monitoring of high solids content starved-semi-batch emulsion copolymerization reactions by fourier...

1270 Volume 59, Number 10, 2005 APPLIED SPECTROSCOPY0003-7028 / 05 / 5910-1270$2.00 / 0q 2005 Society for Applied Spectroscopy

Monitoring of High Solids Content Starved-Semi-batchEmulsion Copolymerization Reactions by Fourier TransformRaman Spectroscopy

OIHANA ELIZALDE,* JOSE M. ASUA, and JOSE R. LEIZA†Institute for Polymer Materials, POLYMAT, and Grupo de Ingenierıa Quımica, Departamento de Quımica Aplicada, Facultad deCiencias Quımicas, The University of the Basque Country, Apdo, 1072, 20080 Donostia-San Sebastian, Spain

A high solids content n-butyl acrylate/methyl methacrylate emulsioncopolymerization process carried out under starved semi-batch con-ditions was for the first time monitored on-line by means of Fouriertransform (FT)-Raman spectroscopy. Partial least squares regres-sion was employed to build calibration models that allowed relatingthe spectra with solids content (overall conversion), free amounts ofboth n-butyl acrylate (n-BA) and methyl methacrylate (MMA), andcumulative copolymer composition. In spite of the heterogeneousnature of the polymerization, the similarities of the spectra forMMA, n-BA, and for the copolymer, and the low monomer concen-trations in the reactor, the FT-Raman spectroscopy has been shownto be a suitable noninvasive sensor to accurately monitor the pro-cess. Therefore, it is well suited for on-line control of all-acrylicpolymerization systems.

Index Headings: Raman spectroscopy; Partial least squares; PLS;On-line monitoring; Emulsion polymerization; Acrylic monomers.

INTRODUCTION

Emulsion polymers are products-by-process, whichmeans that the final product properties and quality aredefined during the polymerization. Therefore, efficientpolymerization process control is needed to producehigh-performance emulsion polymers. This requires theavailability of on-line measurements. An adequate on-line sensor must be able to provide continuous (or at leastfrequent) measurements, must be accurate enough, andmust operate over long periods of time in environmentsthat are often physically and chemically aggressive.

The difficulties in controlling emulsion polymeriza-tions are in many cases associated with the lack of effi-cient and robust on-line measurements of the molecularproperties.1,2 There are very few instruments and tech-niques to evaluate on-line the polymer properties, andthese usually present problems due to long measurementdelays and poor reliability.

Spectroscopic techniques using optical fibers that allowin situ measurements are very promising in this aspect,because they directly provide chemical information andthey eliminate the necessity of withdrawing samples fromthe reaction medium. The most frequently used tech-niques are absorption spectroscopies (mainly IR (infra-red), NIR (near-infrared), and MIR (mid-infrared)), andemission spectroscopies (fluorescence and Raman).3–11

Raman spectroscopy presents several advantages com-pared to the absorption techniques (mainly NIR and

Received 23 March 2005; accepted 28 July 2005.* Current address: BASF AG Ludwigshafen (Germany).† Author to whom correspondence should be sent. E-mail: jrleiza@

sq.ehu.es.

MIR).3,12–14 Some of these advantages are related to thespectroscopic information and others to the instrumenta-tion itself. Regarding the former, the main advantages are:(1) the Raman spectrum of water (which is the majorcomponent in the reaction mixture) is very weak, andtherefore water does not interfere with the spectra of theother compounds; and (2) contrary to what happens inabsorption processes, some important functional groupssuch as the carbon–carbon double bonds provide strongsignals in Raman spectroscopy.

Raman spectroscopy has been used to monitor poly-merizations and to characterize different polymeric ma-terials. Gulari et al.15 used Raman spectroscopy to mon-itor methyl methacrylate (MMA) and styrene (Sty) bulkhomopolymerizations. Clarkson et al.16 studied the bulkhomopolymerizations of several acrylic monomers. Ra-man scattering was also used to monitor solution homo-polymerizations of MMA17 and copolymerizations of sty-rene and n-butyl acrylate (n-BA).10 Van den Brink et al.18

reported the use of Raman spectroscopy for the monitor-ing and control of solution polymerization of n-BA indioxane. Pepers19 reported the use of Raman spectroscopyfor the monitoring and control of several solution homo-and copolymerizations.

However, only a few cases of use of Raman spectros-copy in emulsion copolymerization systems have beenreported. Most of these studies were focused on moni-toring emulsion homopolymerizations,4,7,19,20 likely be-cause it is relatively easy to follow the changes in inten-sity of the band corresponding to the (C5C) bond.

Co-terpolymerizations are still straightforward formonitoring the overall conversion studying the intensitychanges of the vinylic double bond. However, it is ofmajor interest to calculate the individual conversion ofeach monomer from the spectrum of the sample. Thistask is relatively simple if an independent band for eachmonomer is available.9,19,21,22 However, for many como-nomer systems of industrial interest, such those includingonly acrylic monomers in the formulations (used for theproduction of coatings and adhesives), the bands of themonomers appear at the same frequencies. Ellis et al.23

and Claybourn et al.24 monitored copolymerizations ofcommercial acrylic mixtures (n-butyl acrylate, methylmethacrylate, and allyl acrylate), but in both cases onlythe overall conversion was monitored. Everall et al.6 mea-sured the copolymer composition of several preformedpoly(acrylates). Pepers19 studied the MMA/n-BA emul-sion copolymerization starting from a polystyrene seed inorder to have an internal standard in the spectra. Reis et

APPLIED SPECTROSCOPY 1271

FIG. 1. Spectra of copolymer latexes (BA-co-MMA) containing 30%monomer (n-BA/MMA) at different monomer ratios as indicated in thelegend. Spectra were taken using a laser power of 500 mW, a resolutionof 8 cm21, at room temperature, and accumulating 50 scans.

TABLE I. Formulation of the seeds used to prepare the differentsemi-batch reactions.

Ingredient (g) 50/50 70/30 90/10

n-BAMMAH2OK2S2O8

NaHCO3

DowfaxFinal SC

6753

80.930.1

10860.40.44.8

10%

110.49.6

al.25–27 used Raman spectroscopy to monitor severalemulsion copolymerization reactions and in some casescompared the results with NIR spectroscopy.25 Theyclaimed that Raman spectroscopy can be used to monitorparticle size in emulsion polymerization reactions.26 Inaddition, they assessed different partial least squares re-gression (PLS-R) calibration models to monitor the con-centration of monomers in a vinyl acetate/n-butyl acrylateemulsion copolymerization system.27 This monomer sys-tem is challenging because the bands corresponding tothe double bonds are completely overlapped as in all-acrylic formulations. They made the calibration modelsusing synthetic latex samples and samples taken fromsimilar reactions and found that both types of modelsallowed a reasonable prediction of the monomer concen-trations. However, the monomer concentrations moni-tored in their experiments were significantly larger thanthose typical of a commercial semi-continuous emulsionpolymerization of acrylic monomers.

In this work, the semi-batch emulsion copolymeriza-tion n-BA/MMA carried out under industrial conditions(high solids content, starved conditions) was monitoredby in-line Fourier transform (FT)-Raman spectroscopy.The spectra were taken from a polymerization reactor us-ing a dip-probe. The article is organized as follows. First,a brief introduction about the quantitative analysis of Ra-man spectra is presented. Afterwards, the experimentaldetails related to the reactions that were carried out andthe Raman spectra acquisition is given. Thus, calibrationmodels for the different properties are developed. Next,the calibration models are experimentally validated. Fi-nally, the main conclusions of the work are presented.

RAMAN SPECTROSCOPY

Quantitative Analysis of Raman Spectra. The objec-tive of the quantitative spectral analysis is to predict theconcentrations of the components of interest (called Yvariables in this article) from the spectral data (X vari-ables). In this particular study, the objective is to predictthe overall conversion (solids content, SC), the freeamounts of the monomers, and the cumulative copolymercomposition from Raman spectra acquired during seededsemi-batch emulsion copolymerization of n-BA/MMA.The Raman spectral data for this monomer system arecomplex, and no separate bands for the monomers can

be observed due to the similarity of the chemical struc-ture of both monomers and the copolymer (see Fig. 1,where the spectra of samples containing a n-BA/MMAcopolymer latex with different ratios of n-BA/MMAmonomers are displayed). Therefore, it is not possible toapply univariate regression techniques for all-acrylic co-polymerizations. Consequently, it is necessary to use che-mometric28–32 methods to extract the property of interestfrom the information contained in the Raman spectra.

Calibration Data. The selection of the calibrationsamples is a key point for a successful quantitative anal-ysis, especially when the number of ingredients in thesample is high. In order to achieve accurate predictions,the calibration data must be representative of the processto be monitored. This implies that the latex used to buildthe calibration models should be as similar as possible tothe latex contained in the reactor during the polymeri-zation reaction and include variations in factors thatchange during the reactions and have an influence on theRaman measurements. For model construction, real pro-cess samples or synthetically prepared laboratory samplescan be employed.

Most of the works reported in the literature to monitoremulsion polymerizations used real process sam-ples.9,10,18,19,25,26 In this work process data from seededsemi-batch emulsion polymerizations of n-BA/MMAwere used to build the calibration models.

EXPERIMENTAL

Polymerization Reactions. Deionized water was usedin all polymerizations. All reactants, monomers (n-BAand MMA, Quimidroga), emulsifier (Dowfax), initiator(K2S2O8, Fluka), and buffer (NaHCO3, Panreac) wereused as supplied. Seeded emulsion copolymerization re-actions were used to calibrate and validate the PLS mod-els. In some cases, the seeds were prepared in situ, andin other cases they were prepared previously and addedin the initial charge. For this last case, seeds of differentn-BA/MMA molar ratios (50/50, 70/30, 90/10) were pre-pared using the formulation in Table I. For all seeds, thefinal solids content was 10 wt %. The reactions werecarried out at 80 8C using a turbine type impeller and astirring speed of 400 rpm. After reaction, the seed waskept overnight at 90 8C to ensure that all the initiator wasconsumed.

Table II summarizes the seven semi-batch reactionsthat were used for constructing the PLS models (Cal 1–7) and that used to validate them (Val 1). Table III con-tains the formulation employed. In reactions Cal 1–3, theseed was prepared in situ, and once the monomer used

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TABLE II. Summary of the semi-batch reactions used for model construction and validation.

Run n-BA/MMA K2S2O8 (g) NaHCO3 (g) tfeed (min) Seed (n-BA/MMA)

Cal 1Cal 2Cal 3Cal 4Cal 5Cal 6Cal 7Val 1

50/5050/5050/5050/5050/5070/3090/1050/50

11121221

11121221

180180180180120180180180

In situ (50/50)In situ (50/50)In situ (50/50)Initial charge (50/50)Initial charge (50/50)Initial charge (70/30)Initial charge (90/10)Initial charge (50/50)

TABLE III. Formulation of the n-BA/MMA emulsion copolymer-izations employed in the calibration reactions Cal 1–7 and the val-idation reaction Val 1.

IngredientInitial charge

(g) Stream 1 (g) Stream 2 (g)

n-BA 1 MMAn-BA/MMAH2OSeedK2S2O8

NaHCO3

DowfaxT (8C)

——

61763

variablea

variablea

—70

510variableb

——————

——

32.55———9.45—

a See Table II.b 50/50, 70/30, and 90/10.

to form the seed was consumed the reaction was carriedout according to the formulation in Table III. In reactionsCal 4–7 and Val 1, the seed was prepared previously,using the formulation in Table I, and then added to theinitial charge. Reactions Cal 6 and 7 were carried outwith 70/30 and 90/10 n-BA/MMA feed compositions, re-spectively. Nitrogen was fed continuously during the re-actions to keep an inert atmosphere.

Reactor Setup. The experimental setup employed torun the reactions was a commercial calorimetric reactor(RC1, Mettler-Toledo) equipped with a stainless steelHP60 reactor. The reactor was also equipped with a Ra-man dip-probe that was directly immersed in the reactionmixture, making possible in situ measurements along thereaction. The spectrometer used in the experiments wasa near-infrared Fourier transform Raman spectrometer(RFS 100/S, Bruker) equipped with a 1064 nm wave-length Nd:YAG laser, with a maximum power of 1.5 W.The spectral coverage is from 50 to 3500 cm21, corre-sponding to the Stokes interval. The spectrometer uses agermanium detector (D481-TU), optimized for FT-Ra-man measurements, that is refrigerated with liquid nitro-gen. The Raman dip-probe is connected to the equipmentthrough two optical fibers (5 meters long), one to carrythe laser signal to the reaction mixture, and the other tocarry the scattered radiation back to the spectrometer.

Acquisition and Processing of Raman Spectra. Ra-man spectra were collected from the start of the reaction,at regular intervals of 10 min. Each spectrum consistedof the accumulation of 200 scans, and it was taken witha spectral resolution of 4 cm21, using a laser power of 1W. Considering the time required to switch the laser onand off before and after each measurement (due to se-curity reasons) and the data acquisition time, the timerequired to get each Raman measurement was approxi-

mately 4 min. A resolution of 4 cm21 was found to beadequate for good peak resolution and high signal-to-noise (S/N) ratio for most condensed-phase samples. Ahigher resolution would give noisier spectra and increasethe acquisition time, while there would be no improve-ment in peak assignment and measurement accuracycompared to the lower resolution spectra.31 For fast re-action kinetics a smaller number of scans should be usedin order to capture the faster changes occurring duringthe reaction. Reis et al.27 have shown that even when lownumbers of scans are collected, the prediction of themonomer concentration might still be reasonable. Nev-ertheless, in the present case, the kinetics of the processallowed us to take a relatively large number of scans.

The OPUS software (supplied with the instrument byBruker) was used for data acquisition and preprocessing.Figure 2a shows the spectra evolution along one of thereactions, Fig. 2b contains a single spectrum acquired atthe middle of the reaction with the main Raman bandsmarked, and Fig. 2c shows in more detail the evolutionof the carbonyl (1745 cm21) and vinyl (1640 cm21) dou-ble bond band region. During the acquisition of each Ra-man spectra, a sample was withdrawn from the reactionmixture (after a Raman acquisition time of 2 min) foroff-line analysis in order to characterize the calibrationand validation samples.

Due to fluctuations of the Raman intensities (mainlybecause of laser power and temperature variation) the useof absolute Raman intensities is not recommended, andhence a spectra pretreatment stage is usually necessaryto make the results immune to instrumental variations andobtain the spectra in the most appropriate way for thequantitative analysis.32 Normalization to unit area,32 firstand second derivatives of the spectra, and smoothing (4thand 8th order Savitzky–Golay filtering to the normalizedspectra) were applied to the spectra prior to quantitativeanalysis.

Off-Line Measurements/Reference Methods. SolidsContent. The conversion of monomer to polymer wasdetermined by gravimetric analysis as follows:

Polymer (t)Conversion(t) 5 (1)

TotalpMonomerpRecipe

For this analysis, immediately after collecting the sam-ples, the reaction was quenched by addition of an inhib-itor (hydroquinone).

Residual Monomer and Cumulative Copolymer Com-position. The amount of residual monomer in the reactorwas measured by means of gas chromatography (GC) andthe cumulative molar composition of the copolymer was


FIG. 2. (a) Evolution of the Raman spectra during a semi-batch n-BA/MMA copolymerization reaction; (b) detail of one of the spectra with themain Raman bands; and (c) detail of the evolution of the C5O and C5C bands.

1274 Volume 59, Number 10, 2005

FIG. 3. Calibration data used for modeling the SC, unreacted n-BAand MMA weight fractions, and the cumulative composition. The cross-es represent the same data organized in ascending order.

FIG. 4. Evolution of the instantaneous conversions of n-BA and MMAduring reaction Cal 4. Feeding time 5 180 min in both reactions.

calculated from this measurement. A gas chromatograph(Shimadzu GC-14A) with a flame ionization detector(FID) and an integrator (Shimadzu C-R6A) was used inthis work. A calibration model for the MMA/(MMA 1 n-

BA) ratio was constructed from the GC chromatograms.This measurement was coupled with the gravimetry dataand material balances to compute the cumulative copol-ymer composition.

Calibration Data. Figure 3 shows the calibration dataused to build the models for the SC, n-BA, and MMAweight fractions and the cumulative copolymer compo-sition. It can be seen that for solids content and n-BAand MMA concentrations, the property ranges were ho-mogeneously covered. For the cumulative composition(Fig. 3b) the range was not homogeneously covered,since discontinuities were apparent between the differentfeed compositions. Furthermore, as the amount of MMAin the formulation decreased, the composition change inthat specific reaction narrowed. Note, however, that forthe 50/50 composition reactions the range was well cov-ered. Two sets of calibration models were built: onebased on the 50/50 composition data (runs Cal 1–5) andthe second for the reactions including different feed com-positions (runs Cal 4–7).

Figure 3 also shows that n-BA was present in the re-actor in a larger amount, and MMA was in all cases thefirst monomer to be consumed during the reaction. Figure4 shows the evolution of the instantaneous conversionsfor n-BA and MMA during reaction Cal 4. It can be seenthat MMA concentration was very low because MMA isthe most reactive monomer. A similar behavior was ob-served in the other reactions. This made very challengingthe prediction of the MMA concentration from Ramanspectra.

An important point that should be considered is theinfluence of the solids content (polymer content) in theprediction of unreacted monomer fractions. Significantpolymer interference in the detection of residual mono-mers can be expected because the turbidity (caused bythe polymer latex particles) changes drastically during thereaction, due to the increase of the concentration of poly-mer particles in the reaction medium along the reaction.For this reason, calibration procedures must take polymercontent into account. In order to minimize the interfer-ence of the polymer in the evaluation of monomer con-centrations, calibration samples with similar amounts ofMMA and/or n-BA, but very different polymer contents,were included in the calibration set.


TABLE IV. Results from the different PLS calibration models (Model 1).

Property Sample set Region (cm21) Pretreatment No. LV RMSEV

SC 123456

Cal 1–5Cal 1–5Cal 1–5Cal 1–5Cal 1–5Cal 1–5

Full spectrumFull spectrumFull spectrumFull spectrumFull spectrum

3500–2800

Norm. unit areaNorm. 1 Smooth4Norm. 1 Smooth8Norm. 1 1st deriv.Norm. 1 2nd deriv.Norm. unit area

333322

0.7120.7040.7142.0694.0541.7

Comp 1234567

Cal 1–5Cal 1–5Cal 1–5Cal 1–5Cal 1–5Cal 1–5Cal 1–5


3500–28001400–700

Norm. unit areaNorm. 1 Smooth4Norm. 1 Smooth8Norm. 1 1st deriv.Norm. 1 2nd deriv.Norm. unit areaNorm. unit area

3333232

0.8340.8290.8270.8961.2691.2031.717

MMA frac. 12345678

Cal 1–5Cal 1–5Cal 1–5Cal 1–5Cal 1–5Cal 1–5Cal 1–5Cal 1–5


1800–7001800–11001100–700

Norm. unit areaNorm. 1 Smooth4Norm. 1 Smooth8Norm. 1 1st deriv.Norm. 1 2nd deriv.Norm. unit areaNorm. unit areaNorm. unit area

33332322

5.354 3 1024

5.477 3 1024

5.411 3 1024

6.633 3 1024

9.864 3 1024

7.355 3 1024

7.809 3 1024

1.298 3 1023

BA frac. 12345678

Cal 1–5Cal 1–5Cal 1–5Cal 1–5Cal 1–5Cal 1–5Cal 1–5Cal 1–5


1800–7001800–11001100–700

Norm. unit areaNorm. 1 Smooth4Norm. 1 Smooth8Norm. 1 1st deriv.Norm. 1 2nd deriv.Norm. unit areaNorm. unit areaNorm. unit area

44433332

4.703 3 1023

4.945 3 1023

4.769 3 1023

6.113 3 1023

9.312 3 1023

6.505 3 1023

7.24 3 1023

1.153 3 1022

TABLE V. Number of calibration samples employed, optimalnumber of latent variables (LVs) required for each property, andRMSEV values for PLS Models 1 and 2.

PropertyCalibration

samplesNumberof LVs RMSEV

Cal 1–5 reactions (Model 1)SCCum. Compositionn-BA fraction (wt %)MMA fraction (wt %)

10610610199

3343

0.7120.834

4.703 3 1023

5.354 3 1024

Cal 4–7 reactions (Model 2)SCCum. Compositionn-BA fraction (wt %)MMA fraction (wt %)

95959595

3345

1.3424.453

4.455 3 1023

7.497 3 1024

CALIBRATION MODEL CONSTRUCTION

Partial least squares (PLS) analysis were performed us-ing The Unscrambler software (version 7.6, CAMO ASA,Norway). All data (Raman spectra as well as individualamounts) were mean-centered and afterwards PLS mod-els were formed using the solids content, n-BA molarcumulative composition, and n-BA and MMA weightfractions as calibration data. Separate models were con-structed for each property.

Partial Least Squares Models for n-BA/MMA 5 50/50 Composition Reactions (Model 1). The calibrationmodels for the different properties are based on a set offive semi-batch reactions (Cal 1–5), carried out with a50/50 molar composition of the feed. PLS models werebuilt using different spectra pretreatment techniques: firstand second derivatives of the spectra, smoothed spectra,and considering only the spectral regions that contain

specific information on the target property. All the cali-bration models were validated by a full cross-validation(CV), and the results are summarized in Table IV. Thequality of the models is given by the root mean squareerror of cross-validation (RMSEV), which corresponds tothe optimal number of latent variables (LVs). RMSEVvalues are a pragmatic approximation to RMSEP values(root mean square error of prediction), especially whenthe data collection is time consuming, as in this casewhere polymerization reactions and two different analyt-ical techniques are needed to collect the data.

A low value of the RMSEV indicates a high accuracyon the prediction of the specific property. Since RMSEVis given in the units of the property that is being pre-dicted, it is not possible to directly compare the RMSEVvalues of properties having different units. Table IVshows that for each property, the models built with spec-tra pretreatment were, in the best case, similar to thoseobtained with only normalized spectra. Therefore, themodels built up with the complete normalized spectrawill be shown in this work. The optimal number of LVs,the number of calibration samples used in each case, andthe RMSEV corresponding to the optimal number of LVsare summarized in Table V. For the SC, cumulative com-position and MMA fraction, models with three LVs wererequired and for the case of n-BA fraction four LVs wererequired. This may be due to the fact that the Ramansignals given by MMA are much more intense than theones given by n-BA.33 This is very convenient in thiscase, because MMA was present in a very low concen-tration.

Table VI shows the explained variance for X (Ramanspectra) and Y (property) for different numbers of LVs.

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TABLE VI. The percentage of X (Raman) and Y (target property)variables explained by each LV for calibration Models 1 and 2.

Property

Model 1

LVs X (%) Y (%)

Model 2

LVs X (%) Y (%)

SC 1234

90110

96210

123—

8213

1—

8710

2—

Composition 1234

90140

3654

30

123—

7817

1—

4646

4—

n-BA 12345

893300

46523

32

1234—

85691—

5502219—

MMA 1234—

89420—

113842

4—

12345

7916

111

352814

86

FIG. 5. Plots for the predicted versus measured values for the SC,copolymer composition, MMA fraction, and n-BA fraction for the dataused to build the models (Model 1).

The explained variance (indicated as a percentage) is ameasure of the proportion of variation in the data ac-counted for by the current LV. For example, in the modelfor the SC, the first LV was able to explain 90% of thevariation in the spectra and 96% of the variation in theSC data. The second and third LVs still add some smallinformation, but nothing is gained with the fourth LV.Therefore, for this property a model with only three LVswas considered to be the optimum. The analysis for theother properties can be done in a similar way. Whereasthe trend is similar for the variance in the spectra, sig-nificant differences were observed for the variance in theprediction of the property with the number of LVs in themodel; namely that the second and third LVs did con-tribute significantly in the variance of the property. Theplot for the RMSEV of n-BA (not shown) gave a mini-mum value for the model having four LVs, but as can beobserved in Table VI, the explained variance did notchange much by using three or four LVs. It was decidedto use the criterion of the minimum RMSEV, and there-fore, the model with four LVs was chosen. The scoresand loading plots confirmed the trends and data of TableVI. For the sake of brevity they are not included here,but are available elsewhere.33,34

Figure 5 compares the predicted with measured valuesof the four target properties for the calibration samples.The larger scattering of the data corresponding to themonomer fractions in comparison to the SC was mainlydue to the fact that no clear separate bands were observedfor each monomer and to the very low monomer amountsin the reactor. It is also important to point out that nointernal standard was available to construct the PLS cal-ibration models.

Partial Least Squares Models from Different FeedComposition Reactions (Model 2). As in the previoussection, PLS calibration models were built to predict theSC, cumulative n-BA composition, and n-BA and MMAweight fractions, but in this case, reactions with differentfeed compositions were used in the calibration set (re-actions Cal 4–7). The procedure was similar to the oneexplained in the previous section, and therefore only the


FIG. 6. Plots for the predicted versus measured values for the copolymer composition (left) and MMA fraction (right) for the data used to buildthe models (Model 2).

models using the complete normalized spectra will beshown. Table V presents a summary of the calibrationmodels. In order to give a similar weight to all the feedcompositions in the calibration models, it was decided toinclude only two of the 50/50 feed composition reactions(Cal 4 and 5).

Comparison of these results with those correspondingto the set of reactions Cal 1–5 shows that the RMSEVvalues were in general larger when reactions with differ-ent feed compositions were included in the calibrationdata. In the case of the copolymer composition, the rea-son for the increase in the RMSEV could be that theexperimental range was larger (40–90% versus 40–52%for the reactions with constant feed composition). An ad-ditional reason that might cause the increase in theRMSEV was the fact that composition data were not ho-mogeneously distributed over the experimental range (seeFig. 3). In the case of n-BA, the RMSEV obtained wasvery similar to that of Model 1, even slightly lower. Re-actions Cal 6 and 7 were richer in BA and this fact mighthelp the PLS model to extract data about the BA fraction.The opposite happened with MMA, which was presentin lower concentrations in reactions Cal 6 and 7. Theincrease in the RMSEV and the requirement of additionalLVs may be due to the very low MMA contents thatmade the data extraction from the spectra more difficult,since the spectral changes related to MMA were smaller.

Table VI contains the explained percentages for X (Ra-man data) and Y (target property) by each LV. The valuesare similar to those of the Model 1, with a slight effectof the different feed compositions that affected the dataextraction introducing some variability in the percentag-es.

It is worth noting that RMSEV values for n-BA andMMA predictions in Tables V and VI might seem to below, but once they are compared with the actual meanconcentrations they accounted for an error of about 10%.This error is reasonable, if we take into account that un-der the same conditions reaction calorimetry, anothernoninvasive monitoring technique used in emulsion po-lymerization reactors, can lead to deviations higher than20%.35

The predicted vs. measured values for n-BA and theSC were very similar to those obtained in calibrationswith only 50/50 composition reactions and hence onlythe plots for the copolymer composition and MMA areshown in Fig. 6. The negative values for some points in

the MMA prediction indicate that the concentration isvery low, close to zero, and therefore they should betruncated to zero. The predicted vs. measured plot for thecopolymer composition reflects very well the non-conti-nuity of the calibration data in the calibration data range,and this may affect the calibration model performance.The predictions were poor for the 90/10 reaction (Cal 7),and this could be due to the interference of the SC. Thelowest composition predicted by the model for the 90/10composition (about 70%) corresponded to the first samplein the reaction, namely, the lowest SC. Then, the pre-dicted composition data were organized in ascending SCvalues. In this reaction the MMA content was low, butmore importantly, the composition drift was very small,between 87 and 90. Thus, very small spectral changeswere caused by the copolymer composition. Therefore, itseems reasonable to relate the observed trend in the pre-dicted versus measured plot for the composition to theSC interference.

EXPERIMENTAL VALIDATION OF THEMODELS

In order to experimentally check the model perfor-mance, a reaction, totally independent from the calibra-tion data set (Cal 1–7), was carried out (Val 1). The for-mulation used in this reaction is shown in Table III. Thesolids content was 50 wt % and the monomer molar com-position 50/50, and different feed composition. For thisvalidation reaction, the monomers and an emulsifier so-lution were fed into the reactor during 3 h at a constantflow rate. Spectra were recorded every 10 min, and dur-ing the time required for spectral acquisition (4 min) sam-ples were withdrawn from the reactor for off-line anal-ysis.

Figure 7 shows the performance of the models for thesolids content and cumulative copolymer composition.As can be observed, a very good agreement between off-line and Raman measurements was obtained for the solidscontent. The predictions of the Models 1 and 2 were verysimilar, with close mean absolute errors (Table VII). Thevalues of the absolute errors were comparable to the errorof the gravimetric measurements, meaning that very ac-curate predictions were obtained by the PLS models.Model 1 predicted well the evolution of the cumulativemolar copolymer composition (based in n-BA) withslight deviations at the beginning and at the end of the

1278 Volume 59, Number 10, 2005

FIG. 7. On-line monitoring by FT-Raman spectroscopy of the evolu-tion of the SC and copolymer composition during reaction Val 1, usingModels 1 and 2.

TABLE VII. Mean absolute errors for reaction Val 1 using thedifferent calibration models.

Target property PLS model Mean absolute error

SC

Composition

n-BA fraction

MMA fraction

Model 1Model 2Model 1Model 2Model 1Model 2Model 1Model 2

0.6220.8191.0252.3170.00260.0040.000420.00061

FIG. 8. On-line monitoring by FT-Raman spectroscopy of the evolu-tion of the n-BA and MMA fractions during reaction Val 1, using Mod-els 1 and 2.

polymerization. The performance of Model 1 was betterthan that of Model 2, and therefore a lower mean absoluteerror was obtained.

Figure 8 compares the evolution of unreacted n-BAand MMA concentrations estimated from FT-Ramanmeasurements with those determined by GC. Note thatreaction Val 1 was carried out in a semi-continuous re-actor under industrial-like conditions, which are designedto control copolymer composition, and hence monomerconcentrations should remain quite constant during theprocess. It can be observed that on-line measurementsagree well with the off-line determination of the unreact-ed monomers and that both models gave similar predic-tions. For both monomers, the predictions using Model 1were slightly better at the early stages of the reaction,when the SC was low, and consequently the calculatedmean absolute errors were a bit lower. In any case, theerror values were comparable to the experimental errorof GC.

Considering the small amount of monomer present inthe reaction mixture, the results are very satisfactory andcan be extremely useful for control strategies. It is im-portant to point out that under these conditions, the es-timation of the amounts of unreacted monomer using re-action calorimetry leads to substantial errors.35

CONCLUSION

In this work, an on-line monitoring technique for highsolids content all-acrylic emulsion copolymerizationsbased on FT-Raman spectroscopy was developed. Despite

the wide use of these emulsion copolymers in the chem-ical industry, the on-line monitoring of these copolymer-ization systems by Raman spectroscopy under industrialconditions had not been reported before. Models for theprediction of the solids content, the cumulative copoly-mer composition, and unreacted monomer amounts weredeveloped using partial least squares regression and ex-perimentally validated, obtaining satisfactory results. Themodels worked very well even under starved conditions,where very little monomer was present in the reactor. Theobtained results showed that FT-Raman spectroscopy of-fers the possibility to monitor several molecular proper-ties from a single measurement, which makes this tech-nique suitable for multipurpose in-line monitoring. Thisopens the possibility for application of FT-Raman spec-troscopy to process control.


ACKNOWLEDGMENTS

O. Elizalde acknowledges Ministerio de Educacion y Ciencia for thescholarship. The authors acknowledge the financial support from theUniversity of the Basque Country (Grant UPV 00221.215-13594/2001)and CICYT (project PP02000-1185).

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monitoring of high solids content starved-semi-batch emulsion copolymerization reactions by fourier...

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