2026-09-19T08:53:20Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1218202026-06-16T18:53:07Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Paul I. Barton.
Dowdy, Garrett Ryan.
Massachusetts Institute of Technology. Department of Chemical Engineering.
Massachusetts Institute of Technology. Department of Chemical Engineering
2019-07-18T20:33:16Z
2019-07-18T20:33:16Z
2019
2019
https://hdl.handle.net/1721.1/121820
1103917704
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 329-334).
Distributions appear in many forms in models of chemical engineering systems. Such distributions account for microscopic variability in the system while simultaneously explaining its macroscopic properties. These macroscopic properties are often of practical engineering interest. Thus, it is valuable to be able to characterize the underlying distributions that affect them. Recently, in the mathematical programming literature, it was shown that it is possible to optimize a linear objective over a set of distributions by solving a specific type of convex optimization problem called a semidefinite program (SDP). From a theoretical perspective, SDPs can be solved efficiently. Furthermore, there exist several off-the-shelf codes designed specifically to solve SDPs. This thesis demonstrates how these theoretical and practical advancements can be applied to chemical engineering problems featuring distributions. Broadly speaking, it shows how, given limited information about a distribution, one can use SDPs to calculate mathematically rigorous bounds on various descriptions of that distribution. Two specific types of distributions are examined: particle size distributions and probability distributions arising in stochastic chemical kinetics, with the majority of the thesis covering the latter topic. The SDP-based bounding method described herein provides a rigorous solution to the long-standing "moment closure problem" arising in stochastic chemical kinetics. Moreover, it provides a means of analyzing of stochastic chemical kinetic systems which cannot be effectively analyzed using existing methods. The bounding method does have some limitations, and we present several refinements of the method aimed at overcoming these limitations. Finally, we discuss several ideas through which the bounding method may be further improved, which have not yet been explored.
by Garrett Ryan Dowdy.
Ph.D.
Ph.D. Massachusetts Institute of Technology, Department of Chemical Engineering
334 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Chemical Engineering.
Using semidefinite programming to bound distributions in chemical engineering systems
Thesis
application/pdf
2019-07-18T20:33:13Z
Publication
Doctoral
ChemEng
unknown
unknown
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<Abstract>Distributions appear in many forms in models of chemical engineering systems. Such distributions account for microscopic variability in the system while simultaneously explaining its macroscopic properties. These macroscopic properties are often of practical engineering interest. Thus, it is valuable to be able to characterize the underlying distributions that affect them. Recently, in the mathematical programming literature, it was shown that it is possible to optimize a linear objective over a set of distributions by solving a specific type of convex optimization problem called a semidefinite program (SDP). From a theoretical perspective, SDPs can be solved efficiently. Furthermore, there exist several off-the-shelf codes designed specifically to solve SDPs. This thesis demonstrates how these theoretical and practical advancements can be applied to chemical engineering problems featuring distributions. Broadly speaking, it shows how, given limited information about a distribution, one can use SDPs to calculate mathematically rigorous bounds on various descriptions of that distribution. Two specific types of distributions are examined: particle size distributions and probability distributions arising in stochastic chemical kinetics, with the majority of the thesis covering the latter topic. The SDP-based bounding method described herein provides a rigorous solution to the long-standing "moment closure problem" arising in stochastic chemical kinetics. Moreover, it provides a means of analyzing of stochastic chemical kinetic systems which cannot be effectively analyzed using existing methods. The bounding method does have some limitations, and we present several refinements of the method aimed at overcoming these limitations. Finally, we discuss several ideas through which the bounding method may be further improved, which have not yet been explored.</Abstract>
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