Authors
Yuan Yu, Baoli Wu, Zhiliang Gao, Nanqi Ren, Shijie You
Published in
Environmental science & technology. Volume 60. Issue 44. Pages 31721-31732. Nov 10, 2026.
Abstract
Electrokinetic remediation (EKR) holds promise for in situ cleanup of heavy-metal-contaminated soils, yet rational design has been hampered by the undetectability of process parameters and physical fields. This study reported the inversion of EKR process parameters and electro-hydro-chemical (EHC) fields from limited-view and sparse data by physics-informed neural networks that embed the coupled Richards-Nernst-Planck equations (RNP-PINNs). Reaction-transport stiffness in the RNP equation was mitigated by local-equilibrium formulation, field-wise subnetworks, physics-based nondimensionalization, and sequentially coupled optimization. Pointwise pH values and endpoint total-Pb data were used for inversion of half-adsorption pH (pH50), dimensionless electroosmotic scaling factor (Keos), and H+ buffering coefficient (RH), together with the neural-network parameters. The parameters were tightly clustered across 500 bootstrap refits (Keos = 0.8-0.9, RH = 18.5-19.2, pH50 = 2.9-3.2), revealing weak pairwise compensation (absolute Pearson correlations ≤0.13). This allowed inversion of the spatiotemporal EHC fields with RMSEs of 1.3 × 10-1 mol m-3 d-1 for total-Pb transport and 2 × 10-3 d-1 for Richards equation residuals. The RNP-PINNs offered mechanistic insights into the way saturation reorganized pore-water transport, accelerated acid-front propagation, and promoted Pb redistribution and re-adsorption near the pH front. Compared with conventional theoretical numerical modeling or experimental investigation, this study provides a "theory + data" double-driven paradigm for the inversion of EKR processes.
PMID:
43007174
Bibliographic data and abstract were imported from PubMed on 10 Nov 2026.
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