2026-09-19T08:32:01Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/824142022-01-13T07:54:53Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Youssef M. Marzouk.
Conjeevaram Krishnakumar, Naveen Kartik
Massachusetts Institute of Technology. Computation for Design and Optimization Program.
Massachusetts Institute of Technology. Computation for Design and Optimization Program
2013-11-18T19:20:06Z
2013-11-18T19:20:06Z
2013
2013
http://hdl.handle.net/1721.1/82414
862813005
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).
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.
by Naveen Kartik Conjeevaram Krishnakumar.
S.M.
86 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Computation for Design and Optimization Program.
A Bayesian approach to feed reconstruction
Thesis
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