Control of Molecular Weight Distribution in Emulsion Polymerization Using On-Line Reaction Calorimetry

Vicente, M.; Benamor, S.; Gugliotta, Luis Marcelino; Leiza, José R.; Asúa, José M.

doi:10.1021/ie000387e

Cited by 74 publications

(84 citation statements)

References 19 publications

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“…In order to get a target MWD, especially for batch processes, normally optimal or sub-optimal control trajectories are determined by optimisation design either off-line 6 ,27 or on-line lB . 26 . and then efforts are made to implement these trajectories using control strategies.…”

Section: Mokcumrwe~htd~tributionconuolm Polymerisation Processesmentioning

confidence: 99%

Recent Developments in Stochastic Distribution Control/A Review

Yue

Wang

2003

Measurement and Control

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Figure 1: MWD in chemical control reaction system Mokcumrwe~htd~tributionconuolm polymerisation processesIt is important to control a polymer's molecular weight distribution (MWD) in industrial polymerisation processes because a polymer's end-use properties are strongly dependent on its MWD. Though it is still not an easy control task, extensive studies have been made on how to get a polymer's MWD when the polymerisation kinetic model is available5.6.25. Mostly the MWD is calculated by numerical integration of the polymer material balances or by using the moment generating function. In certain cases, some stochastic models have been proposed for MWD description. For example, for linear chains, Flory's distribution can be used to describe the instantaneous MWD of polyolefin made with single-site-type catalystslI.Industry-scale closed-loop control of MWD is still a challenging subject because the feasibility of on-line measurement of MWD for polymerisation processes remains to be demonstrated. Most research is based on simulated reactors. In order to get a target MWD, especially for batch processes, normally optimal or sub-optimal control trajectories are determined by optimisation design either off-line 6 ,27 or on-line lB . 26 . and then efforts are made to implement these trajectories using control strategies. State observers are often needed to get on-line trajectories lB , while for off-line control trajectories, batch-to-batch modifications to the optimal trajectories may be added so that the target MWD can be achieved after several batches 4 • Figure 1 shows a general MWD system. General research into the control of stochastic systems has been focused on the control of the system output, rather than the probability density function (PDF) of the system output. A number of well-known algorithms have thus been developed such as minimum variance control, linear quadratic Gaussian control, self-tuning control and stochastic control for systems with Markovian jump parameters, etc. In most of the existing approaches, it has been assumed that the random variables in the system are subjected to Gaussian processes. This is based on the fact that most input noises can be characterised as coloured noises, which are generated by a white noise sequence. Although many successful awlications of these developed methods have been reponed, difficulties remain in many cases where this assumption is not valid. As such, a new group of techniques has been developed for the model1ing and control of the probability density functions of the output variables of stochastic systems 3l .The idea of stochastic distribution control is to design the control input so that the PDF of the system output can follow a target PDF. This control purpose is highly demanded in many industrial systems, such as chemical engineering, powder industry, papermaking, and combustion flame distribution processes, etc. Several typical industrial examples are discussed here. Industrial BackgroundA group of new control strategies called stochastic distribution c...

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Section: Mokcumrwe~htd~tributionconuolm Polymerisation Processesmentioning

confidence: 99%

Recent Developments in Stochastic Distribution Control/A Review

Yue

Wang

2003

Measurement and Control

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“…[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%

“…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. [28] Emulsion systems were fed to suspension reactions at different times achieving bimodality along with an extra feeding of only monomer and water. [29] During emulsion copolymerization reactions, multiobjective controllers have been studied as a means to also achieve a certain copolymer composition distribution (CCD), along with MWD.…”

Section: Introductionmentioning

confidence: 99%

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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“…In order to produce a certain molecular weight distribution or a special co-polymer composition, control of the concentrations of the monomers and possibly of the chain transfer agent is necessary. Vicente et al (2001) calculate an optimal trajectory by minimizing the inverse of the reaction rate to achieve an optimal process time. The equality constraints of this optimization problem are given by a rigorous dynamic model of the polymerization of styrene with butyl maleate (BuM), which acts as a CTA.…”

Section: Introductionmentioning

confidence: 99%

“…The process investigated here is the emulsion polymerization of styrene using butyl maleate as a CTA (cf. (Vicente et al, 2001)). The goal is to produce a prespecified multi modal molecular weight distribution in minimum time.…”

Section: Introductionmentioning

confidence: 99%

Time Optimal Control of the Molecular Weight in a Semi Batch Emulsion Polymerization

Gesthuisen

Dadhe

Krämer

et al. 2004

IFAC Proceedings Volumes

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In this contribution a hierarchical approach to the control of the molecular weight in a semi-batch emulsion polymerization is derived, which ensures minimum batch time. Rather than performing a numerical optimization to determine an optimal trajectory for the process at any point in time, the existing boundary conditions are examined and used to determine the time optimal operation at each time step. This procedure provides the set points for the molar holdups of monomer and chain transfer agent (CTA) in the system. Either a decentralized PI controller or a nonlinear model predictive controller (NMPC) can be applied to track the calculated trajectory. As the set point calculation is a solution of a system of algebraic equations it can easily be performed on-line. It is shown that this concept can be used to produce multi modal molecular weight distributions and can also handle disturbances in the process, such as an unexpected nucleation of particles.

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Control of Molecular Weight Distribution in Emulsion Polymerization Using On-Line Reaction Calorimetry

Cited by 74 publications

References 19 publications

Recent Developments in Stochastic Distribution Control/A Review

Recent Developments in Stochastic Distribution Control/A Review

Automatic Synthesis of Multimodal Polymers

Time Optimal Control of the Molecular Weight in a Semi Batch Emulsion Polymerization

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