The enrichment and migration of geogenic high-arsenic contaminated groundwater are controlled by complex hydrological conditions and hydrogeochemical processes.Conventional reactive transport models encounter dual cha...The enrichment and migration of geogenic high-arsenic contaminated groundwater are controlled by complex hydrological conditions and hydrogeochemical processes.Conventional reactive transport models encounter dual challenges,including poor convergence and low computational efficiency,when simulating multi-pathway reaction networks.To address these limitations,this study develops an improved physics-informed neural network(PH-PINNs)by integrating physical constraints with the PHREEQC geochemical module.The proposed model explicitly accounts for microbially mediated reaction networks involving iron-sulfur-carbon-nitrogen cycles,which drive arsenic enrichment and mobilization in groundwater.Leveraging field monitoring data from the Shanyin experimental site in the Datong Basin,Shanxi Province,a two-dimensional numerical model was constructed to simulate site-scale arsenic reactive transport.Results indicate that the PH-PINNs model significantly outperforms traditional PINNs in capturing nonlinear and non-stationary dynamics,reducing the root mean square error(RMSE)by over 50%.The model exhibits superior predictive accuracy,numerical stability,and adaptability for complex,multi-component reaction networks.This framework provides a robust tool for advancing both theoretical research and practical management of arsenic-contaminated groundwater systems.展开更多
基金supported by the National Key Research and Development Program of China(Grant No.2021YFA0715903)the National Natural Science Foundation of China(Grant Nos.42330718,42472311)。
文摘The enrichment and migration of geogenic high-arsenic contaminated groundwater are controlled by complex hydrological conditions and hydrogeochemical processes.Conventional reactive transport models encounter dual challenges,including poor convergence and low computational efficiency,when simulating multi-pathway reaction networks.To address these limitations,this study develops an improved physics-informed neural network(PH-PINNs)by integrating physical constraints with the PHREEQC geochemical module.The proposed model explicitly accounts for microbially mediated reaction networks involving iron-sulfur-carbon-nitrogen cycles,which drive arsenic enrichment and mobilization in groundwater.Leveraging field monitoring data from the Shanyin experimental site in the Datong Basin,Shanxi Province,a two-dimensional numerical model was constructed to simulate site-scale arsenic reactive transport.Results indicate that the PH-PINNs model significantly outperforms traditional PINNs in capturing nonlinear and non-stationary dynamics,reducing the root mean square error(RMSE)by over 50%.The model exhibits superior predictive accuracy,numerical stability,and adaptability for complex,multi-component reaction networks.This framework provides a robust tool for advancing both theoretical research and practical management of arsenic-contaminated groundwater systems.
