A Bayesian approach to feed reconstruction
Name
862813005-MIT.pdf
Description
Full printable version
Size
6.09 MB
Format
Adobe PDF
Checksum (MD5)
53d1fa7e8009692f179b41e74f888c32
Author(s)
Conjeevaram Krishnakumar, Naveen Kartik
Advisor(s)
Youssef M. Marzouk.
Date Issued
2013
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 83-86).
Subjects
Computation for Design and Optimization Program.
MIT Department
Massachusetts Institute of Technology. Computation for Design and Optimization Program
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copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
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