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Online Monitoring of Suspension Polymerization Reactions Using Raman Spectroscopy Juliana C. Santos,† Marlon M. Reis,‡ Ricardo A. F. Machado,† Ariovaldo Bolzan,† Claudia Sayer,‡ Reinaldo Giudici,‡ and Pedro H. H. Arau ´ jo*,† Departamento de Engenharia Quı´mica, Escola Polite´ cnica, Universidade de Sa˜ o Paulo, Caixa Postal 61548, CEP 05424-970, Sa˜ o Paulo, SP, Brazil, and Departamento de Engenharia Quı´mica e Alimentos, Universidade Federal de Santa Catarina, CTC, Caixa Postal 476, CEP 88040-970, Floriano´ polis, SC, Brazil
The main objective of this paper is verifying the feasibility of using Raman spectroscopy for online monitoring of suspension polymerization reactions. Whether the Raman spectra are affected by the particle size distribution (PSD) is also investigated. It is shown that it is possible to estimate the evolution of conversion during suspension polymerization from Raman spectra collected with the probe connected to the reactor window with a short acquisition time. Results also indicate that monitoring by Raman spectroscopy may allow identification of abnormal behavior during suspension polymerization reactions with the formation of unexpected PSD. Furthermore, results suggest that Raman spectroscopy probably has the potential to infer the PSD of suspension polymerizations because the Raman spectrum is affected by the PSD. Introduction Several important commercial resins are manufactured by suspension polymerization as polystyrene resins (ABS, EPS, GPPS, HIPS, and SAN) and poly(vinyl chloride). Suspension polymerization has in principle the conversion, by free-radical polymerization, of monomer(s) droplets dispersed in an aqueous medium by a combination of strong stirring and the use of small amounts of suspending agents into polymer particles. The monomer droplets are slowly converted from a highly mobile liquid state through a sticky stage to hard solid polymer particles. The end-use properties of polymers depend on polymer structures [particle size distribution (PSD), molecular weight distribution, and copolymer composition] and consequently on the reaction that has produced it; thus, the control of suspension polymerization processes is a very important issue. A drawback for the process control of such systems is the relatively scarce techniques implemented in industry for inline evaluation of polymer properties. In such an instance, the development of sensors for online monitoring of polymerization reactors to measure the monomer and polymer concentrations, for example, has become a challenge in the engineering processes.1,2 A few different approaches on monitoring suspension polymerization have been described in the literature such as ultrasonic monitoring,3,4 reaction calorimetry,5-7 near-infrared spectroscopy,8,9 and, recently, Raman spectroscopy.10 With the development of fiber-optic-based techniques, Raman spectroscopy, which is an established technique for the offline polymer characterization, has also become promising for polymerization process monitoring, allowing sophisticated spectroscopic process measurements in otherwise inaccessible environments. Raman spectroscopy has the advantage of providing abundant chemical information, which allows the determination of the concentrations of individual species, for instance, monomer and poly† ‡
Universidade Federal de Santa Catarina. Universidade de Sa˜o Paulo.
mer concentrations.11 In terms of the suspension polymerization process, Raman spectroscopy has as additional advantages weak Raman scattering of water and strong scattering of vinyl bands, common in most of the monomers used in suspension polymerization. Successful results were recently obtained for emulsion polymerization monitoring using Raman spectroscopy.11-15 Nevertheless, suspension polymerization presents several significant differences from emulsion polymerization. The main characteristic that may affect the Raman signal is the PSD that usually ranges from 20 to 3000 µm in suspension polymerization instead of from 20 to 800 nm in emulsion polymerization because it drastically reduces the particle concentration inside the reactor and may increase the heterogeneity of the medium. As mentioned before, the suspension polymerization consists of the conversion of monomer(s) dispersed in the water as droplets into a stable dispersion of polymer particles. The final PSD does not maintain the initial droplet size distribution because the droplets/particles suffer a continuous process of coalescence and breakage along the reaction. Therefore, the PSD changes along the reaction, and it may cause some interference in the Raman signal. According to Vivaldo-Lima et al.,16 the most important issue in the practical operation of suspension polymerization is the control of the final PSD. The major objective of this work is verifying the feasibility of using Raman spectroscopy for online monitoring of suspension polymerization reactions. It is also investigated if the Raman spectra are affected by the PSD of a suspension polymerization because this could open the possibility of monitoring the PSD. Experimental Section Batch styrene (Sty) suspension polymerization reactions were performed with formulations shown in Table 1. All reactants, Sty, benzoyl peroxide (BPO), sodium dodecylbenzene sulfonate (SDBS), poly(vinyl pyrrolidone) (PVP) K90, were used as received. The initial
10.1021/ie034278m CCC: $27.50 © 2004 American Chemical Society Published on Web 08/05/2004
Ind. Eng. Chem. Res., Vol. 43, No. 23, 2004 7283 Table 1. Formulations of Reactions R1-R5 water (g) Sty (g) BPO (g) PVP K90 (g) SDBS (g) temperature (°C) impeller type agitation speed (rpm)
R1
R2
R3
R4
R5
1058.0 460.0 17.2 0.80 0.11 92 ( 1 anchor 450
1058.0 460.0 17.2 0.80 0.22 90 ( 1 anchor 450
1058.0 460.0 17.2 0.80
1058.0 460.0 17.2 0.80
90 ( 1 anchor 450
85 ( 1 anchor 550
525.0 230.0 8.60 0.80 0.24 90 ( 2 propeller 600
charge (water and Sty) was purged with nitrogen during a period of 60 min, and nitrogen feeding to the reactor was maintained during the whole reaction. PVP and SDBS were added to the reactor respectively 15 and 25 min after the reaction had started. Reactions were performed with different reaction temperatures to obtain different polymerization rates. To verify if the Raman signal is affected by the PSD, reactions were performed with different suspension stabilizer concentrations and agitation speeds. Offline characterization was carried out by measuring the evolution of conversion along the reaction by gravimetry. For this purpose, samples were collected throughout the reactions and the reactions were stopped short by the addition of p-benzoquinone and toluene. The PSD of suspension polymerizations can only be determined offline with good accuracy after the particle identity point (PIP). After this point, the viscosity of the polymer particles is too high, so they cannot break up or coalesce, and their diameter remains constant.17 For this reason, only the final PSD was determined by sieving. Raman spectra were collected in a FRA 106/S Fourier transform Raman accessory attached to a Bruker IFS 28/N spectrometer, equipped with a quartz beam splitter. The spectral range comprises equally spaced measurements from 100 to 4000 cm-1 with a resolution of 8 cm-1, and the laser frequency and power are respectively 1064 nm and 450 mW. During Sty suspension, polymerization reaction spectra were collected with 32 scans in order to allow fast measurements, compatible with the dynamics of these batch reactions, and as described in Figure 1, the Raman probe was connected to the reactor window, which is made of glass (15 mm width) and is suitable for reaching an internal reactor pressure of 60 bar. Methodology Light (radiation) scattering can be elastic, without changes in the energy of the scattered light, or inelastic, with loss or gain of energy. The elastic scattering is
Figure 2. (a) Sty spectrum. (b) Area under CdC stretching used to estimate the monomer conversion: the solid line corresponds to the first spectrum of reaction R2 and the dashed line to a spectrum after 118 min of reaction.
known as Rayleigh scattering and the inelastic as Raman scattering. The classical theory of light scattering from molecules describes the molecule-radiation interaction by means of the oscillating dipole moment induced in the molecule by the presence of an incident radiation electric field.18,19 For isotropic samples, such as a liquid, the Raman intensity can be written in terms of the mean polarizability and anisotropy. In general terms, intensities of Raman bands can be expressed by an equation analogous to the Beer-Lambert law, as given by eq 1,18 where Iν is the Raman intensity of band
Iν ) cI0VKν
ν, I0 is the intensity of the exciting radiation, V is the volume of the sample illuminated by the source and viewed by the spectrometer, c is the sample concentration, and Kν is a constant characteristic for each band. In mathematical terms, the intensity of Raman scattering at frequency ν1 is linear with the concentration of the active compound, for example, for sample i of compound A at concentration cA,i, as shown by eq 2,
Iν1,A,i ) cA,iφν1,A
(2)
where φν1,A ) I0VKν1,A is a constant characteristic of compound A at concentration cA,i for the frequency ν1, as given in eq 1. Raman spectroscopy is an attractive method for monitoring polymerization of vinyl monomers because the vinyl group, which disappears during the reaction, is a strong scattering group20,21 due to CdC stretching, as illustrated in Figure 2a. The simplest way to perform quantification with Raman spectroscopy is to build a linear model (or a calibration curve) from a known data set. A linear model corresponding to eq 2 is given by eq 3.
Iν1,A,i(φν1,A)-1 ) cA,i
Figure 1. Illustration of Raman probe connected to the reactor window.
(1)
(3)
In this way, the calibration data set (the data set with known concentrations) is used to calculate (φν1,A)-1 and, for unknown samples, the Raman intensity at the frequency ν1 is measured and used to quantify the
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concentration of the compound A in the unknown sample according to eq 3. Batch homopolymerization processes can also be monitored by direct estimation of the monomer conversion, as shown in eq 4, which is done by taking the Raman intensity corresponding to CdC stretching as shown in eq 5, where xconv is the monomer conversion, cA,t is the monomer concentration at time t, and cA,initial is the monomer concentration at the beginning of the reaction.
xconv ) 1 -
xconv ) 1 -
cA,t
(4)
cA,initial
Iνi,A,t(φνi,A)-1 Iνi,A,initial(φνi,A)
)1-1
Iνi,A,t Iνi,A,initial
(5)
Each Raman spectrum can be represented in terms of eq 6, where Iν1,i, ..., Iνn,i are the Raman intensities at
(Iν1,i Iν2,i ‚‚‚ Iνn,i ) ) cA,i(φν1,A φν2,A ‚‚‚ φνn,A ) (6) frequencies ν1, ..., νn corresponding to the CdC stretching band and φν1,A, ..., φνn,A their characteristic constants of compound A at concentration cA,i for the frequencies ν1, ..., νn. Alternatively, it is also possible to use the area under the CdC stretching band to reduce the influence of noise in such an estimation. This area can be approximated as shown in eq 7 and the conversion estimated as described in eq 8, where Aν1-n,i is an approximation of
(Iν1,i Iν2,i
()
∆ν ∆ν ‚‚‚ Iνn,i ) l ∆ν
)
n×1
cA,i(φν1,i φν2,i
()
∆ν ∆ν ‚‚‚ φνn,i ) l ∆ν
(7) n×1
Aν1-n,i ) cA,iΘν1-n,i the area under the CdC stretching band for spectrum i and Θν1-n,i is a constant corresponding to the band area of a unitary concentration spectrum.
xconv ) 1 - Aν1-n,t/Aν1-n,initial
(8)
This latter procedure was adopted in this work as shown in Figures 2 and 3. These figures show Sty spectra, as well as spectra collected during a suspension polymerization. It should be mentioned that the spectra are normalized with respect to the intensity of the peak at 1002 cm-1 corresponding to the aromatic ring breathing. This procedure is based on the idea that this peak would not vary along the batch as long as the aromatic ring is present in the unreacted monomer as well as in the monomeric units at the polymer chains. Raman spectra collected during the reactions were acquired with a small number of scans to reduce the spectra acquisition time, and thus they present a low signal-to-noise ratio. Therefore, a dynamic filter was applied. The estimated concentration profile was filtered online with a smoothing spline from the first value
Figure 3. (a) Spectra collected during reaction R2: the dots correspond to the CdC stretching band. (b) Area under CdC stretching band (normalized with respect to the aromatic ring breathing peak intensity).
estimated to the actual value. In this case, the filter is applied after 4 min and 15 s of reaction, and the five concentration values estimated from the beginning of the reaction up to 4 min and 15 s are used in the first application of the filter. This procedure is applied successively; in each instant the predicted value from the calibration model is corrected by the smoothing filter, using all of the points available up to the time considered. It is important to emphasize that this filtering procedure was designed for online utilization; thus, no correction is applied backward. The use of this filter leads to a reduction of the variability of the estimation. This smoothing procedure makes it possible to describe noisy experimental data by means of splines without interpolating it exactly.22 Its goal is to obtain a smoothed representation of data that reflects the main trend without being affected by random changes (rapid local variation). This filtered trend can be reached by incorporating some regularization on the experimental data. The method used to obtain such regularization is the measurement of such a rapid local variation by a roughness penalty parameter.23 In this work, the roughness penalty is the integrated squared second derivative introduced into eq 9, where function lλ is minimized to estimate the g function (i.e., description of experimental data in terms of B splines23), which gives the smoothed data vector
lλ(R) )
∑t (at - Rt)2 + λ∫g′′2 dt
r ) [g(t1), g(t2), ..., g(tM)]T
(9) (10)
where at are the experimental data (estimation of conversion from t ) 0 to time t), r denotes a column vector with values calculated by function g(t) on the t corresponding points, and g′′ denotes the second derivative of g(t). The integral term in eq 9 is responsible for
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the curvature of function g(t) (or the rate of exchange between the residual error and the local variation). In this way, changing the values of λ leads to changes in ∫g′′2 dt, and consequently the curvature of g(t) is adjusted. The choice of the penalty parameter must produce a realistic curve that expresses the characteristic evolution of the reaction. In this work, the penalty parameter λ in eq 9 is chosen by using an ordinary leave-one-out cross-validation method.24 This method is based on the principle of leaving the data points out, one at a time, and choosing that value for the desired parameter that results in the best predictions of the missing data points. The smoothing-spline routine used is part of a package for R-Language (www.r-project.org) named “modreg”. Principal component analysis25 (PCA) is applied on the spectral data analysis. It is performed by projecting the original data, described in terms of a matrix, i.e., X, where each row corresponds to a spectrum, into an orthogonal basis set. As a result, the original data are described in terms of new variables, i.e., X ) TPT, where T is the new variable matrix, also known as the matrix of scores, and P is used to project X into T (i.e., XP ) T) and is called the matrix of loadings. The superscript T stands for the matrix transposition operation. Detailed analysis of Raman data by PCA is described by Reis et al.26 To study the feasibility of using Raman spectroscopy for online monitoring of suspension polymerization reactions and the influence of the PSD on the Raman spectrum, two different kinds of analyses were performed: (a) collection of spectra (average of 32 scans in order to allow fast measurements, compatible with the dynamics of batch reactions) with the Raman probe connected to the reactor window; (b) collection of spectra (average of 256 scans in order to improve the signalto-noise ratio) with the probe positioned over the samples, dry solid polystyrene particles produced previously, or connected to a glass flask containing monomer (Sty). Results (a) Online Monitoring. Suspension polymerization presents a high heterogeneity of the reaction medium, which makes its monitoring a challenge. Offline quantification of monomer conversion by gravimetry has in the representative sampling from the reactor its major difficulty. On the other hand, Raman monitoring through the reactor window is not invasive and might take advantage of stirring that can reduce the medium heterogeneity viewed through the reactor window. Parts a and b of Figure 4 show the Raman monitoring of a suspension polymerization (reaction R2) with conversion estimated directly from the spectra and after the application of the filter. In this figure, a good agreement between the Raman estimation and gravimetric data is observed. The gravimetric data were obtained up to 75 min of reaction. After that, representative sampling of the suspended particles became difficult, as was also observed by Kalfas et al.,27 and also, toward the end of some reactions, polymer built up on the sampling device. After the PIP is reached at about 75% of monomer conversion, the polymer particles become hard solid and stable. In Figure 4a, an increase is observed in the variability of the Raman estimation after the PIP. This variability of Raman estimations is due to the hetero-
Figure 4. Evolution of monomer conversion during reaction R2: (a) Raman estimation without a filter (R2); (b) Raman estimation with a smoothing-spline filter (R2); (c) Raman estimation with a smoothing-spline filter (R2). Full squares and triangles denote gravimetric quantifications during reactions R2 and R5, respectively.
geneity of the medium because the area of incidence of the Raman laser is limited and might therefore be a whole polymer particle, a set of particles, or even just the aqueous phase. Before the PIP, particles are very tacky and some of them may adhere for a short period of time on the reactor window, increasing the signal of the Raman spectrum of Sty when these particles cross the laser beam and thus improving estimations of the Sty concentration. Another factor that must also be taken into account is the error in the calculation of the area under the band due to CdC stretching. At high conversions (x > 75%), this area becomes rather small and is, therefore, affected more intensely by the signal noise than at the beginning of the reaction (see Figure 2b). Nevertheless, as shown in Figure 4b, the use of a filter leads to a significant reduction of the variability of the estimation. Figure 4c compares the online conversion estimated from Raman measurements during reaction R2 and the offline gravimetric conversion for reaction R5, showing a fair agreement in the whole range of monomer conversion. Although reaction R5 was carried out in a smaller reactor with a different type of stirrer and with
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Figure 5. Conversion of reactions R1-R4 estimated by Raman scattering with a smoothing-spline filter: (O) reaction R1; (0) reaction R2; (2) reaction R3; (b) reaction R4.
different PVP and SDBS concentrations (see Table 1), it should be mentioned that these changes affect the particle size but do not affect the rate of monomer conversion in suspension polymerization. Figure 5 shows the results of Raman estimations of conversion with a smoothing-spline filter during reactions R1-R4. These reactions were carried out at different temperatures, and higher temperatures led to faster reactions. Because the temperature of reactions R2 and R3 are equal, both reactions presented very similar polymerization rates. Figure 6 shows the PSD of these four reactions. As can be observed, the PSDs of R1, R3, and R4 are very broad. Furthermore, R1 presents a large fraction of particles above 2360 µm and a more heterogeneous PSD. The larger size of the particles might be the cause of the considerable dispersion observed in the estimated conversion of reaction R1 (see Figure 5) because the
Figure 6. Polymer PSD of reactions R1-R4 obtained by sieving.
larger the particle size, the higher the heterogeneity of the reaction medium. These results show that variations in the medium heterogeneity might be detected. To verify whether the distribution of polymer particle sizes affects the Raman spectra, a spectral analysis was performed by PCA. The scores of the first principal component fitted from the data of reaction R2 (i.e., XR2 ) TR2PT, where TR2 represents the scores corresponding to reaction R2 and XR2 corresponds to spectra of reaction R2) were compared with the scores corresponding to reactions R1, R3, and R4 projected into the first principal components of R2 (i.e., XRnP ) TRn, where TRn represents the scores corresponding to reaction Rn and XRn corresponds to spectra of reaction Rn, where n ) 1, 3, and 4). In Figure 7, it is observed that at the beginning of the reaction (until approximately 25 min) the scores present the same profile; after that they start to diverge (this time, 25 min corresponds exactly to the shot addition of SDBS for reactions R1 and R2). It is important to observe that reaction R1, with the largest particles (see Figure 6), also presented the most different profile of the scores. In sequence only the scores of reactions R2 and R3 will be compared. These reactions are very interesting because, although they were carried out at the same temperature but with different amounts of surfactant (see Table 1), these reactions have very similar conversion profiles (see Figure 5) and quite different PSDs (see Figure 6). In reaction R2, the surfactant SDBS was added at 25 min of reaction, and in reaction R3, no SDBS was used. Because this surfactant increases the stability of the particles, the net effect is that it reduces their sizes. Consequently, the PSD of R2 was displaced
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Figure 9. Loading values for the first principal component estimated from data of reaction R2. Figure 7. Score values versus time for the first principal component estimated from the data of reaction R2 projected into the first principal component: (O) reaction R1; (0) reaction R2; (2) reaction R3; (b) reaction R4.
Figure 10. Spectra collected during reaction R2 (solid lines) and reaction R3 (dashed line). All spectra are normalized to have the same intensity for the band corresponding to benzene ring breathing at 1000 cm-1 (spectra are shown with a shift in the baseline to improve visualization).
Figure 8. Score values for the first principal component estimated from the data of reaction R2 projected into the first principal component: (0) reaction R2; (2) reaction R3. (a) Scores versus time. (b) Scores versus conversion.
for lower values when compared to R3. Parts a and b of Figure 8 show the score values for the first principal component estimated from the data of reaction R2 projected into the first principal component versus time and versus conversion. Figure 8a suggests that after 25 min of reaction there is a change in the two reactions, leading to quite different spectra, and this corresponds exactly to the point when the surfactant was added to R2. It must be noticed that this difference is not observed in the conversion of these two reactions, as verified in Figure 5. Figure 9 shows that the loadings corresponding to the first principal component present large negative values of around 500 cm-1. Also, the normalized spectra of reactions R2 and R3 also show that the spectral range between 200 and 600 cm-1 allows the discrimination of these two reactions as shown in Figure 10. After the initial reaction stage, around 25 min, reaction R2, with the smaller particle sizes, shows a more intense scattering than reaction R3 in the low-wavenumber region. These results are very encouraging because the effect of SDBS on the PSD is almost immediate after it has
been added to the reaction medium. This shows that Raman spectroscopy was able to detect when both PSDs began to deviate from each other. (b) Offline Characterization. To confirm the effect of the particle size and PSD on Raman spectra, a series of offline Raman analyses were conducted. In this case, the measurements were made with an average of 256 scans in order to improve the signal-to-noise ratio. The spectra shown in Figure 11a were collected with the probe positioned right over the samples (dry solid polystyrene particles produced previously and Sty monomer). The spectra shown in Figure 11b were collected with the probe placed at the wall of a glass flask containing dry solid polystyrene particles produced previously. The spectrum of the empty glass flask is also presented. Figure 11a compares the spectrum of Sty with the spectra of two different samples of polystyrene particles, one with a small average particle size (P1) and another with a larger average particle size (P2). The PSDs of both samples (P1 and P2) are shown in Figure 12. The spectrum of the sample with the smaller particles has more intense Raman scattering than the sample with the larger particles. On the other hand, it is interesting to observe in Figure 11b that the mixture of the two samples (50/50 wt %) results in a significantly more intense scattering. This figure also shows that the spectral range comprising low wavenumbers (
