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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