• Outlook

OUTLINE

No. 2

SABIC IN NUMBERS

1976, our beginning

2nd largest global diversified chemical company*

88th largest public company in the world*

90 B$ total assets

50 B$ annual revenue

40,000 employees in 40 countries

6 Strategic Business Units

62 world-class plants worldwide

1 Corporate Research & Innovation Centre

17 Technology and Application Centres

150 new products each year

8,000 global patents

* Forbes 2012

No. 3

OUR GLOBAL OPERATIONS

SABIC Global Headquarters (1)

Technology Centers (13)

Application Centers (4)

SABIC Corporate Research

and Innovation Center (1)

Distribution, Storage Facilities

and Logical Hubs (52)

International Subsidiaries and

Sales Offices (81)

Manufacturing and Compounding

Companies (62)

No. 4

PRODUCTION HAS MULTIPLIED BY 5 IN 20 YEARS

A high rate of growth…

Pro

du

cti

on

(m

illio

n to

ns)

…reaching 72M metric tons in 2012

Metals

5.615

Fertilizers

6.546

Chemicals

46.122

Polymers

11.933

Performance Chemicals

0.558

Innovative Plastics

1.214

0

10

20

30

40

50

60

70

80

1985 1990 1995 2000 2005 2010 2011 2012

22

13

28

47

69 72

6

67

No. 5

INNOVATIVE PLASTICS

• LEXAN™, NORYL™, ULTEM™, VALOX™,

XENOY™, CYCOLAC™, CYCOLOY™,

EXTEM™, GELOY™, XYLEX™

• LNP™ specialty compounds

• EXATEC™ glazing technology

• SABIC® PP compounds; STAMAX® long

glass fiber-filled PP

• Specialty film and sheet

• Specialty Additives and Intermediates

Brands marked with ™ are trademarks of SABIC

POLYMERS

• Polyethylene

• Polypropylene

• Polyethylene terephthalate

• Polyvinyl chloride

• Polystyrene

SIX STRATEGIC BUSINESS UNITS

CHEMICALS

• Olefins and gases

• Oxygenates

• Aromatics and chlor-alkali

• Glycols

PERFORMANCE

CHEMICALS

• Ethanolamines

• Ethoxylates

• Linear alpha olefins

• Catalysts ….

FERTI LIZERS

• Urea

• Ammonia

• Phosphates

METALS

• Long steel

• Flat steel

No. 6

WHY MODELING?

Development:

0.5 lt, batch

10 lt, batch 15 lt,

continuous

Pilot plant,

100 kg/hr,

continuous

Commercial plants,

150-300 m3,

continuous

Data transferability, Catalyst performance

No. 7

KINETIC/PLANT MODEL

The Kinetic Behaviour / Mechanism of the polymerization in the Commercial Plants

• Quantify the effect of key R-or variables on the polymerization rate, Mn, Mw, MWD, etc.

• Phenomena effects: multi-site nature of ZN-cat

• Design the experiments / approach

• Batch lab reactors data for ZN kinetics

• Obtain a single set of kinetics with the modeling tool

• Kinetics tuned to Commercial Plant data for several grades

No. 8

CHALLENGE: CHANGE IN SCALE

Ti

Cl

Cl

Cl Cl

Cl

Cl

CH2

CH2

CH2

CH2

CH2

CH2

Active Site (<10-10 m)

• Insertion of monomer, formation of chains

• Intrinsic kinetics

Sub-Particle fragment (10-10-10-6 m)

• Sorption and diffusion of monomer

• Distribution of active sites

• Local conditions, crystallization of macromolecules

Particle Scale (10-5-10-3 m)

• Transport across boundary layer

• Internal diffusion (pore & polymer

• Particle morphology

Particle Swarm (10-2-10-1 m)

• Particle-particle interaction

• Particle-wall interaction

• Agglomeration, sheeting

Reactor Scale (1-10 m)

• Macromixing

• Heat removal

• Reactor hydrodynamics

No. 9

KINETIC MODELING REQUIREMENTS

Property Model

Reaction Mechanism

Experimental Data

Fitting Methodology

Modeling Tool

PC-SAFT Equation of State *

Pure Component & Binary Interaction Parameters

*Perturbed-Chain Statistical Associated Fluid Theory: An Equation of State Based

on a Perturbation Theory for Chain Molecules;

Joachim Gross and Gabriele Sadowski

No. 10

KINETIC MODELING REQUIREMENTS

Property Model

Reaction Mechanism

Experimental Data

Fitting Methodology

Modeling Tool

Mechanism Rate constant

Catalyst Site Activation with Cocatalyst 𝑘act(𝑗)

Chain Initiation by Monomer / Comonomer 𝑘𝑖,𝐴(𝑗), 𝑘𝑖,𝐵(𝑗)

Chain Propagation Monomer / Comonomer 𝑘𝑝,𝐴𝐴(𝑗), 𝑘𝑝,𝐴𝐵(𝑗),

𝑘𝑝,𝐵𝐴(𝑗), 𝑘𝑝,𝐵𝐵(𝑗),

Chain transfer to Monomer / Comonomer 𝑘𝑡𝑚 𝐴𝐴(𝑗), 𝑘𝑡𝑚,𝐴𝐵(𝑗),

𝑘𝑡𝑚,𝐵𝐴(𝑗), 𝑘𝑡𝑚,𝐵𝐵(𝑗),

Chain transfer to Hydrogen 𝑘𝑡ℎ,𝐴(𝑗), 𝑘𝑡ℎ,𝐵(𝑗)

Catalyst Site Inhibition with Hydrogen (forward and reverse) 𝑘ℎ𝑖𝑓(𝑗), 𝑘ℎ𝑖𝑟(𝑗)

Spontaneous catalyst Site Deactivation 𝑘𝑠𝑑(𝑗)

Catalyst Type Reaction Species

No. 11

KINETIC MODELING REQUIREMENTS

Property Model

Reaction Mechanism

Experimental Data

Fitting Methodology

Modeling Tool

Cocatalyst Conc. Site Activation Ch. Tran. Cocatalyst

Mon/CoMon Pressure Initiation & Propagation Ch. Tran. Mon/CoMon

Polymerization Time Deactivation Spontaneous Ch. Tran.

H2/C2 Ratio Hydrogen Inhibition Ch. Tran. Hydrogen

Catalyst Charge Catalyst Site Density Propagation

C2 Uptake profile GPC

Required Data

Molecular weight

No. 12

KINETIC MODELING REQUIREMENTS

Property Model

Reaction Mechanism

Experimental Data

Fitting Methodology

Modeling Tool

Consider all the reactions as elementary; Set rate orders to 1

Fit k0 at the base temperature (set Ea = 0 and Tref = base

temperature)

Fit activation energies with data at several different

temperatures

No. 13

Homopolymerization of ethylene without comonomer or

hydrogen

1. Catalyst site activation by cocatalyst

2. Chain initiation by monomer

3. Propagation

4. Chain transfer to monomer

5. Site deactivation

At base temperature

Production rate and Mw data from several homopolymerization

experiments at different temperature to fit the activation

energies for these reactions

KINETIC MODELING REQUIREMENTS

Property Model

Reaction Mechanism

Experimental Data

Fitting Methodology

Modeling Tool

No. 14

Effect of comonomer

1. Propagation reactions

2. Chain transfer to monomer reactions

At base temperature

Using the number average molecular weight of the polymer

we would fit the chain transfer to monomer rate constants

Using data at different temperatures and different levels of

comonomer we fit the activation energies for these reactions

KINETIC MODELING REQUIREMENTS

Property Model

Reaction Mechanism

Experimental Data

Fitting Methodology

Modeling Tool

No. 15

The effect of hydrogen on the production rate/activity

profile with different levels of H2

1. Forward site inhibition

2. Reverse site inhibition

At base temperature

Using the number average molecular weight of the

polymer from these experiments we would fit the chain

transfer to H2 rate constants

KINETIC MODELING REQUIREMENTS

Property Model

Reaction Mechanism

Experimental Data

Fitting Methodology

Modeling Tool

No. 16

KINETIC MODELING REQUIREMENTS

Property Model

Reaction Mechanism

Experimental Data

Fitting Methodology

Modeling Tool

The single set of single site kinetic over a range of

experimental datasets

• Perform GPC deconvolution on a number of experimental

datasets to determine minimal number of sites necessary

to reproduce the measured GPC curves

• The rate constants for multi-site kinetics are set and fine

tuned to get a good reproduction of the molecular weight

distribution over the different datasets

• Implement the rate constanst into the plant model and fine

tune for the several grades

No. 17

KINETIC MODELING REQUIREMENTS

Property Model

Reaction Mechanism

Experimental Data

Fitting Methodology

Modeling Tool

• Aspen Custom Modeler

• Aspen Plus

No. 18

SINGLE SITE KINETICS

No. 19

MULTI SITE KINETIC EXAMPLE

Input data

Predicted MN,

MW of each site

Predicted

MN, MW

Deconvolution Results :

4-9 site types

Molecular weight distribution (MWD)

*GPC: Gel Permeable Chromatography

1 1

2

3

4

5 6

Macros for running the deconvolution program

No. 20

KINETIC CATALYST MODEL TO PLANT MODEL

Initial process conditions for new recipes / optimize transition material

Plant operating window (downstream equipment) applying new process conditions (recipes)

Limitations / improvement of:

Drying unit

Wax recovery unit

Monomer / Co-monomer recovery

Influence of adding / removing equipment / optimizing downstream processing

Translate results from lab-batch, to plant conditions (plant control)

Effect of RTD (i.e. production rate) on product

Use of different co-catalysts

Compare different catalysts

No. 21

PLANT MODEL EXAMPLE

No. 22

OUTLOOK

1. Modeling of the reactors including ZN kinetics, to evaluate the interaction between

mixing, mass transfer and reaction rate for the different regions in the reactors

2. Collect product property (MI, density) and molecular structure (Mw avg, copolymer

composition) data for several grades to develop structure property correlations relating

the product property to the polymer molecular structure. These correlations can be

incorporated into the plant model so that it also predicts the product properties.

.

No. 23

Thank you for your attention