In Situ Near-Infrared Spectroscopy for Simultaneous Monitoring of

Jun 2, 2004 - Universidade Federal de Santa Catarina, CTC, Caixa Postal 476, CEP 88040-970, Floriano´polis, SC, Brazil. In this work near-infrared ...
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Ind. Eng. Chem. Res. 2004, 43, 7243-7250

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In Situ Near-Infrared Spectroscopy for Simultaneous Monitoring of Multiple Process Variables in Emulsion Copolymerization Marlon M. Reis,† Pedro H. H. Arau ´ jo,‡ Claudia Sayer,† and Reinaldo Giudici*,† Escola Polite´ cnica, Departamento de Engenharia Quı´mica, 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

In this work near-infrared spectroscopy is used to monitor semicontinuous styrene/butyl acrylate emulsion copolymerization reactions. A set of nine reactions with slightly different formulations were carried out. The results of five reactions were used to fit a SIMPLS model, and the four remaining reactions were monitored in order to evaluate the model performance for estimation of important variables and latex properties such as individual monomer concentrations and the average polymer particle diameter. These variables were used to calculate the online monomer conversion, copolymer composition, particle number, and average number of radicals per polymer particle. Introduction Monitoring emulsion polymerization reactions has been the focus of a wide research field where different successful approaches have been described. Among these approaches, spectroscopic techniques were pointed out as very promising,1-3 and this has been confirmed with several recent results described in the literature.4-10 As advantages, spectroscopic techniques have ease of handling, fast measurements, and multipurpose application. To estimate properties and characterize the reaction medium by spectroscopic methods, a calibration model is required to correlate the spectroscopic measurement (i.e., spectra) with the property (or properties) of interest. Thus, fitting a representative calibration model for the process is the key for a successful spectroscopic monitoring, which, in general, is attained by using samples (spectra) that represent tentatively all variations of the process. In terms of emulsion polymerization reactions, the model fitting is a challenge because of the multiphase medium (polymer particles dispersed in the medium), which affects the spectra. Additionally, there are process variables such as temperature that also affect the spectra and, therefore, have to be taken into account. Near-infrared (NIR) spectroscopy became a useful technique for monitoring a wide range of industrial processes in otherwise inaccessible environments because of the development of optical fiber technology.11,12 In emulsion polymerization, NIR spectroscopy has shown its applicability in estimating the monomer concentration, polymer content, and identification of the presence of monomer droplets in the reaction medium as well as polymer particle size.13-15 The practical implementation of NIR process monitoring goes through different steps from data collection to model fitting and its evaluation. In the former, the process characteristics drive the choice for which data to use in the model fitting. The data (NIR spectra) must represent all variability that affects the NIR spectra in the process range. One major characteristic of batch and * To whom correspondence should be addressed. Tel.: 5511-3091-2254. Fax: 55-11-3813-2380. E-mail: [email protected]. † Universidade de Sa ˜ o Paulo. ‡ Universidade Federal de Santa Catarina.

semicontinuous emulsion polymerization reactions is the variability of the reaction medium: particle nucleation, depletion of micelles and eventually also particle coalescence, variation of monomer or comonomer concentrations, monomer droplets sometimes being present, increasing average particle sizes, and polymer content. One way to collect such data is to perform a set of similar reactions and then use this data set, NIR spectra and data of reference methods, for calibration model fitting. In this case, a problem usually faced is similarity among the reactions that do not produce a wide range of process variability. An alternative is to perform different reactions, which may not be feasible in an industrial reactor. Thus, monitoring by in situ NIR spectroscopy of nine slightly different semicontinuous styrene (Sty)/butyl acrylate (BA) emulsion copolymerizations is described in this work. The major variations among these reactions were different final reaction temperatures and the addition of different amounts of initiator after the end of the monomer feeding period. This reaction set is interesting because it mimics a problem that would be faced on fitting calibration models from historical industrial data because in industrial scale significant process variations would hardly be allowed. It is shown that NIR spectroscopy might be used to estimate not only monomer and polymer concentrations, and therewith conversion and copolymer composition, but also morphological properties such as the average polymer particle diameter. This result is very important because in this way it is possible to calculate online very important emulsion polymerization variables such as the particle number and average number of radicals per polymer particle. Experimental Procedures Polymerization Reactions. A set of nine reactions were performed in semicontinuous conditions with a constant Sty/BA weight ratio (50/50) in the monomer feed stream. Distilated and deionized water and technical-grade reactants, such as monomers [Sty, BA, and acrylic acid (AA)], emulsifier [sodium lauryl sulfate (SLS)], initiator [sodium persulfate (Na2S2O8)], and pH buffer [sodium carbonate (Na2CO3)], were used in the reactions in order to approach industrial conditions. The

10.1021/ie034277u CCC: $27.50 © 2004 American Chemical Society Published on Web 06/02/2004

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Figure 1. Average particle diameter and feeding and temperature profiles of emulsion copolymerization reactions; the first letter in the reaction names stands for calibration C referring to model fitting and M for those reactions used for validation: 0, monomer feed profile (scale factor 900.40 g); O, average particle diameter (scale factor 127.2 nm); 9, initiator feed profile (scale factor 268.72 g); b, temperature (scale factor 90.57 °C). Table 1. Basic Formulation Used in the Semicontinuous Sty/BA Reactions (T ) 70 °C and 200 rpm). Amounts in grams reagent Sty BA AA water SLS Na2S2O8 Na2CO3

initial charge

feed stream 1

feed stream 2

447.00 447.00 4.47 1700.00 9.443

114.80

where Fcopol is the density of the copolymer in grams per cubic centimeter and mMtot and mRtot are the total amounts of monomers and the total charge of the reactor in grams. Average number of radicals per polymer particle: Conversion and particle number data were used to calculate the reaction rate and the average number of radicals per polymer particle (n˜ ).

4.940 4.512

n˜ ) initial charges were purged with nitrogen during a period of 60 min, and nitrogen feeding to the reactor was maintained throughout the reactions. Table 1 shows the basic formulation of the reactions, and Figure 1 describes the average particle diameter and feeding and temperature profiles throughout the reactions (the profiles were normalized to improve the visualization). Latex Characterization. Monomer concentrations: Sty and BA concentrations were determined by gas chromatography (Shimadzu HS-GC 17A) combined with gravimetry. Conversion: Monomer conversion (x) was calculated from gravimetric data. Copolymer composition: The cumulative composition of the copolymer (weight fraction of Sty in the copolymer) was calculated from monomer concentrations measured by gas chromatography. Average particle diameter: Photon correlation spectroscopy (Coulter N4-Plus) was used to measure the average particle diameters (Dp). Particle number: Conversion and average particle diameter data were used to calculate the particle number (Np).

Np )

6x(mMtot/mRtot) πFcopolDp3

(1)

nMtot

∑

dx dt

NA M kpijPi[i]p RtotNp

(2)

i)1,2 j)1,2

Pi )

kpji[i]p kpji[i]p + kpij[j]p

(3)

where nMtot is the number of moles of monomers used during the reactions, NA is the Avogrado’s number, kpij is the propagation rate constant of terminal radical i with monomer j, shown in Table 2, Pi is the relative frequency of radicals presenting a monomeric unit of type i on its active end, and [i]p is the concentration of monomer i in polymer particles calculated by the iterative procedure proposed by Omi et al.16 Because this procedure is rather fast, these equations can also be used for online calculations of n˜ based on NIR spectroscopy measurements (monomer concentrations and average particle sizes). Inline Monitoring: NIR Spectroscopy. NIR spectra were collected in an IFS 28/N Bruker spectrometer, equipped with a quartz beam splitter, using a probe (Hellma 661.622-NIR, with a transflection system with an overall light path equal to 1 mm) immersed in a reaction medium. Each spectrum is an average of four scans with a resolution of 4 cm-1 in a spectral region between 7602 and 4902 cm-1. Two spectra were collected for each interval of 1 min during the reactions.

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Figure 2. Monomer and water spectra (A) and their derivatives (B). The rectangles denoted with a and b describe the spectral regions used on the model fitted for monomer concentration estimation. Table 2. Kinetic Constants and Parameters Used for the Calculation of the Average Number of Radicals per Polymer Particle constant

equation/value

description

ref

kpSty kpBA r1 r2 d KSty d KBA p KSty p KBA

1.8 × 1012 exp[-10400/(1.987T)] 2.73 × 109 exp[-6300/(1.987T)] 0.76 0.19 2618 705 1570 460

Sty propagation constant (cm3/mol‚s) BA propagation constant (cm3/mol‚s) reactivity ratio reactivity ratio partition coefficient of Sty between aqueous and monomer phases partition coefficient of BA between aqueous and monomer phases partition coefficient of Sty between aqueous and polymer phases partition coefficient of BA between aqueous and polymer phases

17 18 19 19 20 20 20 20

Calibration Model Development Monitoring of the emulsion polymerization by NIR requires a calibration model, which has to be fitted from spectra that represent the overall reaction stages. In some cases, the data available from historical reactions do not represent the wide range of variability necessary to fit a representative model. This is exactly the case evaluated in this work, where models were fitted from data of five slightly different reactions, which do therefore not present a wide range of variability, and these models were tested during four new reactions. The multivariate linear model used in this work is the SIMPLS method, based on the partial least squares21 (PLS) method, developed by de Jong.22 Our own routine of SIMPLS has been used on the model fitting; it was implemented in R language (www.r-project.org; in this website there is also a package, named pls.pcr, for PLS and SIMPLS). Table 1 and Figure 1 describe a set of almost similar reactions, especially during the monomer feeding, which results in a narrow range of variability. The major differences among these reactions, as shown in Figure 1, were the temperature profiles and the initiator feed profiles, which resulted in differences in conversion and, especially, in particle size evolution. The data corresponding to reactions for the calibration model fitting were divided into two sets, one for model fitting and another for testing the fitted model (internal test data set). Two models were fitted, one to estimate the

monomer concentrations and another for the average polymer particle diameter. To improve the specific predictions of the monomer concentrations and average particle size, these two models differ in the spectral region used to fit them and in spectral pretreatment. The monomer concentration model used the derivative of the spectra in the spectral regions between 45165095 and 5480-6252 cm-1, as shown in rectangles a and b in Figure 2 with the spectra of monomers and water and of its derivatives, respectively. These spectral regions correspond to those where the monomer absorption is more intense and water absorbs less. The derivative was applied to improve the presence of shoulders in these spectral regions. Because both monomers are fed during the reactions, collinear monomer concentration profiles are obtained during the reactions, which is a problem to be faced in the calibration model fitting. Therefore, in this work, the spectra of the pure monomers and water were included in the set of spectra used to fit the monomer concentration model to improve the range of variability of the data. The resulting SIMPLS model was composed of six latent variables; this number was chosen based on a leave-one-out crossvalidation23 procedure and evaluation of the model with the internal test data set. The cross-validation procedure is based on taking out one sample (spectrum and corresponding property to be predicted) of the calibration data set, fitting the model with the remaining spectra, and finally evaluating the spectrum that was

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Figure 3. Methodology for the evaluation of the model performance.

taken out . This procedure is repeated for all n spectra in the calibration data set; as a result, n models are fitted, and for each one, the spectrum that was taken out has its property predicted. Finally, the model is evaluated by checking the PRESS, the sum of squared differences between the predicted property, by cross validation and their expected values. In this case, the whole procedure is repeated for an increasing number of latent variables (L). The number of latent variables is chosen either when PRESS is a minimum value or when PRESS becomes almost insensible to the changes in L. Once the number of latent variables is chosen by PRESS, a model is fitted and tested with the internal test data set to verify if the number latent variables suggested by the cross validation was a good choice. In the SIMPLS model developed for the average particle diameter, the original spectral information in spectral regions 8267-8913 and 10 475-13 000 cm-1 was used. These spectral regions were chosen by selecting those that resulted in good estimations of the

internal test data set and that also presented regression coefficient vectors distinct for the polymer particle size and polymer content. This was done because these properties have a similar evolution throughout the reactions and they need to be discriminated in the calibration model. Five latent variables were used in the SIMPLS model for the average particle diameter; this number was chosen based on a cross-validation procedure, and the evaluation of the model with the internal test data was five latent variables. Reaction monitoring has, among its goals, the detection of abnormal changes during the reactions, which sometimes drives the reaction to stages not represented by the calibration models. It might also be used to detect failure in probe measurements due to the presence of N2 bubbles (at the beginning of the reaction) or film formation in the optical path toward the end of the reaction.24 In this case, the spectra collected during these reaction periods cannot be used for the estimation of properties because they were not described by the corresponding model. The detection of such situations has been identified by the statistic of Hotteling, T2, and statistic Q.25 Figure 3 describes the outline of the methodology for the evaluation of the model performance. Results and Discussion Figures 4 and 5 show the evolution of respectively the Sty and BA concentrations during the four validation reactions. These concentrations were estimated directly using the calibration developed for these variables as described in the previous section, and a good agreement might be observed between online estimated values and reference method values, indicating that the calibration model is representative of the process. The blank spaces during the NIR monitoring represent spectra that were rejected by the statistics of Hotteling, T2, and Q. In general, those spectra corresponding to the beginning

Figure 4. Evolution of Sty concentrations during validation reactions. Full lozenges denote NIR monitoring. Open squares denote reference measurements (gas chromatography and gravimetry).

Figure 5. Evolution of BA concentrations during validation reactions. Full lozenges denote NIR monitoring. Open squares denote reference measurements (GC and gravimetry).

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Figure 6. Evolution of conversion during validation reactions. Full lozenges denote NIR monitoring. Open squares denote reference measurements (gravimetry).

Figure 7. Evolution of the copolymer composition (weight fraction of Sty) during validation reactions. Full lozenges denote NIR monitoring. Open squares denote reference measurements (gas chromatography and gravimetry).

Figure 8. Evolution of the average particle diameter during validation reactions. Full lozenges denote NIR monitoring. Open squares denote reference measurements (photon correlation spectroscopy).

of these semicontinuous reactions were probably rejected because of the presence of nitrogen bubbles in the probe optical path, due to the relatively low initial level of reactor charge and the presence of foam. Reaction M2 is the only one in the validation data set that had a temperature increase from 70 to 90 °C, around 100 min of reaction; when such a change takes place, a reduced number of spectra were also rejected. Reaction conversion and copolymer composition (weight fraction of Sty in the copolymer; Figures 6 and 7) were calculated online from the monomer concentration estimations, the amount of monomer fed, and the total reactor mass. This was performed to evaluate how the small deviations in monomer concentration estimations would affect the monitoring of these other important process variables. Results show that deviations between variables calculated from NIR estimations and variables calculated from reference method measurements are not significant and therewith indicate that NIR spectroscopy measurements can be used in closed-loop control schemes of emulsion copolymerization reactions. Figure 8 shows the evolution of the average particle diameters during validation reactions. As described in

Figure 9. Spectra of the same final latex (polymer emulsion) at different temperatures. The rectangles denoted with a and b describe the spectral regions used on the model fitted for the average polymer particle diameter.

the Calibration Model Development section, average particle diameters were estimated directly with an independent model that was fitted with spectral regions different from those used for monomer concentration

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Figure 10. Evolution of the particle number during validation reactions. Full lozenges denote NIR monitoring. Open squares denote reference measurements (photon correlation spectroscopy and gravimetry).

Figure 11. Evolution of the average number of radicals per polymer particle during validation reactions. Full lozenges denote NIR monitoring. Open squares denote reference measurements (photon correlation spectroscopy and gravimetry).

estimation. For the majority of the reactions, agreement with the reference method measurements is fairly good. Nevertheless, it is interesting to observe that in reaction M2 all spectra are rejected by statistics T2 and Q after a temperature rise from 70 to 90 °C. This result can be explained by sensitivity toward the temperature of the spectra in the regions used in this calibration model. Figure 9 shows the spectra of a final polymer emulsion taken at three different temperatures (64, 70, and 90 °C), and it might be observed that NIR absorption increases considerably with temperature in the spectral regions, marked with rectangles, selected for the calibration model of the average particle diameter. Figure 10, which shows the evolution of the particle number calculated online based on the monomer conversion and average particle diameter estimations by NIR spectroscopy and on polymer density data, helps to elucidate the reason for the deviations in the estimation of the average particle diameters during reactions M4 (peak between 100 and 120 min and after 160 min) and M1 (peak between 90 and 130 min). First of all, it is very interesting to observe that reaction M3, which presented the best estimation of the average particle diameter in Figure 8, is the only reaction of the validation set that did not present a decrease in the particle number along the reaction. All other validation reactions presented a decrease in the particle number, suggesting particle coalescence, which might be the reason for the deviations in the particle size monitoring. When polymer particle aggregates are formed during emulsion polymerization reactions, these big aggregates might get stuck in the optical path of the NIR immersion probe, resulting in positive deviations in the average particle diameter estimations until these aggregates are released again and NIR estimations return to the correct values of the average particle diameters, as observed during reactions M4 and M1.

Another very important result shown in Figure 10 is the possibility of the online estimation of the particle number along emulsion polymerization reactions. This result is quite motivating because it shows that NIR spectroscopy monitoring can be used for early detection of particle coalescence and also may allow a study in detail of complex phenomena like particle nucleation and particle coalescence mechanisms. The evolution of the average number of radicals per polymer particle, shown in Figure 11, was calculated online based on conversion and particle number estimations using eq 3 described in the Latex Characterization section. On the one hand, this result is very interesting because monitoring the evolution of the average number of radicals per polymer particle during emulsion polymerization reactions may help to evaluate radical entry and exit rates as well as to establish the onset end extent of the gel effect. On the other hand, it was shown that particle size estimation used in the online calculation of the average number of radicals per polymer particle is affected by the reaction temperature and local particle aggregation. Therefore, to estimate precisely the average number of radicals per polymer particle, particle aggregation must be avoided and either reactions must be carried out isothermally or, if possible, a more robust model must be developed for the estimation of the average particle diameters. Additionally, this direct calculation of the average number of radicals is very sensitive to small variations in the average particle diameter (n˜ ∝ Dp3) and in conversion (n˜ ∝ dx/dt). To make this estimation more robust, state estimators could be applied.26-29 Figure 12 shows the projection of spectra for both data sets, model fitting reactions, and monitored reactions, into the space of principal components (PCs)25 used in the statistics of Hotteling. The PCs in Figure 12 were fitted only with spectra of calibration reactions. In

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Figure 12. PC analysis of spectra used in model fitting and of spectra of monitored reactions. The arrow in part a indicates reaction M1, and the numbers in part b are the time of reaction M4.

Figure 12a, it is possible to notice that reaction M1, denoted by an arrow, is quite different from the others, especially in the first two PCs. Figure 12b shows that samples corresponding to times of 100, 110, and 120 min of reaction M4 are quite different from the other samples corresponding to the same reaction; it must be noticed that these three samples represent the period of failure in the particle size monitoring (Figure 8). The PC analysis described in Figure 12 confirms that some periods of reactions M1 and M4 are not represented in the calibration model range and also suggest that this problem would be identified graphically during the reaction monitoring. The results described in this work show a correlation between the average polymer particle size and NIR spectra, which had also been noticed in a previous work.15 It also has been verified that coalescence affects the estimation of the polymer particle size. Otherwise, it is not fully understood how the polymer particle size affects NIR spectra. NIR spectroscopy is based on light absorption due to molecular vibrational transitions; basically, a light beam goes through the sample, part of it is absorbed, and the other is transmitted. The logarithm of the ratio between intensities of transmitted light and incident light gives the absorbance used on property estimation. Because the reactor medium is isotropic and the polymer is amorphous, the effects of the polymer particle size distribution over NIR spectra might be due to the physical characteristics of the latex. Effects related to elastic light scattering play an important role in such a process. The transmitted part of the light intensity detected in the NIR spectra contains information similar to those detected in other methods of particle size measurements, such as turbidimetry, and covers a wide range of wavelengths. Work is underway to obtain a more theoretical explanation on the observed relationship between the particle size and NIR spectra. Conclusions A realistic situation on implementing NIR spectroscopy for monitoring emulsion polymerization was discussed in this work. The data set, in some way, mimics

an industrial behavior where frequently the historical data available for model fitting are very similar among the forming reactions, not providing a wide range of variability. Data from nine reactions was divided into two data sets: one with data from five reaction, which was used for calibration model fitting, and another derived from four slightly different reactions used as the test data set. Two models were fitted, one for prediction of individual monomer concentrations and another for the average polymer particle size. It was shown that simultaneous monitoring of different process variables, monomer concentrations (conversion and copolymer composition), and average particle diameters (particle number and average number of radicals per polymer particle) was performed with success, although the calibration model for polymer particle size estimation was more susceptible to failure because of deviations of reactions from the behavior described by the corresponding model (temperature variations and particle aggregation). Finally, the estimation of process variables combined with online NIR probe fault detection (i.e., presence of N2 bubbles and polymer film formation in the optical path) procedures opens the possibility of using NIR estimations in closed-loop control schemes of polymer and latex properties such as copolymer composition and particle sizes. Also, the fast and frequent estimation of the particle number and average number of radicals per polymer particle may allow one to study in detail in situ complex phenomena like particle nucleation and coalescence, radical entry, and desorption. Acknowledgment The financial support from CNPq and FAPESP, a PROFIX fellowship of CNPq-Brazil for P.H.H.A. and a FAPESP fellowship for M.M.R. (Grant 01/13017-1), is gratefully appreciated. The authors also thank Raisio Chemicals for supplying monomers. Literature Cited (1) Chien, D. C. H.; Penlidis, A. On-line sensors for polymerization reactors. J. Macromol. Sci., Rev. Macromol. Chem. Phys. 1990, C30 (1), 1-42.

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Received for review November 28, 2003 Revised manuscript received April 28, 2004 Accepted May 3, 2004 IE034277U