Control of microstructural properties in emulsion polymerization systems

Control strategies for the simultaneous control of microstructural properties of copolymer latexes (copolymer composition and molecular weight distribution) are presented. For linear polymers, on-line control strategies based on calorimetric measurements allowed to produce styrene/n-butyl acrylate emulsion polymers of predefined copolymer compositions and MWDs. The strategy failed for nonlinear polymers because the polymer produced at a certain process time might later in the process become active varying its molecular weight. Alternative open-loop control policies were developed for nonlinear polymers. These strategies required a mathematical model of the process that is used in an off-line optimization to determine the trajectories of the manipulated variables (feed flow rates of monomer and CTA) that allow producing the desired copolymers. The implementation of the open-loop control allowed the production of nonlinear MMA/n-BA emulsion copolymers of well-defined copolymer composition and MWD.     

Spectroscopic on-line monitoring of reactions in dispersed medium: chemometric challenges

Emulsion and suspension polymerizations are important industrial processes for polymer production. The end-user properties of polymers depend strongly on how the polymerization reactions proceed in time (i.e. a batch or semicontinuous, rate of reagents feeding, etc.). In other words, these reactions are process dependent, which makes the successful process control a key point to ensure high-quality products. In several process control strategies the on-line monitoring of reaction performance is required. Due to the multiphase nature of the emulsion and suspension processes, there is a lack of sensors to perform successful on-line monitoring. Near infrared and Raman spectroscopies have been pointed out as useful approaches for monitoring emulsion and suspension polymerizations and several applications have been described. In such instance, the chemometric approach on relating near infrared and Raman spectra to polymer properties is widely used and has proven to be useful. Nevertheless, the multiphase nature of emulsion and suspension polymerizations also represents a challenge for the chemometric approach based on multivariate calibration models and demands the development of new methods. In this work, a set novel results is presented from the monitoring of 15 batch emulsion reactions that show the chemometric challenge to be faced on development of new methods for successful monitoring of processes taken under dispersed medium. In order to discuss these results, several chemometric approaches were revised. It is shown that Raman and NIR spectroscopic techniques are suitable for on-line monitoring of monomer concentration and polymer content during the polymerizations, as well as medium heterogeneity properties, i.e. average particle size. It is also shown that Hotteling and Q statistics, widely used in chemometrics, might fail in monitoring these reactions, while an approach based on principal curves is able to overcome such restriction.     

苯乙烯/丙烯酸正丁酯乳液聚合反应过程中残余单体含量的实时监测

采用近红外光谱分析技术在线测量苯乙烯(St)/丙烯酸正丁酯(BA)乳液聚合体系中残余单体的含量. 共设计9个半连续方式的St/BA乳液共聚反应, 在反应过程中实时取样测量其残余单体含量, 并记录取样时刻对应的聚合体系的近红外光谱. 采用多元散射校正法(MSC)处理光谱, 有效地克服了乳胶粒子散射效应对近红外光谱分析的影响. 采用主成分分析法(PCA)对乳液体系的近红外光谱数据进行了解析. 选取6个聚合反应对应不同反应时间的72个样品, 用于建立校正模型, 另外3个聚合反应共取36个样品用于校正模型的验证, 并在反应设计上体现了乳化剂用量的变化, 从而使校正模型对乳化剂用量的变化具有一定的适应性. 研究结果表明, 所得模型对残余单体St和BA含量的预测结果标准差(SEP)分别为0.08026和0.05305.     

Classification and quantitation of finishing oils by near infrared spectroscopy

One of the steps in the manufacturing of synthetic fibres involves using finishing oils to ensure proper lubricity and adherence between fibres, and also the absence of static electricity. Choosing an appropriate oil and dosage are essential with a view to ensuring effective subsequent processing and use. The aim of this work was to develop a fast method for determining the different finishing oil content in acrylic fibres by use of near infrared spectroscopy (NIRS) in conjunction with partial least-squares regression (PLSR). The high similarity between the NIR spectra of finishing oils led us to assume that a single calibration model might allow determine the oil content. However, the inability to quantify accurately different finishing oils by using a sole calibration model, constrain to the prior classification of the fibres coated with the different finishing oils. Two different pattern recognition methods were used: supervised independent modeling of class analogy (SIMCA) and artificial neural networks (ANNs). However, the low contribution of the finishing oil to the NIR spectrum for the fibre sample, the high similarity between the NIR spectra for the different oils and the substantial contribution of the linear density of the acrylic fibre to the spectrum precluded correct classification by SIMCA; on the other hand, ANNs provided good results. By constructing appropriate PLSR models for the different types of finishing oils, these can be accurately determined in acrylic fibres.     

Comparison of various chemometric evaluation approaches for on-line FT-NIR transmission and FT-MIR/ATR spectroscopic data of methyl methacrylate solution polymerization

For the evaluation of vibrational spectroscopic data acquired on-line to a chemical reaction a broad range of different chemometric algorithms is available. The present study reports the comparative results obtained by different chemometric techniques from the data acquired by light-fiber coupled Fourier-transform near infrared (FT-NIR) transmission spectroscopy and Fourier-transform mid-infrared (FT-MIR) spectroscopy in the attenuated total reflection (ATR) mode to monitor the solution polymerization of methyl methacrylate (MMA). We have found that the results obtained by the application of multivariate curve resolution (MCR) methods to the MIR spectral data acquired during the polymerization of MMA are quite comparable to the results derived by partial least-squares calibration. In the case of the NIR data univariate calibration yields somewhat poorer results than multivariate calibration and MCR, but still inside an acceptable range.     

EFFECT OF 2—HYDROXYETHYL METHACRYLATE ON SEMIBATCH EMULSIFIER—FREE EMULSION COPOLYMERIZATION OF METHYL METHACRYLATE AND BUTYL ACRYLATE

The semibatch emulsifier-free emulsion copolymerization of methyl methacrylate (MMA) and butyl acrylate(BA) in the presence of 2-hydroxyethyl methacrylate(HEMA) initiated by K2S2O8(PSP) was studied.The latex particles can maintain an appreciable stability during the emulsifier-free emulsion copolymerization of MMA and BA in the presence of HEMA.The average particle diameter increase with an increase of total solids content,HEMA content,PSP content,ionic strength of the system and monomer feed rate,and decrease with the monomer feed ration from 3/1 (MMA/BA:molar ration).to 1/3.The stability of this reaction system is improved by adding HEMA as nonionic comonomer,High solids content (50%) latex with monodisperse particle can be obtained using this process.     

Random subspace regression ensemble for near-infrared spectroscopic calibration of tobacco samples.

An ensemble, a model-independent technique based on combining several models for classification/regression tasks, allows us to achieve a high accuracy that is often not achievable with single models. Such combinations have gained increasing attention in many fields. This paper proposes the use of random subspace (RS)-based regression ensemble as an alternative method for near-infrared (NIR) spectroscopic calibration of tobacco samples. Because of the considerable reduction of variables in a random subspace, multiple linear regression (MLR) is used as the base algorithm and the method is therefore also referred to as RS-MLR. The overall performance of the proposed RS-MLR method is compared to those of partial least square regression (PLSR), kernel principal component regression (KPCR) and kernel partial least square regression (KPLSR). The results reveal that the RS-MLR method not only has a simple concept but also can produce a more parsimonious and more accurate calibration model than PLSR, KPCR and KPLSR, at a lower computational cost. Besides, we also found that the RS-MLR method is very appropriate for the so-called small sample problems and that the calibration models built by RS-MLR are less sensitive to overfitting.     

Non-invasive determination of ethanol, propylene glycol and water in a multi-component pharmaceutical oral liquid by direct measurement through amber plastic bottles using Fourier transform near-infrared spectroscopy

Fourier transform near-infrared (FT-NIR) spectroscopy was used to quantify rapidly the ethanol (34-49% v/v), propylene glycol (20-35% v/v) and water (11-20% m/m) contents within a multi-component pharmaceutical oral liquid by measurement directly through the amber plastic bottle packaging. Spectra were collected in the range 7302-12,000 cm-1 and calibration models set-up using partial least-squares regression (PLSR) and multiple linear regression. Reference values for the three components were measured using capillary gas chromatography (ethanol and propylene glycol) and Karl Fischer (water) assay procedures. The calibration and test sets consisted of production as well as laboratory batches that were made to extend the concentration ranges beyond the natural production variation. The PLSR models developed gave standard errors of prediction (SEP) of 1.1% v/v for ethanol, 0.9% v/v for propylene glycol and 0.3% m/m for water. For each component the calibration model was validated in terms of: linearity, repeatability, intermediate precision and robustness. All the methods produced statistically favourable outcomes. Ten production batches independent of the calibration and test sets were also challenged against the PLSR models, giving SEP values of 1.3% v/v (ethanol), 1.0% v/v (propylene glycol) and 0.2% m/m (water). NIR transmission spectroscopy allowed all three liquid constituents to be non-invasively measured in under 1 min.     

Comparing near infrared and Raman spectroscopy for on-line monitoring of emulsion copolymerization reactions

In this work, Raman and Near InfraRed (NIR) spectroscopies are evaluated for the monitoring of different semicontinuous emulsion homo- and co-polymerization reactions. Important process variables, namely monomer concentrations and average particle sizes, were monitored by both techniques under realistic conditions that would be found in an industrial environment (e.g. low signal/noise ratio, probe placed in the reaction medium). Results suggest that Raman and NIR are suitable for on-line monitoring of emulsion polymerization reactions and that the success of their application is mainly related to representative calibration models used for the estimation of the properties of interest.     

功能单体α-烯烃磺酸钠用于无皂乳液共聚合

用工业原料α-烯烃磺酸钠(AOS)作为功能单体与甲基丙烯酸甲酯(MMA)和丙烯酸丁酯(BA)进行了乳液共聚合,通过测定AOS与MAA的竞聚率,确定了适宜的聚合方式为连续加料法.使用5%AOS制备了高固含量(>60%)的胶乳,并与用十二烷基硫酸钠(SDS)作乳化剂时该体系的乳液共聚合进行了比较.AOS是影响乳液稳定性和胶粒大小的主要因素,当AOS含量为单体总质量的1%时可以得到固含量大于40%粒径小于100 nm的乳液;当AOS含量为5%时可以得到固含量大于60%的乳液.两种情况下胶粒粒径分散性均较窄,明显优于同样条件下用SDS制备的胶乳.使用1%AOS制得的胶乳静置1年后粒径及其分布基本保持不变.     

衰减全反射-紫外/可见光谱技术应用

本文对衰减全反射法(ATR)的光学测定原理、衰减全反射-紫外/可见光谱方法(ATR-UV)的特点以及它们在一些典型的工业过程溶液,如高浓度和含有大量固体颗粒和微乳液聚合等体系检测的应用进行了综述。ATR-UV光谱技术适宜实时地反馈工业过程溶液的组分改变和浓度变化,从而帮助我们了解该过程的进展。这些优良的特性能帮助我们开发在线的传感器,因此可用于监测许多典型的工业过程溶液。其方法简单、迅速,一般无需对试样进行预处理或稀释。ATR-UV作为光谱学测定的重要波段,其技术开发将在化工过程检测方面具有重要的意义。     

P(MMA-M12-BPMA)乳液的合成及紫外吸收性能

以甲基丙烯酸甲酯(MMA)、可聚合乳化剂马来酸酐衍生物磺酸钠(M12)和可聚合紫外线吸收剂2-羟基-4-(3-甲基丙烯酰氧基-2-羟基丙氧基)苯甲酮(BPMA)为原料,采用乳液聚合方法制备了P(MMA-M12-BP-MA)共聚物乳液。通过转化率、红外光谱、以及紫外吸收光谱测定,分别研究了M12含量对聚合反应速率的影响、所得共聚产物的结构、以及共聚物乳液和共聚物紫外吸收特性。结果表明,随M12含量增大,聚合反应速率增加;BPMA含量增大有利于提高共聚物乳液和共聚物紫外吸收性能。     

Synthesis of waterborne acrylic/clay nanocomposites by controlled surface initiation from macroinitiator modified montmorillonite

The nitroxide mediated controlled surface initiated polymerization of methyl methacrylate (MMA)/butyl acrylate (n-BA) was carried out with a macroinitiator modified montmorillonite. The macroinitiator was synthesized by the nitroxide mediated polymerization of vinylbenzyl trimethylammonium chloride (VBTMACl), methylmethacrylate (MMA) and styrene (S) at 90 °C using BlocBuilder®. The macroinitiator was exchanged with the sodium cations of the montmorillonite, to yield surface modified reactive montmorillonite. The bulk polymerizations of BA/MMA from the clay surface produced controlled molecular weight polymers that were able to exfoliate the clay. This controlled polymer/clay nanocomposite was used as masterbatch and further dispersed in monomers and miniemulsified to perform miniemulsion polymerization of BA/MMA (90/10 wt.%) at 30 wt.% solids content at low emulsifier concentration. The adhesive properties of the nanocomposites prepared with the masterbatch were proved to be better than those prepared with an organically modified clay.     

EFFECT OF 2-HYDROXYETHYL METHACRYLATE ON SEMIBATCH EMULSIFIER-FREE EMULSION COPOLYMERIZATION OF METHYLMETHACRYLATE AND BUTYL ACRYLATE

IINTRODUCTIONSemibatchemulsionpolymerizationisanimportalmethodfortheproductionofpolymersforthecoatingsandadhesivesindustries.Thetraditionallatexproductsaregenerallystabilizedbyemulsifiers.However,theemulsifiersremaininginthelatexproductcanhaveanegativeeffectontheapplicationpropertiessuchasadhesiontosubstratesandfilmformationandwaterresistanceofthecoatingmaterials,becausethesmallandmobileemulsifiermoleculestendtomigratetothesurfacelayerofthepolymericfilm.Withtheemulsifier-freeemulsionpolym…     

Semibatch Emulsifier-free Emulsion Copolymerization of Methyl Methacrylate and n-Butyl Acrylate in the Presence of 2-Hydroxyethyl Methacrylate

ThemethodsofimProvingthestabilityofemulsifier-freeemulsionpolymerizationhavebeenreportedinliteraturet(a)choosingionizableinitiators,suchaspotassiumpersulfate'andazo-bis(isobutyramidinehydrochloride)';(b)copolymerizationwithhydrophiliccomonomers,whichinvolvestheionictypesuchascarboxylicmonomers',sulfoderivativesofvinylmonomers',andthenonionictypesuchasglycidylmethacrylate';(c)copolymerizationwithsurface-activemonomers,suchassulfodecylslyrylether';(d)addingorganicsolventstothesystem,suchasmethano…     

In Line Monitoring of VAc-BuA Emulsion Polymerization Reaction in a Continuous Pulsed Sieve Plate Reactor using NIR Spectroscopy

Summary: This work deals with the in line and in situ monitoring of the changes in residual monomer concentrations and polymer particle size in the process of emulsion copolymerization of vinyl acetate (VAc) and butyl acrylate (BuA) over the sections of a novel tubular reactor (pulsed sieve plate continuous reactor, PSPC) using NIR spectroscopy. Off-line measurements (gas chromatographic, gravimetric and dynamic light scattering) were used as reference for the development of the multivariate PLS calibration model. All NIR spectra were on-line collected with an IFS 28/N Bruker spectrometer using a probe (transflectance mode) immersed into the reaction medium. The calibration model and validation data were analyzed using the OPUS/QUANT software. The results indicated that there exists a good agreement between values from the NIR calibration models and the off-line reference measured experimental data. Moreover, the on-line NIR can detect efficiently the occurrence of disturbances during the polymerization reaction, a useful tool for the improving the process safety.     

Combined Experimental and Multivariate Model Approaches for Glycoalkaloid Quantification in Tomatoes

The intake of tomato glycoalkaloids can exert beneficial effects on human health. For this reason, methods for a rapid quantification of these compounds are required. Most of the methods for α-tomatine and dehydrotomatine quantification are based on chromatographic techniques. However, these techniques require complex and time-consuming sample pre-treatments. In this work, HPLC-ESI-QqQ-MS/MS was used as reference method. Subsequently, multiple linear regression (MLR) and partial least squares regression (PLSR) were employed to create two calibration models for the prediction of the tomatine content from thermogravimetric (TGA) and attenuated total reflectance (ATR) infrared spectroscopy (IR) analyses. These two fast techniques were proven to be suitable and effective in alkaloid quantification (R² = 0.998 and 0.840, respectively), achieving low errors (0.11 and 0.27%, respectively) with the reference technique.     

Multivariate NIR spectroscopy models for moisture, ash and calorific content in biofuels using bi-orthogonal partial least squares regression

The multitude of biofuels in use and their widely different characteristics stress the need for improved characterisation of their chemical and physical properties. Industrial use of biofuels further demands rapid characterisation methods suitable for on-line measurements. The single most important property in biofuels is the calorific value. This is influenced by moisture and ash content as well as the chemical composition of the dry biomass. Near infrared (NIR) spectroscopy and bi-orthogonal partial least squares (BPLS) regression were used to model moisture and ash content as well as gross calorific value in ground samples of stem and branches wood. Samples from 16 individual trees of Norway spruce were artificially moistened into five classes (10, 20, 30, 40 and 50%). Three different models for decomposition of the spectral variation into structure and noise were applied. In total 16 BPLS models were used, all of which showed high accuracy in prediction for a test set and they explained 95.4-99.8% of the reference variable variation. The models for moisture content were spanned by the O-H and C-H overtones, i.e. between water and organic matter. The models for ash content appeared to be based on interactions in carbon chains. For calorific value the models was spanned by C-H stretching, by O-H stretching and bending and by combinations of O-H and C-O stretching. Also -C=C- bonds contributed in the prediction of calorific value. This study illustrates the possibility of using the NIR technique in combination with multivariate calibration to predict economically important properties of biofuels and to interpret models. This concept may also be applied for on-line prediction in processes to standardize biofuels or in biofuelled plants for process monitoring.     

Multiplexing fibre optic near infrared (NIR) spectroscopy as an emerging technology to monitor industrial bioprocesses

The application of near infrared spectroscopy in bioprocessing has been limited by its dependence on calibrations derived from single bioreactor at a given time. Here, we propose a multiplexed calibration technique which allows calibrations to be built from multiple bioreactors run in parallel. This gives the flexibility to monitor multiple vessels and facilitates calibration model transfer between bioreactors. Models have been developed for the two key analytes: glucose and lactate using Chinese hamster ovary (CHO) cell lines and using analyte specific information obtained from the feasibility studies. We observe slight model degradation for the multiplexed models in comparison to the conventional (single probe) models, decrease in r² values from 89.4% to 88% for glucose whereas for lactate from 92% to 91.8% and a simultaneous increase in the number of factors as the model incorporates the inter-probe variability, nevertheless the models were fit for purpose. The results of this particular application of implementing multiplexed-NIRS to monitor multiple bioreactor vessels are very encouraging, as successful models have been built on-line and validated externally, which proffers the prospect of reducing timelines in monitoring the vessels considerably, and in turn, providing improved control.     

Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk

This paper proposes the use of the least-squares support vector machine (LS-SVM) as an alternative multivariate calibration method for the simultaneous quantification of some common adulterants (starch, whey or sucrose) found in powdered milk samples, using near-infrared spectroscopy with direct measurements by diffuse reflectance. Due to the spectral differences of the three adulterants a nonlinear behavior is present when all groups of adulterants are in the same data set, making the use of linear methods such as partial least squares regression (PLSR) difficult. Excellent models were built using LS-SVM, with low prediction errors and superior performance in relation to PLSR. These results show it possible to built robust models to quantify some common adulterants in powdered milk using near-infrared spectroscopy and LS-SVM as a nonlinear multivariate calibration procedure.