Title:
Machine learning approaches for considering decentralized EVB pre-processing facilities with respect to end-use sectors and a potential second-use location
Machine learning approaches for considering decentralized EVB pre-processing facilities with respect to end-use sectors and a potential second-use location
Author(s)
Haynes, Megan
Advisor(s)
Hatzell, Marta C.
Editor(s)
Collections
Supplementary to
Permanent Link
Abstract
Lithium-Ion Batteries (LIBs) at End-of-Life (EoL) pose several safety risks, as LIBs
have the potential to self-ignite during transportation, release toxic compounds during incineration, and can leach contaminants into landfills. To reduce these safety risks in US,
LIBs are labeled Class 9 hazardous materials under the Code of Federal Regulations. This
causes LIBs to be subject to numerous policies, including the requirement of certified personnel and companies to pack and ship the items, and regulatory processing with government agencies involved in transport. Efforts to improve LIB recycling focus on reducing
costs to make recycling economically lucrative. Hence, there is a significant emphasis on
improving recycling processes; however, transport cost alone has been identified to be on
average 41% of the total cost of LIB recycling.
This thesis aims to provide a methodology for choosing a Spatially Constrained Multivariate Clustering Analysis (SCMCA) heuristic which could be implemented in a variety
of applications. A case study is analyzed which investigates network optimization for potential decentralized pre-processing facility locations of Electric Vehicle Batteries (EVBs)
in California. The decentralized facilities aim to minimize the transportation distance and
costs of shipping intact EVBs between end-use sectors, the facilities, and potential seconduse locations.
The methodology consists of a clustering analysis comparison of unsupervised SCMCA
Machine Learning heuristics followed by location analyses of potential pre-processing facilities. The freight capacity of the solutions under different transportation scenarios is
utilized as the primary criteria to determine an appropriate SCMCA heuristic for the case
study. Following this, a sensitivity analysis is implemented to evaluate the volatility of the
solutions presented by the various heuristics. Finally, a staged development scenario is proposed for the construction and expansion of facilities in California to manage the increasing
rate of EoL EVBs from 2024 to 2030.
Sponsor
Date Issued
2022-08-03
Extent
Resource Type
Text
Resource Subtype
Thesis