Show simple item record

dc.contributor.authorAjagekar, Akshay
dc.date.accessioned2020-08-10T20:07:39Z
dc.date.available2020-08-10T20:07:39Z
dc.date.issued2020-05
dc.identifier.otherAjagekar_cornell_0058O_10797
dc.identifier.otherhttp://dissertations.umi.com/cornell:10797
dc.identifier.urihttps://hdl.handle.net/1813/70302
dc.description142 pages
dc.description.abstractQuantum computing (QC) is the next frontier in computation and has attracted a lot of attention from the scientific community in recent years. With the ever-increasing complexity of combinatorial optimization problems accompanied by a quickly growing search space, there arises a need for novel solution approaches capable of overcoming limitations of the current optimization paradigms carried out on state-of-the-art classical computers. QC provides a novel approach to help solve some of the most complex optimization problems while offering an essential speed advantage over classical methods. Complex nature of energy systems due to their structure and large number of design and operational constraints make energy systems optimization a hard problem for most available algorithms. We propose novel reformulations of energy systems optimization problems namely facility location-allocation for energy systems infrastructure development, unit commitment of electric power systems operations, and heat exchanger network synthesis into unconstrained binary optimization problems to facilitate ease of mapping and solving on quantum hardware. Several technological limitations face commercially available quantum computers, therefore, harnessing the complementary strengths of classical and quantum computers to solve complex large-scale optimization problems is of utmost importance. We further develop novel hybrid QC-based models and methods that exploit the complementary strengths of QC and exact solution techniques to overcome the combinatorial complexity when solving large-scale discrete-continuous optimization problems. The applicability of these QC-based algorithms is demonstrated by large-scale applications across scales that are relevant to molecular design, process scheduling, manufacturing systems operations, and logistics optimization. Apart from optimization, QC-based techniques can also be applied to fault diagnosis of complex chemical processes.
dc.subjectDeep learning
dc.subjectHybrid techniques
dc.subjectOptimization
dc.subjectProcess monitoring
dc.subjectProcess systems
dc.subjectQuantum computing
dc.titleQuantum Computing for Process Systems Optimization and Data Analytics
dc.typedissertation or thesis
thesis.degree.disciplineChemical Engineering
thesis.degree.grantorCornell University
thesis.degree.levelMaster of Science
thesis.degree.nameM.S., Chemical Engineering
dc.contributor.chairYou, Fengqi
dc.contributor.committeeMemberVarner, Jeffrey
dcterms.licensehttps://hdl.handle.net/1813/59810
dc.identifier.doihttps://doi.org/10.7298/1qrb-8t07


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Statistics