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Record W4407636418 · doi:10.1016/j.jclepro.2025.145054

Digitalization-driven circular economy in battery closed-loop supply chain network design

2025· article· en· W4407636418 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Cleaner Production · 2025
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsCircular economySupply chainLoop (graph theory)Battery (electricity)Closed loopSupply chain networkChain (unit)BusinessIndustrial organizationEngineeringEconomicsSupply chain managementControl engineeringMathematicsPower (physics)MarketingPhysics

Abstract

fetched live from OpenAlex

Nowadays, Lithium-ion Batteries (LIBs) are growingly utilized in a wide range of products (e.g., electric vehicles) due to their superiority over all types of rechargeable batteries. The amount of valuable returned LIBs in various situations has increased dramatically as LIBs production has grown. As the collection and separation of returned LIBs have many challenges (e.g., identifying LIBs’ characteristics), using an Internet of Things (IoT)-based system could effectively address the problem of categorizing returned LIBs. This study aims to design the LIBs closed-loop supply chain to treat LIBs in different stages, including collection, separation, and recycling, to address the potential socio-environmental concerns. To do this, a multi-objective programming model is proposed to reduce environmental impacts and maximize social outcomes while minimizing total costs. Then, an integrated solution approach is developed encompassing four main phases: (i) Developing a digital transformation strategy to implement an IoT-based system, (ii) Employing a Partitioning Around Medoids (PAM)-Build algorithm to cluster the returned LIBs into repurposable returned LIBs, recyclable returned LIBs, and unrecyclable returned LIBs, (iii) Proposing an adaptive data-driven robust optimization to overcome the uncertainties of the studied problem, (iv) Developing an augmented epsilon-constraint method based on extracting efficient spaces. The results imply that increasing the rate of repurposable returned LIBs leads to worsening the values of the economic objective function (i.e., over 28%) and environment objective function (i.e., over 8%). Furthermore, transportation modes “Euro IV heavy-duty truck” and “Euro V heavy-duty truck” are more applicable in the proposed model since other ones are used when economic and environment-related objective functions are at their best values. • Proposing a framework for adopting digital transformation strategies in the battery industry. • Incorporating an IoT-based system into recycling centers within battery supply chains. • Addressing socioenvironmental issues in designing a battery closed-loop supply chain. • Developing a PAM-Build algorithm to cluster the quality of returned Lithium-ion batteries. • Specifying a trade-off surface of design objectives using an augmented epsilon constraint.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.791
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.230
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it