Improved Kinetic Rate Constants for Nylon 6, Nylon 6,6, and Nylon 6/6,6 Copolymer

Liu, Fei F.; McAuley, Kimberley B.

doi:10.1002/mren.201900037

Cited by 6 publications

(2 citation statements)

References 66 publications

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“…Values of the rate-constant and activation-energy estimates agree well with literature values reported for similar polycondensation systems. [52][53][54] A sum of squared deviations for each set of b θ old values was calculated using:…”

Section: Using MC Simulations To Test the Effectiveness Of The Proposed Mbdoe Methodsmentioning

confidence: 99%

Model‐based design of experiments for polyether production from bio‐based 1,3‐propanediol

Shahmohammadi

McAuley

2021

AIChE Journal

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Sequential model-based design of experiments (MDBOE) accounts for information from previous experiments when selecting conditions for new experiments. In the current study, sequential MBDOE is used to select operating conditions for experiments in a batch-reactor that produces bio-based polytrimethylene ether glycol (PO3G). These Bayesian A-optimal experiments are designed to obtain improved estimates of 70 fundamental-model parameters, while accounting for industrial data from eight previous runs. Settings are selected for three decision variables: reactor temperature, initial catalyst level, and initial water concentration. If only one new experiment is conducted, it should be run at high temperature, with relatively high concentrations of catalyst and initial water. When two new runs are conducted, one should use an intermediate catalyst concentration. The effectiveness of the proposed MBDOE approach is tested using Monte-Carlo simulations, revealing that the selected experiments are superior compared to experiments selected randomly from corners of the permissible design space. K E Y W O R D S model-based design of experiments, parameter estimation, PO3G, polymerization model, sequential experimental design | INTRODUCTIONCerenol ® is tradename for a bio-based polytrimethylene ether glycol (PO3G) that has been commercialized by Dupont. [1][2][3] The monomer used for producing Cerenol ® is corn-based 1,3-propanediol derived from a glucose fermentation process (Bio-PDO ® ), 4 so that Cerenol ® is renewably-sourced. Cerenol ® offers a variety of value-added properties (e.g., excellent biodegradability, high reactivity, high oxidative stability) and has a wide range of applications in automotives, cosmetics, and polymer specialties. [1][2][3]5 Cerenol ® is produced in batch, semi-continuous, and continuous reactors using super-acid catalyst. Operating conditions such as catalyst concentration and temperature vary depending on the desired molecular weight and the end-use properties of the final products. 3,[6][7][8][9][10][11][12][13][14] The influences of operating conditions on product properties and production rates have been studied via several fundamental PO3G models. [15][16][17][18][19][20][21][22] Recently, we developed a series of PO3G models that account for: (a) the effects of super-acid catalyst on polycondensation reactions and side reactions in the liquid phase, (b) liquid hold-up in the condenser system, (c) the inhibitory influence of water on polycondensation kinetics, (d) the generation, consumption, and evaporation of cyclic oligomers, and (e) the effects of temperature on polycondensation kinetics and on mass-transfer rates of volatile species. [20][21][22] Our most-recent PO3G model contains a total of 49 ordinary differential equations (ODEs) and 70 kinetic, transport and thermodynamic parameters. 22 A comprehensive industrial dataset obtained from 8 batch-reactor runs, 3 which were conducted at temperatures ranging from 160 to 180 C and using super-acid catalyst levels from 0.1 to 0.25 wt%, w...

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Section: Using MC Simulations To Test the Effectiveness Of The Proposed Mbdoe Methodsmentioning

confidence: 99%

Model‐based design of experiments for polyether production from bio‐based 1,3‐propanediol

Shahmohammadi

McAuley

2021

AIChE Journal

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“…Yao's estimability ranking methods and Wu's MSE-based criterion have been used to aid parameter estimation in many different chemical and biochemical process models, including polymerization models. [15,[19][20][21][22][23][24][25][26] Many researchers have modeled ethylene/𝛼-olefin copolymerization systems over the years, but many of these models do not account for temperature effects. [27][28][29][30][31][32][33][34][35][36][37][38] Table 1 summarizes the main PE copolymerization modeling contributions in the literature where activation energies were estimated so that temperature effects could be included.…”

Section: Introductionmentioning

confidence: 99%

Accounting for Temperature Effects When Predicting Molecular Weight and Composition Distribution for Gas‐Phase Polyethylene Produced using a Multisite Catalyst

Gibson

Aiello

Yan

et al. 2022

Macro Theory & Simulations

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A dynamic model is developed for gas-phase ethylene/1-hexene polymerization with a three-site hafnocene catalyst. The model accurately predicts molecular weight and comonomer composition distributions for 15 lab-scale copolymerization runs performed at different temperatures. The experimental runs used to fit this model are performed at temperatures between 60 and 85 °C. Gas-phase concentrations are measured every 2.7 min throughout each run. Predicted chain-length distributions are discretized to aid model development, keeping the number of ordinary differential equations manageable. Kinetic parameters at the reference temperature of 81 °C and activation energies are estimated. Using parameter subset selection techniques, it is determined that 53 of the 60 model parameters should be estimated using the product characterization and reactor data. An additional data set obtained at 85 °C is used for model validation, confirming the predictive power of the model. The proposed model and its parameter estimates can aid selection of operating conditions to achieve targeted polymer properties.

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Diagnosing Parameter Estimability Problems in Polymerization Models

Liu

McAuley

2021

Macro Theory & Simulations

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To understand why some parameters are difficult to estimate in polymerization models, a new diagnostic methodology is proposed. This method is then used to investigate whether parameter estimability difficulties arise from the small influence of certain parameters on model predictions or from correlated effects with other parameters. The proposed method builds on a popular orthogonalization‐based parameter‐ranking algorithm that ranks parameters from the most estimable to the least estimable. A nylon 6/6,6 copolymerization model and a bio‐based polyether (PO3G) model are used to illustrate the effectiveness of the proposed methodology. Diagnosis of the nylon 6/6,6 model reveals that correlated behavior among six parameters related to nylon 6 cyclic dimer formation leads to parameter‐estimation difficulties. Diagnosis of the PO3G model reveals that difficulties in estimating low‐ranked parameters are mainly due to low‐sensitivities of some parameters and to lack of information about these parameters in the data. The proposed methodology will help future modelers make decisions about simplification of their model equations and about possible future experiments aimed at obtaining reliable parameter estimates and model predictions.

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Improved Kinetic Rate Constants for Nylon 6, Nylon 6,6, and Nylon 6/6,6 Copolymer

Cited by 6 publications

References 66 publications

Model‐based design of experiments for polyether production from bio‐based 1,3‐propanediol

Model‐based design of experiments for polyether production from bio‐based 1,3‐propanediol

Accounting for Temperature Effects When Predicting Molecular Weight and Composition Distribution for Gas‐Phase Polyethylene Produced using a Multisite Catalyst

Diagnosing Parameter Estimability Problems in Polymerization Models

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