Terpolymerization of Triisopropylsilyl Acrylate, Methyl Methacrylate, and Butyl Acrylate: Reactivity Ratio Estimation

Yousefi, Fereshteh K.; Jannesari, Ali; Pazokifard, Shahla; Saeb, Mohammad Reza; Scott, Alison J.; Penlidis, Alexander

doi:10.1002/mren.201900014

Cited by 3 publications

(5 citation statements)

References 25 publications

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“…Yousefi et al [93] Triisopropylsilyl acrylate/Methyl methacrylate/Butyl Acrylate terpolymerization Linearization-based JCR* Krieger et al [24] Itaconic acid/N-vinyl-2-pyrrolidone Copolymerization Linearization-based JCR and CIs* Santos et al [23] Styrene/VeoVa-10 copolymerization Linearization-based CIs* Abdi and McAuley [74] N-butyl methacrylate and n-butyl acrylate copolymerization Linearization-based JCR and Bootstrap-based JCR Reischke [94] 2-methylene-1,3-dioxepane/vinyl acetate and methyl methacrylate/ dodecyl methacrylate copolymerization Linearization-based CIs and JCR, and Likelihood-based CIs and JCR Pablo-Morales et al [95] Myrcene and glycidyl methacrylate copolymerization Linearization-based JCR and CIs* Abdi et al [37] N-hexane hydroisomerization Bootstrap-based CIs and boxplots…”

Section: Uncertainty Quantification Techniquementioning

confidence: 99%

Propagating input uncertainties into parameter uncertainties and model prediction uncertainties—A review

2023

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A review of uncertainty quantification techniques is provided for a variety of situations involving uncertainties in model inputs (independent variables). The situations of interest are divided into three categories: (i) when model prediction uncertainties are quantified based on uncertainties in uncertain inputs, (ii) when parameter estimate uncertainties are calculated by propagation of uncertainties from measured inputs and outputs, and (iii) when model prediction uncertainties are quantified based on corresponding uncertainties in measured inputs and uncertain parameter estimates. For all three situations, linearization‐based and Monte Carlo‐based techniques are reviewed and details for their corresponding algorithms are presented. Recommendations are provided on which uncertainty quantification techniques are best for different types of chemical engineering models based on the amount of input uncertainty and nonlinearity over the range of plausible input and parameter values.

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Section: Uncertainty Quantification Techniquementioning

confidence: 99%

Propagating input uncertainties into parameter uncertainties and model prediction uncertainties—A review

2023

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“…Tables 1 and 2 summarize previous EVM studies in the chemical engineering literature where data from experiments with uncertain inputs were used for parameter estimation 4,7–12,14,16,17,19,22,24,25,27,30–51 . Studies in Table 1 involve reactivity‐ratio estimation in copolymerization and terpolymerization studies.…”

Section: Introductionmentioning

confidence: 99%

“…However, in situations where the uncertainties in inputs are relatively large, using WLS parameter estimation might result in unrealistic parameter estimates and underprediction of parameter uncertainties. EVM 7–30 has been used in a variety of chemical engineering modeling studies (see Tables 1 and 2).…”

Section: Introductionmentioning

confidence: 99%

“…Tables 1 and 2 summarize previous EVM studies in the chemical engineering literature where data from experiments with uncertain inputs were used for parameter estimation. 4,[7][8][9][10][11][12]14,16,17,19,22,24,25,27, Studies in As shown in Tables 1 and 2, values of σ 2 Y were reported in 29 of the 39 studies, while the remaining authors were silent about the weighting factors used for parameter estimation. Authors from 23 of the 39 studies assumed that σ 2 Y was known a priori (indicated using AK in the 4 th columns), while 14 studies were silent about how σ 2 Y was obtained (indicated using?…”

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Estimation of output measurement variances for error‐in‐variables model parameter estimation

Abdi

McAuley

2022

AIChE Journal

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Error-in-variables model (EVM) methods require information about variances of input and output measured variables when estimating the parameters in mathematical models for chemical processes. In EVM, using replicate experiments for estimating output measurement variances is complicated because true values of inputs may be different when multiple attempts are made to repeat an experiment. To address this issue, we categorize attempted replicate experiments as: (i) true replicates (TRs) when uncertain inputs are the same in replicated runs and (ii) pseudo replicates (PRs) when measured inputs are the same, but unknown true values of inputs are different. We propose methodologies to obtain output measurement variance estimates and associated parameter estimates for both situations. We also propose bootstrap methods for obtaining joint-confidence information for the resulting parameter estimates.A copolymerization case study is used to illustrate the proposed techniques. We show that different assumptions noticeably affect the uncertainties in the resulting reactivity-ratio estimates.copolymerization, error-in-variables model, Mayo-Lewis equation, reactivity-ratio estimation, replicate experiments | INTRODUCTIONFundamental mathematical models are widely used for chemical process development and improvement. These models usually contain unknown parameters that require estimation. 1,2 In conventional parameter-estimation methodologies such as weighted least-squares (WLS) estimation, the model inputs are assumed to be perfectly known (i.e., measured without error), while random measurement errors are considered for the model outputs. 3,4 Nevertheless, there are important situations where model inputs are not perfectly known, but contain significant uncertainties. The error-in-variables model (EVM) technique was developed to account for measurement uncertainties in both inputs and outputs during parameter estimation. 5,6 In

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“…The diversity of monomers allows for the production of countless new multi-component materials, all with particular combinations of desirable properties for custom applications. In the past decade alone, terpolymers have been investigated for use as electronic materials [3][4][5], biomedical materials [6][7][8], pressure sensitive adhesives [9][10][11][12], coatings [13][14][15], and for many more applications. A good understanding of terpolymerization kinetics (for each unique terpolymer system) ensures that appropriate formulations are selected to achieve desirable properties in the product terpolymer.…”

Section: Introductionmentioning

confidence: 99%

Making the Most of Parameter Estimation: Terpolymerization Troubleshooting Tips

et al. 2019

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Multi-component polymers can provide many advantages over their homopolymer counterparts. Terpolymers are formed from the combination of three unique monomers, thus creating a new material that will exhibit desirable properties based on all three of the original comonomers. To ensure that all three comonomers are incorporated (and to understand and/or predict the degree of incorporation of each comonomer), accurate reactivity ratios are vital. In this study, five terpolymerization studies from the literature are revisited and the ‘ternary’ reactivity ratios are re-estimated. Some recent studies have shown that binary reactivity ratios (that is, from the related copolymer systems) do not always apply to ternary systems. In other reports, binary reactivity ratios are in good agreement with terpolymer data. This investigation allows for the comparison between previously determined binary reactivity ratios and newly estimated ‘ternary’ reactivity ratios for several systems. In some of the case studies presented herein, reactivity ratio estimation directly from terpolymerization data is limited by composition restrictions or ill-conditioned systems. In other cases, we observe similar or improved prediction performance (for ternary systems) when ‘ternary’ reactivity ratios are estimated directly from terpolymerization data (compared to the traditionally used binary reactivity ratios). In order to demonstrate the advantages and challenges associated with ‘ternary’ reactivity ratio estimation, five case studies are presented (with examples and counter-examples) and troubleshooting suggestions are provided to inform future work.

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Terpolymerization of Triisopropylsilyl Acrylate, Methyl Methacrylate, and Butyl Acrylate: Reactivity Ratio Estimation

Cited by 3 publications

References 25 publications

Propagating input uncertainties into parameter uncertainties and model prediction uncertainties—A review

Propagating input uncertainties into parameter uncertainties and model prediction uncertainties—A review

Estimation of output measurement variances for error‐in‐variables model parameter estimation

Making the Most of Parameter Estimation: Terpolymerization Troubleshooting Tips

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