Modeling and optimization of a high-pressure ethylene polymerization reactor using gPROMS

Asteasuain, Mariano; Brandolin, Adriana

doi:10.1016/j.compchemeng.2007.02.011

Cited by 33 publications

(44 citation statements)

References 15 publications

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“…It does not assume a priori any shape of the distribution and can be applied to different systems, even those described by complex kinetic mechanisms. It has provided excellent results in terms of accuracy, simplicity of implementation, and computational effort, in models for simulation and optimization activities 15–21. In its previous state of the art, this technique employed univariate pgfs, which allowed modeling a one‐dimensional distribution.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling of Bivariate Polymer Property Distributions Using 2D Probability Generating Functions, 1 – Numerical Inversion Methods

Asteasuain

Brandolin

2010

Macro Theory & Simulations

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This is the first of two papers presenting a new mathematical method for modeling bivariate distributions of polymer properties. It is based on the transformation of the infinite mass balances describing the evolution of a two‐dimensional distribution using 2D probability generating functions (pgf). A key step of this method is the inversion of the transforms. In this work, two numerical inversion methods of 2D pgfs are developed and carefully validated. The accuracy obtained with both methods was very satisfactory. The inversion formulas of both methods are simple and easy to implement. A simple copolymerization example is used to show the complete procedure from the derivation of the pgf balances to the recovery of the bivariate molecular weight distribution.magnified image

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Section: Introductionmentioning

confidence: 99%

Mathematical Modeling of Bivariate Polymer Property Distributions Using 2D Probability Generating Functions, 1 – Numerical Inversion Methods

Asteasuain

Brandolin

2010

Macro Theory & Simulations

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“…In general, these models consider realistic reactor configurations and include detailed predictions of physical and transport properties along the axial distance. Several applications of parametric analysis,6 optimization studies7–13 and parameter estimation techniques4, 5, 14 have been reported. Most models have focused on polymer average molecular properties, like the average molecular weights or average degrees of branching.…”

Section: Introductionmentioning

confidence: 99%

“…Most models have focused on polymer average molecular properties, like the average molecular weights or average degrees of branching. However, attempts to predict distributions, such as the full molecular weight distribution (MWD) or two‐dimensional distributions in molecular weight and branching frequency have also been reported 9, 15, 16…”

Section: Introductionmentioning

confidence: 99%

High‐Pressure Polymerization of Ethylene in Tubular Reactors: A Rigorous Dynamic Model Able to Predict the Full Molecular Weight Distribution

Asteasuain

Brandolin

2009

Macro Reaction Engineering

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A rigorous dynamic model of the high‐pressure polymerization of ethylene in tubular reactors is presented. The model is capable of predicting the full molecular weight distribution (MWD), average branching indexes, monomer conversion and average molecular weights as function of time and reactor length. The probability generating function method is applied to model the MWD. This technique allows easy and efficient calculation of the MWD, in spite of the complex mathematical description of the process. The reactor model is used to analyze the dynamic responses of MWD and other process variables under different transition policies, as well as to predict the effects of process perturbations. The influence of the material recycle on the process dynamics is also shown.

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“…gPROMS™ parameter estimation tool uses a maximum likelihood framework to estimate parameters within process models [166,167]. The use of both of these functionalities has been documented in the literature for industrial processes [168,169]. Both functions have also been used extensively in the simulation and optimization of batch crystallization processes for pharmaceutical applications [170][171][172].…”

Section: Gproms™ Gproms™ Is a Modeling Platform Developed By Process mentioning

confidence: 99%

Modeling of Particulate Processes for the Continuous Manufacture of Solid-Based Pharmaceutical Dosage Forms

2013

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Abstract:The objective of this work is to present a review of computational tools and models for pharmaceutical processes, specifically those for the continuous manufacture of solid dosage forms. Relevant mathematical methods and simulation techniques are discussed, as is the development of process models for solids-handling unit operations. Continuous processing is of particular interest in the current study because it has the potential to improve the efficiency and robustness of pharmaceutical manufacturing processes.

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Modeling and optimization of a high-pressure ethylene polymerization reactor using gPROMS

Cited by 33 publications

References 15 publications

Mathematical Modeling of Bivariate Polymer Property Distributions Using 2D Probability Generating Functions, 1 – Numerical Inversion Methods

Mathematical Modeling of Bivariate Polymer Property Distributions Using 2D Probability Generating Functions, 1 – Numerical Inversion Methods

High‐Pressure Polymerization of Ethylene in Tubular Reactors: A Rigorous Dynamic Model Able to Predict the Full Molecular Weight Distribution

Modeling of Particulate Processes for the Continuous Manufacture of Solid-Based Pharmaceutical Dosage Forms

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