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Title: A chemistry-informed hybrid machine learning approach to predict metal adsorption onto mineral surfaces

Journal Article · · Applied Geochemistry
ORCiD logo [1];  [1];  [2];  [3]
  1. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
  2. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
  3. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States)
+ Show Author Affiliations

Historically, surface complexation model (SCM) constants and distribution coefficients (Kd) have been employed to quantify mineral-based retardation effects controlling the fate of metals in subsurface geologic systems. Our recent SCM development workflow, based on the Lawrence Livermore National Laboratory Surface Complexation/Ion Exchange (L-SCIE) database, illustrated a community FAIR data approach to SCM development by predicting uranium(VI)-quartz adsorption for a large number of literature-mined data. Here, we present an alternative hybrid machine learning (ML) approach that shows promise in achieving equivalent high-quality predictions compared to traditional surface complexation models. At its core, the hybrid random forest (RF) ML approach is motivated by the proliferation of incongruent SCMs in the literature that limit their applicability in reactive transport models. Our hybrid ML approach implements PHREEQC-based aqueous speciation calculations; values from these simulations are automatically used as input features for a random forest (RF) algorithm to quantify adsorption and avoid SCM modeling constraints entirely. Named the LLNL Speciation Updated Random Forest (L-SURF) model, this hybrid approach is shown to have applicability to U(VI) sorption cases driven by both ion-exchange and surface complexation, as is shown for quartz and montmorillonite cases. The approach can be applied to reactive transport modeling and may provide an alternative to the costly development of self-consistent SCM reaction databases.

Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER). Earth & Environmental Systems Science (EESS); USDOE Office of Nuclear Energy (NE), Office of Spent Fuel and Waste Disposition. Office of Spent Fuel and Waste Science and Technology
Grant/Contract Number:
SCW1053; AC52-07NA27344; AC02-05CH11231
OSTI ID:
1989507
Alternate ID(s):
OSTI ID: 1992184
Report Number(s):
LLNL-JRNL-838970; S0883292723001762; 105731; PII: S0883292723001762
Journal Information:
Applied Geochemistry, Journal Name: Applied Geochemistry Vol. 155; ISSN 0883-2927
Publisher:
ElsevierCopyright Statement
Country of Publication:
United Kingdom
Language:
English

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Related Subjects

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEAR CHEMISTRY
Adsorption
Metal
Mineral
Surface complexation modeling
Random forest
Hybrid machine learning