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Emulsion polymerization predictive control

Wenz and colleagues at Bayer Polymers Inc. describe the use of Raman spectroscopy to monitor the progress of a graft emulsion polymerization process, specifically the manufacture of ABS graft copolymer, in order to select the appropriate reaction termination point.40 Early termination reduces product yield and results in an extra product purification step termination too late reduces product quality. As Figure 5.5 illustrates, the reaction composition over time is not smooth and predictable, making it unlikely that off-line analysis would ever be fast enough to ensure correct control decisions. [Pg.150]

It is clear from Eq. 1 that the monomer concentration in a polymer particle is one of the three key factors that control the particle growth rate, and accordingly, the rate of polymerization. In emulsion polymerization, the course of emulsion polymerization is usually divided into three stages, namely. Intervals I, II and III. In Intervals I and II of emulsion homopolymerization, the monomer concentration in the polymer particles is assumed to be approximately constant. In Interval III, it decreases with reaction time. Two methods are now used to predict the monomer concentration in the polymer particles in emulsion homopolymerization empirical and thermodynamic methods. [Pg.47]

Emulsion polymerization is a powerful technique for developing products for a wide range of industry. The process can be used for the development of structured products in the nano- to microscales. Some of the key advantages of the process are the use of mild conditions, the near-complete conversion of monomers, the minimization of separation and recycling, the improved heat and mass transfer, and the improved environmental benefits due to the use of a water medium. However, the process is complex and requires careful modeling and scale-up. The prediction of process behavior, the key product properties, and the control of emulsion polymerization systems in particu-... [Pg.876]

The large number of variables, and the dependence of many of them upon conversion, usually means that it is necessary to compare experimera with predictions and to Iterate to values of the parameters which give best-fit correlation with experimental data. These control strategies are, therefore, specific to a particular emulsion polymerization reaction (i.e. a specific formulation and reaction conditions). While the principles of this approach can be applied to a range of emulsion copolymerizations, it is not possible to predict the optimum addition rate profiles directly and considerable experimental effort is required to establish optimum values of parameters for each system. [Pg.147]

An optimal predictive controller was developed and implemented to allow for maximization of monomer conversion and minimization of batch times in a styrene emulsion polymerization reactor, using calorimetric measiuements for observation and manipulation of monomer feed rates for attainment of control objectives [31]. Increase of 13% in monomer conversion and reduction of 28% in batch time were reported. On-line reoptimization of the reference temperature trajectories was performed to allow for removal of heater disturbances in batch bulk MMA polymerizations [64]. Temperature trajectories were manipulated to minimize the batch time, while keeping the final conversion and molecular weight averages at desired levels. A reoptimization procediue was implemented to remove disturbances caused by the presence of unknown amounts of inhibitors in the feed charge [196]. In this case, temperatiue trajectories were manipulated to allow for attainment of specified monomer conversion and molecular weight averages in minimum time. [Pg.354]

Additionally, this approach offers, in the case of emulsion polymerization, a unified means of monitoring colloid/ polymer characteristics giving information about both the polymer and particle evolution, and thus allowing one to make correlations between key features of the two aspects of the emulsion polymerization process. Online monitoring of both particle and polymer characteristics should allow for studies of reaction kinetics, predictive and active reaction control, and also the ability to observe deviations and unexpected phenomena. [Pg.253]

Emulsion polymerization was developed for producing polymers with unique properties and because of environmental considerations. Although the reaction medium in emulsion polymerization remains at low viscosity, factors such as instability of the lattices and other nonequilibrium phenomena, complexity of the materials produced, and multifaceted properties requiring multiple characterization approaches add challenges in developing accurate predictive models and/or reaction control schemes. [Pg.255]

In general, the optimization of polymerization processes [2] focuses on the determination of trade-offs between polydispersity, particle size, polymer composition, number average molar mass, and reaction time with reactor temperature and reactant flow rates as manipulated variables. Certain approaches [3] apply nonhnear model predictive control and online, nonlinear, inferential feedback control [4] to both continuous and semibatch emulsion polymerization. The objectives include the control of copolymer composition. [Pg.363]

As mentioned above, the two most popular reaction mechanisms involved in the absorption of free radicals by the monomer-swoUen micelles and polymer particles are the diffusion- and propagation-controlled models. Nevertheless, liotta et al. [39] were inclined to support the colUsion-controlled model. A dynamic competitive particle growth model was developed to study the emulsion polymerization of styrene in the presence of two distinct populations of latex particles (i.e., bimodal particle size distribution). Comparing the on-line density and particle size data with model predictions suggests that absorption of free radicals by the latex particles follows the collision-controlled mechanism. [Pg.108]

Chern [42] developed a mechanistic model based on diffusion-controlled reaction mechanisms to predict the kinetics of the semibatch emulsion polymerization of styrene. Reasonable agreement between the model predictions and experimental data available in the literature was achieved. Computer simulation results showed that the polymerization system approaches Smith-Ewart Case 2 kinetics (n = 0.5) when the concentration of monomer in the latex particles is close to the saturation value. By contrast, the polymerization system under the monomer-starved condition is characterized by the diffusion-con-trolled reaction mechanisms (n > 0.5). The author also developed a model to predict the effect of desorption of free radicals out of the latex particles on the kinetics of the semibatch emulsion polymerization of methyl acrylate [43]. The validity of the kinetic model was confirmed by the experimental data for a wide range of monomer feed rates. The desorption rate constant for methyl acrylate at 50°C was determined to be 4 x 10 cm s ... [Pg.186]

One unique but normally undesirable feature of continuous emulsion polymerization carried out in a stirred tank reactor is reactor dynamics. For example, sustained oscillations (limit cycles) in the number of latex particles per unit volume of water, monomer conversion, and concentration of free surfactant have been observed in continuous emulsion polymerization systems operated at isothermal conditions [52-55], as illustrated in Figure 7.4a. Particle nucleation phenomena and gel effect are primarily responsible for the observed reactor instabilities. Several mathematical models that quantitatively predict the reaction kinetics (including the reactor dynamics) involved in continuous emulsion polymerization can be found in references 56-58. Tauer and Muller [59] developed a kinetic model for the emulsion polymerization of vinyl chloride in a continuous stirred tank reactor. The results show that the sustained oscillations depend on the rates of particle growth and coalescence. Furthermore, multiple steady states have been experienced in continuous emulsion polymerization carried out in a stirred tank reactor, and this phenomenon is attributed to the gel effect [60,61]. All these factors inevitably result in severe problems of process control and product quality. [Pg.189]

The choice of emulsifier is critical since it controls the stability of the emulsions prior to and after polymerization. Moreover, polymerization conditions typically represent destabilizing factors vigorous stirring, temperature rise and evolution of acrylamide content in the aqueous phase. In the case of inverse emulsions, the HLB values mostly used by the formulators range between 4 and 6. Some attempts were made to predict quantitatively the optimal HLB value corresponding to the most stable dispersions [18,19]. The treatment was based on the so-called cohesive energy ratio (CER) concept devekq)ed by Beer-bower and Hill for conventional emulsions [20]. Tins approach is based on a perfect chemical match between the partial solubility parameters of oil (ig)... [Pg.782]

Process Control Vieira et al. [176, 185, 186] studied copolymerizalions of butyl acrylate and MMA in anulsion reactors and reported for the first time the use of the NIR spectral signal to perform the feedback control of an anulsion polymerization reactor. Vieira et al. [176, 185, 186] used a dispersive instrument equipped with a transflectance probe to collect the NIR spectra and showed that robust and reliable PLS caUbralion models could be developed for the prediction of the major constituents of the emulsion. A detailed process model was used as reference to provide estimates of unmeasured process variables, such as average particle sizes and the polymer average molecular weight. The NIR model predictions were corrected in-line with the... [Pg.126]


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