Modeling the nucleation stage during batch emulsion polymerization

Fortuny, Montserrat; Graillat, C.; McKenna, Timothy F. L.; Araújo, Pedro Henrique Hermes de; Pinto, José Carlos

doi:10.1002/aic.10516

Cited by 12 publications

(12 citation statements)

References 49 publications

(42 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The whole scheme is tested using the rather involved model proposed in Ref. 22 as a measurement generator. One way to enforce the performance of the proposed scheme is to iteratively enrich the database used to compute the spline by the new data issued from each batch operation.…”

Section: Discussionmentioning

confidence: 99%

“…22 (serving here as the experimental data generator) and the profiles issued from the mathematical behavioral model's expressions (13)- (15). Note that the precision of the approximation of F ð2Þ max (Á) can be improved by introducing higher order polynomial rather than using a straight line as in (15).…”

Section: Parametrization Of the Temporal Profilesmentioning

confidence: 99%

“…Evaluation of the learning control scheme proposed in section 5 using an erroneous model. This is obtained by changing the coefficients Kp 11 and Kp 12 of the model proposed by 22 used here to produce the measurement data.…”

Section: Numerical Simulationsmentioning

confidence: 99%

“…22 plays the role of experimental facility to deliver the measurement data. For the sake of completeness, this model is described in the appendix.…”

Section: The Contribution Of the Present Work: A Behavioral Model Appmentioning

confidence: 99%

“…(v) The second CBO is now executed with the corrected model (21)- (22). Namely, the CBO parameter vector p (2) is computed according to: …”

Section: Incorporating the Behavioral Model In A Simple Learning Contmentioning

confidence: 99%

See 4 more Smart Citations

Measurement based modeling and control of bimodal particle size distribution in batch emulsion polymerization

Alamir¹,

Sheibat‐Othman²,

Othman³

2010

AIChE Journal

View full text Add to dashboard Cite

In this article, a novel modeling approach is proposed for bimodal Particle Size Distribution (PSD) control in batch emulsion polymerization. The modeling approach is based on a behavioral model structure that captures the dynamics of PSD. The parameters of the resulting model can be easily identified using a limited number of experiments. The resulting model can then be incorporated in a simple learning scheme to produce a desired bimodal PSD while compensating for model mismatch and/or physical parameters variations using very simple updating rules. V V C 2010 American Institute of Chemical Engineers AIChE J, 56: 2122-2136, 2010 Keywords: polymerization in emulsion, reduced model, particle size distribution, nonlinear control, predictive control IntroductionEmulsion polymerization processes are interesting for the production of adhesives and paints. The latex produced in these processes is the dispersion of polymer particles into water. The latex properties (optical properties, film formation) heavily depend on the particle size distribution (PSD) of the latex.1 Particularly, the use of bimodal PSD was found interesting in increasing the solid content (which is of high interest in order to reduce the drying time and transport costs), while keeping a low latex viscosity (see for instance, Refs. 2 and 3).In Refs. 4 and 5 the theoretical controllability of the population balance equations of this system has been analyzed. The authors concluded that the PSD is approximately controllable for unconstrained manipulation of feed surfactant concentration. Then, the authors used online measurements of the monomer conversion obtained by densimetry and delayed measurements of the particle size compensated by an observer to calculate the number of particles and predict the birth rate of particles in a continuous stirred tank reactor. These measurements were then used in a PID controller that manipulates the surfactant feed to control the PSD.Despite the theoretically possible controllability of the system, and because of the lack of online measurements of the PSD, controlling the PSD remained a difficult task. Light scattering technologies mainly concern monodispersed lattices and operate off-line because of the necessity of dilution. However, the analysis time of such techniques can be reduced to about 5 minutes and might therefore be adapted for online use if a dilution system is incorporated in the process. Separative technologies, such as capillary hydrodynamic fractionation (CHDF) are more suitable for multidispersed lattices (see for instance 6 and 7 ) but require a longer analysis time (about 15 minutes) and operate therefore offline. An alternative measurement that can be useful for controlling the PSD is the concentration of the free surfactant in the aqueous phase (since it allows calculating the Correspondence concerning this article should be addressed to M. Alamir at mazen.alamir@gipsa-lab.inpg.fr. 2122AIChE Journal August 2010 Vol. 56, No. 8 concentration of micelles) that can be obtained by conductimetry (...

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Parametrization Of the Temporal Profilesmentioning

confidence: 99%

Section: Numerical Simulationsmentioning

confidence: 99%

“…22 plays the role of experimental facility to deliver the measurement data. For the sake of completeness, this model is described in the appendix.…”

Section: The Contribution Of the Present Work: A Behavioral Model Appmentioning

confidence: 99%

“…(v) The second CBO is now executed with the corrected model (21)- (22). Namely, the CBO parameter vector p (2) is computed according to: …”

Section: Incorporating the Behavioral Model In A Simple Learning Contmentioning

confidence: 99%

See 3 more Smart Citations

Measurement based modeling and control of bimodal particle size distribution in batch emulsion polymerization

Alamir¹,

Sheibat‐Othman²,

Othman³

2010

AIChE Journal

View full text Add to dashboard Cite

show abstract

Calorimetry, Conductivity, Densimetry, and Rheological Measurements

Leiza¹,

McKenna²

2013

Monitoring Polymerization Reactions

View full text Add to dashboard Cite

Mathematical modeling of molecular weight distribution in miniemulsion polymerization with oil‐soluble initiator

et al. 2017

Self Cite

View full text Add to dashboard Cite

A mathematical model for the study of reaction kinetics and molecular weight distributions in miniemulsion polymerization systems with oil‐soluble initiators is presented. The mathematical model allows the computation of the evolution of the complete molecular weight distribution with chain lengths of up to 105 mers in miniemulsion polymerization by direct integration in reasonable computational time. Also, no restriction in the kinetic regime is needed, as the model is able to represent both compartmentalized and pseud‐bulk systems. The model was validated with experimental results for methyl methacrylate and styrene homopolymerizations, with two different oil‐soluble initiators, and adequately represented both the kinetics and molecular weight distributions of these systems. © 2017 American Institute of Chemical Engineers AIChE J, 63: 2128–2140, 2017

show abstract

Modeling the nucleation stage during batch emulsion polymerization

Cited by 12 publications

References 49 publications

Measurement based modeling and control of bimodal particle size distribution in batch emulsion polymerization

Measurement based modeling and control of bimodal particle size distribution in batch emulsion polymerization

Calorimetry, Conductivity, Densimetry, and Rheological Measurements

Mathematical modeling of molecular weight distribution in miniemulsion polymerization with oil‐soluble initiator

Contact Info

Product

Resources

About