Combining a physics-informed broad learning system and multimodal spectral fusion for accurate analysis of chromium speciation in tailings
A novel analytical framework is presented that combines a physics-informed broad learning system network (PI-BLS-Net) with multimodal spectral fusion to enable rapid, low-cost, and interpretable chromium speciation in industrial tailings.
Authors
- Shubin Lyu (ORCID: https://orcid.org/0000-0002-4454-4646)
- Qingya Wang (ORCID: https://orcid.org/0000-0001-5636-8785)
- Fusheng Li (ORCID: https://orcid.org/0000-0001-7491-5535)
- Huijuan Zou
Institutions
- Jiujiang University (CN)
- University of Electronic Science and Technology of China (CN)
- Huzhou Normal University (CN)
- Jiangxi University of Technology (CN)
- Jiujiang Vocational University (CN)
- East China University of Technology (CN)
Publication Details
- Journal
- Journal of Analytical Atomic Spectrometry
- Published
- 2025-12-03
- DOI
- https://doi.org/10.1039/d5ja00383k
- Primary Topic
- Tailings Management and Properties
- Type
- article
- Field-Weighted Citation Impact
- 0.00