A Critical Assessment on the Resources and Extraction of Rare Earth Elements from Acid Mine Drainage

Rare earth elements (REEs) are crucial to many modern products used in both civilian and defense applications. Currently, a reliable supply of these elements is uncertain with the clear majority of REE production and refining occurring predominately in China. Furthermore, domestic ore deposits with commercially attractive concentrations of REEs are uncommon in the United States. As a result, the identification of a domestic supply of these technology metals is essential not only for manufacturing consumer merchandise but also for national security. Recently, one promising source of REEs has been identified: coal and coal-byproducts. One of those is acid mine drainage (AMD), the most prevalent water quality impediment in the Appalachian coal mining region. This research found that AMD concentrates REEs through an autogenous process where the presence of sulfide material in an oxidizing environment results in a general lowering of water pH. This acidic water in turn leaches metals, including REEs, from the surrounding geologic strata. Accordingly, this degraded water holds potential value as a REE source. Furthermore, identification of this environmental burden as a reliable supply of REEs could incentivize additional treatment efforts, while providing an additional revenue stream to those responsible for mitigating this substantial source of water pollution. However, current scientific literature lacks systemic studies that describe the content, distribution, and processing amenability of this resource. Therefore, this research details a study that: (1) characterized the REEs contained in AMD and its byproducts; (2) classified the REEs inherent to AMD and identified the size of the resource; (3) designed a process to recover REEs from AMD byproducts; and (4) demonstrated the feasibility of the beneficiation process by generating a concentrated REE product from AMD. This was accomplished by conducting a broad sampling campaign where 185 raw AMD and 623 AMD precipitate (AMDp) samples were collected across the Northern and Central Appalachian coal basins. Next, a series of laboratory experiments were conducted to determine a hydrometallurgical processing route to recover the REEs from AMDp. The results of the laboratory-scale studies were utilized to design a bench-scale plant capable of producing a concentrated REE product. Finally, an acid leaching and solvent extraction demonstration plant was constructed and operated which produced a rare earth oxide product with a purity greater than 60%

A Feasibility Study for the Automated Monitoring and Control of Mine Water Discharges

The chemical treatment of mine-influenced waters is a longstanding environmental challenge for many coal operators, particularly in Central Appalachia. Mining conditions in this region present several unique obstacles to meeting NPDES effluent limits. Outlets that discharge effluent are often located in remote areas with challenging terrain where conditions do not facilitate the implementation of large-scale commercial treatment systems. Furthermore, maintenance of these systems is often laborious, expensive, and time consuming. Many large mining complexes discharge water from numerous outlets, while using environmental technicians to assess the water quality and treatment process multiple times per day. Unfortunately, this treatment method when combined with the lower limits associated with increased regulatory scrutiny can lead to the discharge of non-compliant water off of the mine permit. As an alternative solution, this thesis describes the ongoing research and development of automated protocols for the treatment and monitoring of mine water discharges. In particular, the current work highlights machine learning algorithms as a potential solution for pH control.;In this research, a bench-scale treatment system was constructed. This system simulates a series of ponds such as those found in use by Central Appalachian coal companies to treat acid mine drainage. The bench-scale system was first characterized to determine the volumetric flow rates and resident time distributions at varying flow rates and reactor configurations. Next, data collection was conducted using the bench scale system to generate training data by introducing multilevel random perturbations to the alkaline and acidic water flow rates. A fuzzy controller was then implemented in this system to administer alkaline material with the goal of automating the chemical treatment process. Finally, the performance of machine learning algorithms in predicting future water quality was evaluated to identify the critical input variables required to build these algorithms. Results indicate the machine learning controllers are viable alternatives to the manual control used by many Appalachian coal producers