- Published 2013

In this thesis, we developed a Bayesian approach to estimate the detailed composition of an unknown feedstock in a chemical plant by combining information from a few bulk measurements of the feedstock in the plant along with some detailed composition information of a similar feedstock that was measured in a laboratory. The complexity of the Bayesian model combined with the simplex-type constraints on the weight fractions makes it difficult to sample from the resulting high-dimensional posterior distribution. We reviewed and implemented different algorithms to generate samples from this posterior that satisfy the given constraints. We tested our approach on a data set from a plant. Thesis Supervisor: Youssef M. Marzouk Title: Associate Professor of Aeronautics and Astronautics

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@inproceedings{Marzouk2013ABA,
title={A Bayesian approach to feed reconstruction by Naveen Kartik},
author={Youssef Marzouk},
year={2013}
}