Comparison of techniques for the determination of conversion during suspension polymerization reactions

Santos, Juliana C.; Lopes, Cristiane N.; Reis, Marlon M.; Giudici, Reinaldo; Sayer, Cláudia; Machado, Ricardo Antônio Francisco; Araújo, Pedro Henrique Hermes de

doi:10.1590/s0104-66322008000200017

Cited by 23 publications

(22 citation statements)

References 18 publications

(20 reference statements)

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“…In [54], the group uses spectroscopy data to detect differences in PSD, noting that it might be possible to identify abnormal behavior in the suspension polymerization with the detection of unexpected PSD.…”

Section: Raman Spectroscopymentioning

confidence: 99%

Polymerization Online Monitoring

Frauendorfer¹,

Wolf²,

Hergeth³

2010

Chem Eng & Technol

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Online monitoring of polymerization reactions is not only important due to the high exothermic nature of most polymerization systems; the data gained also provides information on product composition and quality and control possibilities thereof. Many inline and online methods are still in development and are better suited for application on the laboratory or pilot-plant scale. Industrial polymerization plant environments pose additional technical and financial challenges and constraints for the use of such systems. Available methods and current developments are reviewed with regard to their practicability and usefulness under these aspects.

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Section: Raman Spectroscopymentioning

confidence: 99%

Polymerization Online Monitoring

Frauendorfer¹,

Wolf²,

Hergeth³

2010

Chem Eng & Technol

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“…In special, in hetero geneous polymerization processes, these techniques can be useful to simultaneous monitoring different variables, such as those related to chemical changes in the reaction medium (e.g., monomer conversion) as well as those related to the physical changes in the particles (e.g., changes in particle size). [8][9][10][11][12][13][14][15][16][17][18][19] Spectroscopy-based sensors in combination with optical fibers allow the inline measurements and real-time monitoring of polymerization processes. [18][19][20][21][22][23][24][25][26][27][28][29] For the particular case of heterogeneous polymerization reactions, Raman and NIR spectra show two distinct regions, as identified in Figure 1, where different characteristics can be explored: (a) the spectral region that permits to identify chemical information related to the vibrations of the main bonds present in the components (monomer and polymer) and (b) the spectral region with physical information, where the spectrum baseline variation is related to physical properties, such as particle diameter in this case.…”

Section: Introductionmentioning

confidence: 99%

Miniemulsion Polymerization Monitoring Using Off‐Line Raman Spectroscopy and In‐Line NIR Spectroscopy

Ambrogi

Colmán

Giudici

2016

Macro Reaction Engineering

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by water-borne latex, increasing the importance of conventional emulsion and miniemulsion polymerization.In comparison with conventional emulsion polymerization processes, miniemulsion polymerization presents clear advantages because polymerization takes place inside the monomer droplets, which allows using monomers and other highly hydrophobic components in the formulation. Therefore, these components do not need to diffuse in the aqueous medium as in emulsion polymerization. [1][2][3][4] The miniemulsion polymerization process starts with the preparation of the monomer miniemulsion and, after that, the addition of an initiator starts the polymerization. A standard miniemulsion formulation uses water as continuous phase, monomer as disperse phase, surfactant to stabilize the particles droplets, costabilizer to avoid diffusional degradation (Oswald Ripening), and buffer to stabilize the pH of the medium. [5] Constant particle size, use of costabilizers, use of surfactant below the critical micellar concentration, and inexistence of mass transport through the aqueous phase are Miniemulsion polymerization has been extensively studied in the last decade due to its advantages when compared with conventional emulsion polymerization. In this work, monomer conversion and particle size are monitored during miniemulsion polymerization of styrene using in-line near-infrared (NIR) spectroscopy and at-line Raman spectroscopy. Gravimetric analysis and dynamic light scattering have been used as off-line reference measurements. Good agreement has been found between off-line data and the values predicted by NIR and Raman spectroscopy for conversion. The values of average particle size are well predicted from the NIR spectra with the respective calibration model, but the predictions from Raman spectra present some slight discrepancies for particle size. The results show a decrease of the average particle size during the initial period of the polymerization, indicating the occurrence of nucleation mechanisms other than the classical droplet nucleation. These results indicate that insightful information can be obtained by monitoring miniemulsion polymerizations with the use of spectroscopic techniques.

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“…Considering that polymer properties such as molecular weight, polydispersity index, and morphological characteristics are not easy to be obtained online in a polymerization system, models for estimation of these properties are required for the implementation of efficient control and monitoring systems (Prasad et al, 2002;Vieira et al, 2003;Bindlish and Rawlings, 2003;Santos et al, 2008). For polymerization chain reactions, temperature and initial initiator concentration has high influence on the reaction kinetics and the polymer molecular weight distribution, with a direct effect on polymer properties (Sacks et al, 1973;Erdogan et al, 2002;Hosen and Hussain, 2012).…”

Section: Introductionmentioning

confidence: 99%

Predictive Control of a Batch Polymerization System Using a Feedforward Neural Network With Online Adaptation by Genetic Algorithm

Cancelier

Claumann

Bolzan

et al. 2016

Braz. J. Chem. Eng.

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-This study used a predictive controller based on an empirical nonlinear model comprising a threelayer feedforward neural network for temperature control of the suspension polymerization process. In addition to the offline training technique, an algorithm was also analyzed for online adaptation of its parameters. For the offline training, the network was statically trained and the genetic algorithm technique was used in combination with the least squares method. For online training, the network was trained on a recurring basis and only the technique of genetic algorithms was used. In this case, only the weights and bias of the output layer neuron were modified, starting from the parameters obtained from the offline training. From the experimental results obtained in a pilot plant, a good performance was observed for the proposed control system, with superior performance for the control algorithm with online adaptation of the model, particularly with respect to the presence of off-set for the case of the fixed parameters model.

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Comparison of techniques for the determination of conversion during suspension polymerization reactions

Cited by 23 publications

References 18 publications

Polymerization Online Monitoring

Polymerization Online Monitoring

Miniemulsion Polymerization Monitoring Using Off‐Line Raman Spectroscopy and In‐Line NIR Spectroscopy

Predictive Control of a Batch Polymerization System Using a Feedforward Neural Network With Online Adaptation by Genetic Algorithm

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