Xing, W.W., Chen, H., Chen, Z. et al. (5 more authors) (2025) Adaptive LCI data completion: Integrating neural processes and active learning for enhanced life cycle assessment. In: Mativenga, P. and Gallego-Schmid, A., (eds.) Procedia CIRP. 32nd CIRP Conference on Life Cycle Engineering (LCE 2025), 07-09 Apr 2025, Manchester, United Kingdom. Elsevier , pp. 136-141.
Abstract
Accurate and comprehensive Life Cycle Inventory (LCI) data underpins the reliability and accuracy of Life Cycle Assessment (LCA) results. However, LCI data is often incomplete due to data unavailability, which affects the reliability and accuracy of LCA results. To address this issue, this paper introduces a novel approach for LCI data completion based on Neural Processes (NPs) combined with active learning for efficient adaptive refinement of LCI data completion. Experimental results demonstrate that the proposed approach outperforms the state-of-the-art XGBoost-based method significantly, achieving up to 99% improvement in prediction accuracy. This means that by reducing data requirements by approximately 50% whilst improving predictive accuracy, the proposed AI model can provide more reliable LCA results in less time.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) |
Keywords: | Life Cycle Assessment; Life Cycle Inventory; Neural Processes; Active Learning |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > University of Sheffield Research Centres and Institutes > AMRC with Boeing (Sheffield) The University of Sheffield > Advanced Manufacturing Institute (Sheffield) > AMRC with Boeing (Sheffield) |
Funding Information: | Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL TBC |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 18 Jul 2025 13:50 |
Last Modified: | 18 Jul 2025 14:53 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.procir.2025.01.046 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:229416 |