Abstract: Permeable reactive barriers (PRBs) are used for groundwater remediation at contaminated sites worldwide. This technology has been efficient at appropriate sites for treating organic and inorganic contaminants using zero-valent iron (ZVI) as a reductant and as a reactive material. Continued development of the technology over the years suggests that a robust understanding of PRB performance and the mechanisms involved is still lacking. Conflicting information in the scientific literature downplays the critical role of ZVI corrosion in the remediation of various organic and inorganic pollutants. Additionally, there is a lack of information on how different mechanisms act in tandem to affect ZVI-groundwater systems through time. In this review paper, we describe the underlying mechanisms of PRB performance and remove isolated misconceptions. We discuss the primary mechanisms of ZVI transformation and aging in PRBs and the role of iron corrosion products. We review numerous sites to reinforce our understanding of the interactions between groundwater contaminants and ZVI and the authigenic minerals that form within PRBs. Our findings show that ZVI corrosion products and mineral precipitates play critical roles in the long-term performance of PRBs by influencing the reactivity of ZVI. Pore occlusion by mineral precipitates occurs at the influent side of PRBs and is enhanced by dissolved oxygen and groundwater rich in dissolved solids and high alkalinity, which negatively impacts hydraulic conductivity, allowing contaminants to potentially bypass the treatment zone. Further development of site characterization tools and models is needed to support effective PRB designs for groundwater remediation.

Abstract: The migration process of leached uranium in the in-situ leaching of uranium is considered a typical reactive transport problem. During this process, the lixiviant concentration and injection rate are important in-situ leaching process parameters (ISLPP) to efficiently recover uranium. However, several uncertain factors affect the outcomes of the ISLPP design. In addition, the repeated use of the reactive transport model (RTM) for investigating the acid in-situ leaching of uranium with the application of the Monte Carlo method leads to a substantial computational load. For this reason, a machine learning (ML)–based surrogate model was developed with the backpropagation neural network (BPNN) method to replace the RTM under the condition of uncertain parameters. Moreover, the simulated annealing optimisation model for ISLPP was created based on the proposed surrogate model. The optimal ISLPP was achieved that generated maximum profits from uranium recovery under different lixiviant prices, uranium prices and exploitation times. The optimal design framework of ISLPP based on the proposed ML algorithm was then applied in the Bayan-Uul sandstone-type uranium deposit in Inner Mongolia, China. From our analysis, it was demonstrated that the ML-based surrogate model exhibited great fitness with the RTM. The optimal results of the ISLPP indicated that the lixiviant concentration and injection rate could be adjusted based on the fluctuations in lixiviant price, uranium price and exploitation time. If the prices of sulphuric acid were high, a specific concentration of hydrogen peroxide could be injected into the injection well to promote the oxidation and dissolution of the uranium ore to increase the income from the uranium recovery. The optimisation model can also use the ISLPP scheme to boost the revenues from different lixiviant prices, uranium prices and exploitation times under the uncertainty of porosity, illustrating the applicability of the ML-based optimal design method of ISLPP in ISL mining. A general framework for developing surrogate models, as well as for conducting uncertainty analyses for a wide range of groundwater models was proposed here yielding valuable insights.