Optimal Membrane Cascade Design for Critical Mineral Recovery Through Logic-based Superstructure Optimization
- Carnegie Mellon University (CMU)
Critical minerals and rare earth elements play an important role in our climate change initiatives, particularly in applications related with energy storage. Here, we use discrete optimization approaches to design a process for the recovery of Lithium and Cobalt from battery recycling, through membrane separation. Our contribution involves proposing a Generalized Disjunctive Programming (GDP) model for the optimal design of a multistage diafiltration cascade for Li-Co separation. By solving the resulting nonconvex mixed-integer nonlinear program model to global optimality, we investigated scalability and solution quality variations with changes in the number of stages and elements per stage. Results demonstrate the computational tractability of the nonlinear GDP formulation for design of membrane separation processes while opening the door for decom-position strategies for multicomponent separation cascades. Future work aims to extend the GDP formulation to account for stage installation and explore various decomposition techniques to enhance solution efficiency.
- Research Organization:
- National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)
- Sponsoring Organization:
- USDOE Office of Fossil Energy and Carbon Management (FECM)
- OSTI ID:
- 2439595
- Country of Publication:
- United States
- Language:
- English
Similar Records
Optimal Membrane Cascade Design for Critical Mineral Recovery through Logic-based Superstructure Optimization
Pyomo.GDP: an ecosystem for logic based modeling and optimization development
Linear model decision trees as surrogates in optimization of engineering applications
Conference
·
Sun Jul 14 00:00:00 EDT 2024
·
OSTI ID:2439776
Pyomo.GDP: an ecosystem for logic based modeling and optimization development
Journal Article
·
Fri Apr 23 00:00:00 EDT 2021
· Optimization and Engineering
·
OSTI ID:1781543
Linear model decision trees as surrogates in optimization of engineering applications
Journal Article
·
Thu Jul 13 00:00:00 EDT 2023
· Computers and Chemical Engineering
·
OSTI ID:2311619