Decision-making frameworks for practical industrial applications in optimal process design and control

dc.contributor.advisorBaldea, Michael
dc.contributor.advisorEdgar, Thomas F.
dc.contributor.committeeMemberBeaman, Joseph
dc.contributor.committeeMemberBonnecaze, Roger
dc.contributor.committeeMemberRochelle, Gary
dc.creatorCostandy, Joseph Gamal Nessim
dc.creator.orcid0000-0002-7275-4640
dc.date.accessioned2021-10-18T22:27:53Z
dc.date.available2021-10-18T22:27:53Z
dc.date.created2021-08
dc.date.issued2021-08-16
dc.date.submittedAugust 2021
dc.date.updated2021-10-18T22:27:54Z
dc.description.abstractWhile economics are the driving force behind many of the decisions made by industrial stakeholders, the methodologies employed to make high-level decisions often utilize heuristics that may not be quantitatively optimal. In this dissertation, I develop optimization-based frameworks that enable quantitatively driven high-level decision-making for two problems of practical industrial significance. In the first part of the dissertation, I address the problem of deciding the operating mode (batch or continuous-flow) of a chemical process, while taking into account the fundamental differences in the natures of the two operating modes (such as the batch advantage of utilizing reactors for the manufacture of multiple products, or the batch disadvantage of reactor cleanup between campaigns), the size and cost of the respective reactor units, and the potential use of reactor networks to optimize performance. I develop a first-principles-based non-dimensionalization algorithm that unifies the model for all reactor types and chemical systems from the two operating modes which enables direct performance comparisons between reactors of the two operating modes. In addition, I introduce a novel discretization method, the orthogonal collocation on finite elements for reactors (OCFERE), that allows the consideration of networks of reactors of either of the two operating modes, and I unify the description of the economics of the two operating modes. This results in a framework that encompasses the solution of a single optimization problem to make the decision about operating mode and find the optimal reactor network design. In the second part of the dissertation, I address the problem of quantifying the monetary value of improvements in process control. While methods have been developed for quantifying the value of control in the case of predominantly steady-state processes, there has been no attempt to quantify the monetary value of control for predominantly transient processes. I first review the problem, and highlight the relationship between optimal scheduling and process control for transient processes. Then, I utilize the general framework of integrated scheduling and control to develop novel performance functions that enable the quantification of the monetary value of control from a scheduling perspective for a predominantly transient process. I posit that the transition time between one product and the next in a production sequence can be used as a performance metric over which the value of control can be quantified.
dc.description.departmentChemical Engineering
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2152/89270
dc.language.isoen
dc.subjectProcess systems engineering
dc.subjectMathematical optimization
dc.subjectProcess synthesis
dc.subjectReactor network synthesis
dc.subjectBatch to continuous
dc.subjectProcess economics
dc.subjectValue of control
dc.subjectProduction scheduling
dc.subjectProcess control
dc.subjectEconomic benefit analysis
dc.subjectProcess intensification
dc.subjectChemical reactor design
dc.subjectOrthogonal collocation on finite elements
dc.subjectNumerical methods
dc.titleDecision-making frameworks for practical industrial applications in optimal process design and control
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentChemical Engineering
thesis.degree.disciplineChemical Engineering
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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