(508f) Monitoring Emulsion Polymerization Processes by Means of Ultra-Sound Velocity Measurements

Monitoring Emulsion Polymerization Processes

 by Means of Ultra-Sound Velocity Measurements

Heiko Brandt, Dominik Sühling, Sebastian Engell, TU
Dortmund, Germany

Introduction

Emulsion polymerization is an important industrial
(semi-) batch process for the production of latices, which are used as
adhesives or in paints. To drive the process to its economic optimum, the
desired polymer properties must be achieved at an as high as possible reaction
rate maintaining tight temperature control and respecting safety constraints.
This can only be achieved by model based control, which requires the knowledge
of the process state. In this work the usability of sound velocity measurements
to determine the process state is analyzed.

In a homopolymerization process the measurement of the
jacket and reactor temperature combined with a qualitatively good energy
balance is sufficient to monitor the reaction progress using classical heat
balance calorimetry.  This can be combined with an open loop observer for the
monomer hold up and is widely applied in polymerization processes, e.g. by
(Gugliotta et al, 1994), (de Buruaga et al, 1997), or (Vicente et al, 2001).
Today instead of calorimetry a state estimator is often applied as it offers
the opportunity to combine information from different measurement devices, to
filter the measurement noise and to estimate unknown process parameters at the
same time. Examples are the application of an extended Kalman Filter by e.g. (Gesthuisen
et al, 2004), who estimated the heat of reaction as well as the heat transfer
coefficient, or a high gain observer used by (Sheibat-Othman and Othman, 2006),
who estimated the overall reaction rate.

In a copolymerization process the measurement of the
temperatures enables only to determine the overall reaction rate, i.e. the sum
of all monomer reaction rates. But as the copolymer composition is an important
polymer property, for its control it is necessary to determine the individual
monomer consumptions. An individual measurement of the monomer consumption is
possible e.g. by the application of online gas chromatography. This was
successfully shown by (Dimitratos et al, 1989) and (Urretabizkaia et al, 1994).
But it is not used much in industry due to the infrequently available and
delayed data in addition to the necessary sampling device. Another direct
measurement method for the individual monomer consumption is spectroscopy,
often being (Near) Infrared Spectroscopy (N)IR or Raman Spectroscopy. This
method offers not only the opportunity to determine the individual monomer hold
up directly, but may also be used to measure e.g. the particle size
distribution. Examples for the application of spectroscopy are given in
(Elizalde et al, 2005) or (Santos et al, 2008). For a more frequent industrial
use, the necessary effort to obtain a calibration model, the need of
maintenance, and the high investment cost are problematic up to now.

Beside direct measurements, indirect measurements exist
that can provide information about the monomer concentrations. Examples are
densimetry and ultra-sound velocity measurements. The first one has been
applied e.g. by (Canegallo et al, 1994), but it was claimed by (Vicente, 2001)
to be not robust enough for further application. The usability of ultra-sound
velocity measurements as a second independent measurement is analyzed in this
work. As a first step towards the application to a copolymerization process, we
investigate the monitoring of the reaction progress of a styrene homopolymerization
reaction. Two different models for the computation of the ultra-sound velocity
in aqueous dispersions are studied experimentally and compared.

Ultra-Sound Velocity

Ultra-sound velocity (c) is a simple and cheap
measurement that is sensitive to changes in the different monomer concentrations
due to the big difference in c of monomers and polymers. As the ultra-sound velocity of a medium is
mainly characterized by its properties on the molecular level, detailed
modelling of c becomes very complex.  For liquids the sound velocity can be described
according to (Urick, 1947) by the simple relation:

where  represents the effective density and  the effective adiabatic compressibility of
the medium. According to (Siani et al, 1999), this equation remains valid for
suspensions as long as the operation takes place in the Long-Wavelength Regime,
meaning that the sound wavelength is much larger than the polymer particles. As
the densities as well as the adiabatic compressibility are temperature
dependent, the sound velocity is likewise sensitive to temperature changes. For
the computation of the effective parameters  and , two models are compared in this work. (Mauntz,
2010) assumed them to be linear combinations with respect to the volume
fractions  of water (w), monomer (M)
and polymer (Pol):

(Siani et al, 1999) assumed the parameters to be
linear combinations with respect to the volume fractions of the different
phases involved being droplets (d), polymer particles (p) and
water phase (w). They also included three adjustable parameters (j_cr, b, j_MG) in a compressibility model for the polymer particles to take care of
nonlinear effects:

with j_p,M ,j_p,pol as the volume fractions of monomer, respectively
polymer in the particle phase. The adjustable parameters do not have a direct
physical meaning. The idea of introducing  is to summarize effects due to a possible
glass transition and the idea of introducing  is to summarize all other nonlinear
effects.

Experimental Validation

Independent of the chosen approach to model the
ultra-sound velocity, the
experimental procedure to determine its temperature dependency consists of
three major steps:

·        
determine the
ultrasound-temperature calibration curve for the monomer,

·        
determine the
ultrasound-temperature calibration curve for different emulsifier-water mixtures
and fit a data driven response surface model (e.g. a second order polynomial
function),

·        
determine the
ultrasound-temperature calibration curve of the final lattice with known
composition.

With the temperature dependent densities of water,
monomer and polymer taken from literature their compressibilities can be
determined from the recorded calibration curves at each measurement point by
means of Uricks law. While the model of (Mauntz, 2010) requires to fit the
individual compressibility points to a temperature dependency curve, the model
of (Siani etal, 1999) requires in addition the run of a complete polymerization
process to estimate the adjustable parameters by solving an optimization
problem:

For given measurements, the volume fraction of the monomers is
computed by solving an optimization problem that minimizes the difference
between the measured and calculated sound velocity using the conservation of
mass and the known volumes fed up to this point in time:

For some polymerizations both approaches work well (see
Figure 1) while for others only the model of (Siani et al,
1999) represents the data well when the free parameters are readjusted to the
recipe (see Figure 2).

We will demonstrate the use of ultra-sound velocity measurements
in combination with calorimetry to monitor the reaction progress in an emulsion
polymerization.

References

1.     
Gugliotta, L.M. et al,
Estimation of conversion and copolymer composition in semicontinous emulsion
polymerization using calorimetric data, Polymer, Vol. 36, No. 10, 1995

2.     
de Buruaga, I.S. et
al., Nonlinear Control for Maximum Production Rate of Latexes of Well-Defined
Polymer Composition, Ind. Eng. Chem. Res., Vol. 36, 1997

3.     
Vicente, M. et al,
Simultaneous Control of Copolymer Composition and MWD in Emulsion
Copolymerization, AIChe Journal, Vol. 47, No. 7, 2001

4.     
Gesthuisen, R. et al,
Hierarchical Control Scheme for Time-Optimal Operation of Semibatch Emulsion
Polymerizations, Ind. Eng. Chem. Res., Vol 43, 2004

5.     
Sheibat-Othman N. and
Othman, S., Control of an
Emulsion Polymerization Reactor, Ind. Eng. Chem. Res., Vol. 45, 2006

6.     
Dimitratos, J. et al, Control
of Product Composition in Emulsion Copolymerization, Polymer Reaction
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7.     
Urretabizkaia, A. etal,
On-Line Terpolymer Composition Control in Semicontinous Emulsion
Polymerization, AIChe J., Vol. 40, 1994

8.     
Canegallo, S.,
Composition Control in emulsion copolymerization. II. Application to binary and
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9.     
Elizalde, O. et al.
Monitoring Emulsion Polymerization Reactors: Calorimetry Versus Raman
Spectroscopy, Ind. Eng. Chem. Res., Vol 44, 2005

10. 
Santos, J.C. et al,
Comparison of techniques for the determination of conversion during suspension
polymerization reactions, Brazilian Journal of Chemical Engineering, Vol. 25,
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11. 
Urick, R.J., A sound
velocity method for determining the compressibility of finely divided
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12. 
Siani, A. et al,
Procedure for Calibrating an Ultrasonic Sensor for Online Monitoring of
Conversion in Latex Reactors, J. Appl. Polym. Sci, Vol. 72, 1999

13. 
Mauntz, W., A
Contribution to Observation and Time-Optimal Control of Emulsion
Co-Polymerization Reactions, Dissertation, TU Dortmund, 2010