Date of Award

2016

Degree Type

Thesis

Degree Name

Master of Science in Chemical Engineering

Department

Chemical and Biomedical Engineering

First Advisor

Ungarala, Sridhar

Subject Headings

Biomedical Engineering, Chemical Engineering

Abstract

Process simulation and state estimation have very important applications in chemical engineering as well as the biomedical field. Diabetes is a rapidly growing disease in the United States with 29 million people already diagnosed. The estimation of glucose and insulin concentration in patients is necessary in order to effectively treat diabetes. The Bergman Minimal Model is a popular process model that is used to simulate glucose and insulin dynamics. A simulation of this model was created based on estimated parameters for the model from historical data. This thesis investigated the estimation of glucose concentration, insulin concentration, and effect of active insulin using the extended Kalman filter, unscented Kalman filter, ensemble Kalman filter, and sequential Monte Carlo Particle filter. The performance of the filters was compared using root mean squared error. The filters were studied for the cases of good filter initialization, poor filter initialization, plant-model mismatch, increased measurement noise, and multiple glucose ingestions.

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