An innovative data-driven approach to the design and optimization of battery recycling processes
Loading...
Access rights
openAccess
CC BY
CC BY
publishedVersion
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.
Date
Major/Subject
Mcode
Degree programme
Language
en
Pages
11
Series
Chemical Engineering Journal, Volume 510
Abstract
With the growing demand for raw materials to enable the ongoing electrification transition, robust battery recycling technologies will also become necessary to reduce reliance on primary resources and improve sustainability. To boost the recovery of secondary materials, we combined HSC-Sim® recycling process simulations with data science to analyze the flow of Li-ion battery components through the processing stages. Key operating parameters of the process were varied to assess their impact on material recovery and grade of graphite anode (Gr) and nickel-manganese-cobalt cathode (NMC). The resulting data distributions allowed us to establish if the process design was capable of producing desired recovery outcomes, and under which set of conditions optimal performance could be obtained. Materials flow analysis was utilized to guide decision-making and iteratively redesign the recycling process towards better outcomes. In the final stage, multi-objective optimization was deployed to achieve a balance between maximal NMC mass recovery of 66.3% at 95.7% grade and Gr mass recovery of 88.7% with 99.8% grade. This scalable, data-driven framework could replace intuition-led recycling process trials with rational process design to optimize complex device recycling, accelerating the transition towards more sustainable and effective material recycling.Description
Publisher Copyright: © 2025 The Author(s)
Keywords
Other note
Citation
Emami, N, Gomez-Moreno, L A, Klemettinen, A, Serna-Guerrero, R & Todorović, M 2025, 'An innovative data-driven approach to the design and optimization of battery recycling processes', Chemical Engineering Journal, vol. 510, 161128. https://doi.org/10.1016/j.cej.2025.161128