A Systems Approach to the Modeling and Control of Molecular, Microparticle, and Biological Distributions

A rigorous approach is applied to systems in which the output variable takes the form of a distribution: molecular, microparticle, and biological distributions. This approach involves incorporation of sensor technology and experiments, model identification, simulation algorithm development, parameter sensitivity analysis, parameter estimation, model validation, optimal control formulation, and worst-case and distributional robustness analysis. Free radical bulk polymerization is the model system for molecular distributions. In situ ATR-FTIR spectroscopy was used to determine monomer concentration; off-line gel permeation chromatography was used to determine the molecular weight distribution. Kinetic parameters are determined using maximum likelihood estimation. For the first time, the full molecular weight distribution is simulated by directly solving the entire mole balance equations. The method is capable of predicting bimodal distributions with no prior information of the distribution. Worst-case analysis is used to introduce a new distributional analysis tool: distributional analysis of a distribution, which enables the deep investigation into the effects of parameter uncertainties, control implementation uncertainties, and disturbances in the molecular weight distribution. Suspension polymerization is used to produce micron-sized polymer beads. In situ laserbackscattering (coupled with inverse modeling) and video microscopy (coupled with image analysis) are used to determine the droplet/particle size distribution. A high resolution finite volume algorithm is used to numerically solve the population balance equation to describe the particle size distribution. Parameter sensitivity analysis indicates that droplet breakage is the dominant mechanism. Distributional analysis of the particle size distribution was applied to understand the affects of parameter uncertainties. For biological distributions, parameter estimation, sensitivity analysis, and a cumulative distribution indicates that a microscopic model best predicts the physics associated with single-molecule pulling experiments. For biological systems that exhibit multiple binding states, a double-bond microscopic model is proposed, in which there is good agreement between model predictions and observed data.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Chemical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Hukkanen, Eric John

Contributors dc:contributor

• Braatz, Richard D.

• Engineering, Chemical

Language dc:language

eng

Identifier

(MiAaPQ)AAI3160893

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/82370