Title:
Systematic Design of Bulk Recycling Systems under Uncertainty

dc.contributor.advisor Realff, Matthew J.
dc.contributor.author Wei, Jing en_US
dc.contributor.committeeMember Ammons, Jane
dc.contributor.committeeMember Gallivan, Martha
dc.contributor.committeeMember Lee, Jay
dc.contributor.committeeMember Schork, F Joseph
dc.contributor.department Chemical Engineering en_US
dc.date.accessioned 2005-03-02T22:11:44Z
dc.date.available 2005-03-02T22:11:44Z
dc.date.issued 2004-05-13 en_US
dc.description.abstract The fast growing waste stream of electronic and other complex consumer products is making the bulk recycling problem an important environmental protection issue. These products must be recycled because they contain hazardous materials such as lead and mercury. The focus of this thesis is the development of systematic methods for designing systems to recover mixed plastics from electronic products such as computers and televisions. Bulk recycling systems are similar to other chemical engineering process systems. Therefore they can be synthesized and designed using some existing techniques that have been applied to distillation and reaction systems. However, the existence of various uncertainties from different sources, such as the variation of component fractions and product prices, makes it crucial to design a flexible and sustainable system, and is also a major challenge in this research. Another challenge is that plastics can be separated by different mechanisms based on different properties, but separating a mix of plastics often requires using a combination of different methods because they can have overlapping differentiating properties. Therefore many decisions are to be made including which methods to choose and how to connect them. To address the problem systematically, the design under uncertainty problem was formulated as a stochastic Mixed Integer Nonlinear Program (sMINLP). A Sample Average Approximation (SAA) method wrapped on the Outer Approximation method has been developed in this thesis to solve such problems efficiently. Therefore, large design under uncertainty problems can be solved without intractable computational difficulty. To allow making choices from separation methods by different mechanisms, this research modeled various plastics separation methods taking account of the distribution of particle properties and unified them using a canonical partition curve representation. Finally, an overall design method was proposed in this work to incorporate the design of size reduction units into the separation system. This research is the first formal development of a systematic method in this area to account for uncertainties and interactions between process steps. en_US
dc.description.degree Ph.D. en_US
dc.format.extent 850791 bytes
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/4978
dc.language.iso en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject Optimization en_US
dc.subject Bulk recycling
dc.subject Design under uncertainty
dc.subject Sample average approximation
dc.subject MINLP
dc.subject.lcsh Plastics Recycling en_US
dc.title Systematic Design of Bulk Recycling Systems under Uncertainty en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Realff, Matthew J.
local.contributor.corporatename School of Chemical and Biomolecular Engineering
local.contributor.corporatename College of Engineering
relation.isAdvisorOfPublication dd9d1ca6-3293-43de-b18b-37fc87261bfb
relation.isOrgUnitOfPublication 6cfa2dc6-c5bf-4f6b-99a2-57105d8f7a6f
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Wei_Jing_200408_phd.pdf
Size:
830.85 KB
Format:
Adobe Portable Document Format
Description: