Molecular weight distribution control in a batch polymerization reactor

Takamatsu, Takeichiro; Shioya, Suteaki; Okada, Yohji

doi:10.1021/ie00073a018

Cited by 63 publications

(65 citation statements)

References 4 publications

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“…Reaction temperature and temperature profile used in the course of the reaction have a high influence on the molecular weight distribution (MWD) of the polymer produced. Several studies have explored these variables in order to produce a polymer with the desired quality in terms of MWD (Wu et al, 1982;Özan et al, 1998;Takamatsu et al, 1988;Kiparissides et al, 2002, Sheibat-Othmana, 2011, Zhang and Zhang, 2011. Thus, a controller that has the ability to maintain the temperature at the set point, in addition to allowing transitions between set points in different ways (step quickly and without the occurrence of overshoot and ramp with the desired variation range) ensures that the goal is reached.…”

Section: Resultsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

“…Depending on the application, the polymer needs to meet specific quality standards, among which is the molecular weight distribution, whose variation directly affects its characteristics, such as mechanical, thermal and flow properties (Takamatsu et al, 1988;Crowley and Choi, 1998).…”

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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“…Figure 10 shows that there is a much smaller variation in η r than η r,inst , and Figure 7 shows the smaller variation of M w compared to M w,inst , which yields a significantly different power law for η r versus M w . When using a viscometer instead of light scattering for control, a target trajectory can be established, M w,inst,t , and then Equation (26) can be used to find the corresponding target path η r,inst,t . Figure 12 shows the analysis of Figure 6 data, which is for a trimodal population, produced by stabilizing an initial M w , followed by a first addition of CTA and then a second addition of CTA.…”

Section: Use Of Reduced Viscosity η R In Bimodal Polymer Productionmentioning

confidence: 99%

“…[24,25] Most optimization models use the kinetics of the reaction to predict certain profiles of these variables in order to control MWD of the polymer usually with involved mathematical equations. [26] Attempts at controlling emulsion polymerizations were carried out in different studies by measuring the amount of monomer and CTA inside the reactor by online gas chromatography (GC) [27] or calorimetry [28] and estimating the MWD trajectory with an offline calculation of the optimal path for achieving bimodality through a nonlinear model-based controller. The feed rates of monomer and CTA needed for following this trajectory were applied in different experiments using one CTA [27] and two other types of agents.…”

Section: Introductionmentioning

confidence: 99%

“…After polymerizing one miniemulsion for a certain period, the other miniemulsion was fed into the reactor to obtain a bimodal distribution. [36] The current work concerns the production of two or more modes (or subpopulations) during a polymerization process. Production of multimodal molecular weight distributions is the focus with free radical homopolymerization of linear (unbranched) chains.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Synthesis of Multimodal Polymers

Leonardi¹,

Montgomery²,

Siqueira

et al. 2017

Macro Reaction Engineering

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An automatic molecular weight controller is used in semi‐batch reactions to produce final polymers that contain distinct subpopulations with different molecular weight distributions (MWD). While blending polymers with different MWD is frequently used to make multimodal polymer products, a method is introduced here allowing the multimodal product to be made in successive, automatically controlled stages in the same reactor. The method is demonstrated using free radical polymerization of acrylamide to produce widely separated multimodal MWD. Automatic Continuous Online Monitoring of Polymerization reactions with a Control Interface (ACOMP/CI) was used. Weight average molecular weight (Mw) in the ACOMP/CI was continuously measured with multi‐angle light scattering and ultra‐violet absorption. The controller used two principles: both polymerization rate and instantaneous molecular weight are proportional to monomer concentration. By controlling monomer flow rate into the reactor, the controller produced a targeted amount of polymer with a desired first Mw and then automatically introduced chain transfer agent (CTA) into the reactor to produce a second population of polymers with much lower Mw. Subsequently, reactions were performed to produce trimodal populations. This approach is kindred to multi‐stage synthesis of polymers where distinctly different processes are carried out in succeeding stages to produce polymers with highly specific properties.

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Polynomial ARMA model identification for a continuous styrene polymerization reactor using on-line measurements of polymer properties

Rhee

2000

J. Appl. Polym. Sci.

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The multiinput-multioutput identification for a continuous styrene polymerization reactor using a polynomial ARMA model is carried out by both simulation and experiment. The pseudorandom multilevel input signals are applied for model identification in which input variables are the jacket inlet temperature and the feed flow rate, whereas the output variables are the monomer conversion and the weightaverage molecular weight. The use of a polynomial ARMA model for identification of the multivariable polymerization reaction system is validated by simulation study. For the experimental corroboration, correlations are developed to convert the on-line measurements of density and viscosity of the reaction mixture to the monomer conversion and the weight-average molecular weight. The on-line values of the conversion and weightaverage molecular weight turn out to be in good agreement with the off-line measurements. Despite the complex and nonlinear features of the polymerization reaction system, the polynomial ARMA model is found to satisfactorily describe the dynamic behavior of the polymerization reactor. Therefore, one may apply the polynomial ARMA model to the optimization and control of polymerization reactor systems.

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Molecular weight distribution control in a batch polymerization reactor

Cited by 63 publications

References 4 publications

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

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

Automatic Synthesis of Multimodal Polymers

Polynomial ARMA model identification for a continuous styrene polymerization reactor using on-line measurements of polymer properties

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