Kimberley B. McAuley

Bio: Kimberley B. McAuley is an academic researcher from Queen's University. The author has contributed to research in topics: Polymerization & Estimation theory. The author has an hindex of 16, co-authored 52 publications receiving 677 citations.

Selection of optimal parameter set using estimability analysis and MSE-based model-selection criterion

Abstract: Parameter estimation in complex mathematical models is difficult, especially when there are too many unknown parameters to estimate, and the available data for parameter estimation are limited. Estimability analysis ranks parameters from most estimable to least estimable based on the model structure, uncertainties in initial parameter guesses, measurement uncertainties, and experimental settings. Difficulties associated with poor numerical conditioning are avoided by only estimating those parameters that are most estimable. The remaining parameters are left at their initial values or can be removed from the model via simplification. In this paper, a mean squared error (MSE)-based model-selection criterion is used to determine the optimal number of parameters to estimate from the ranked parameter list, so that the most reliable model predictions can be obtained. This methodology is illustrated using a dynamic chemical reactor model.

Highly-selective CO2 conversion via reverse water gas shift reaction over the 0.5wt% Ru-promoted Cu/ZnO/Al2O3 catalyst

Abstract: The reverse water gas shift (RWGS) reaction can be potentially used to convert captured CO2 into renewable synthetic fuels via syngas generation. However, this conversion pathway has not been yet realized because of a number of technological challenges, including a lack of catalysts which possess satisfactory catalytic performance. In this paper, the RWGS reaction over a 0.5 wt% Ru-promoted 40 wt% Cu/ZnO/Al2O3 catalyst is studied. Due to the Ru addition, CO2 conversion was improved more than two-fold as compared to the baseline Cu/ZnO/Al2O3 catalyst. The catalyst stability was significantly improved as well. Although Ru is known for its high activity in methanation reactions, the 0.5 wt% Ru-Cu/ZnO/Al2O3 catalyst maintained complete selectivity to CO formation. In order to investigate this intriguing finding, the catalyst was studied by XRD, SEM-EDS, STEM-EDS, and TPR, revealing the possibility of the formation of Ru-Cu core-shell nanoparticles. Further investigation have shown that the Ru-support interaction is also a crucial factor affecting the catalyst selectivity. To extrapolate the experimentally measured data, a rate expression was derived and kinetic parameters were estimated. The resulting reaction rate expression was implemented to assess the catalyst performance under a wider range of operating conditions.

Nitroxide-mediated styrene miniemulsion polymerization

Abstract: Living radical polymerization of styrene was conducted in a miniemulsion using TEMPO and the water-soluble initiator potassium persulfate (KPS). The effects of initiator concentration and the TEMPO:KPS ratio on conversion, molecular weight distribution, and particle size were studied. The miniemulsion polymerizations exhibit similar characteristics to bulk living radical systems but with unique features attributable to the heterogeneous nature of the system. There is a strong interaction between the KPS concentration and the TEMPO:KPS ratio, and therefore the effects of changing either variable depend strongly on the value of the other variable. Initiator efficiencies are considerably higher than in conventional KPS-initiated styrene emulsion or miniemulsion polymerizations, while the average number of active radicals per particle (∼10-2) is much lower. Aqueous-phase kinetics and nitroxide partitioning determine the number of chains initiated and therefore also affect the polymerization rate and molecular...

Mean-Squared-Error Methods for Selecting Optimal Parameter Subsets for Estimation

Abstract: Engineers who develop fundamental models for chemical processes are often unable to estimate all of the parameters, especially when available data are limited or noisy. In these situations, modelers may decide to select only a subset of the parameters for estimation. An orthogonalization algorithm combined with a mean squared error (MSE) based selection criterion has been used to rank parameters from most to least estimable and to determine the parameter subset that should be estimated to obtain the best predictions. A robustness test is proposed and applied to a batch reactor model to assess the sensitivity of the selected parameter subset to initial parameter guesses. A new ranking and selection technique is also developed based on the MSE criterion and is compared with existing techniques in the literature. Results obtained using the proposed ranking and selection techniques agree with those from leave-one-out cross-validation but are more computationally attractive.

Subset Selection in Regression

Abstract: 8. Subset Selection in Regression (Monographs on Statistics and Applied Probability, no. 40). By A. J. Miller. ISBN 0 412 35380 6. Chapman and Hall, London, 1990. 240 pp. £25.00.

Controlled/living radical polymerization in dispersed systems

Abstract: 2.6. Microemulsion Polymerization 3778 2.6.1. General Considerations 3778 2.6.2. Microemulsion NMP 3778 2.6.3. Microemulsion ATRP 3778 2.6.4. Microemulsion RAFT Polymerization 3779 2.6.5. ITP in Microemulsion 3779 2.7. Dispersion and Precipitation Polymerizations 3779 2.7.1. General Considerations 3779 2.7.2. NMP 3780 2.7.3. ATRP 3781 2.7.4. RAFT 3782 2.7.5. Dispersion ITP 3782 3. Cross-linking CLRP in Dispersed Systems 3783 3.1. General Considerations 3783 3.2. Cross-linking NMP 3783 3.3. Cross-linking ATRP 3784 3.4. Cross-Linking RAFT Polymerization 3784 3.5. Cross-Linking ITP 3784 4. Particle Morphology 3785 4.1. General Considerations 3785 4.2. Core-Shell Particles 3785 4.3. Hollow Particles 3785 4.4. Multilayered Particles 3786 5. Conclusions and Outlook 3787 6. List of Abbreviations 3788 7. Acknowledgments 3789 8. References 3789