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

Abstract

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. I would first like to express my sincere gratitude to my committee chair, Dr. Aaron Noble for his expert guidance, understanding, support, and encouragement. While Dr. Noble is technically my advisor, his charisma, wisdom, and astute analytical reasoning have placed his role much closer to being a mentor and friend. Given his spirit of adventure in regard to research and genuine excitement in the betterment of others, Dr. Noble has established himself as a role model to to both his students and peers. Next, I would like to thank my committee members Dr. Keith Heasley and Dr. Mark Sindelar for their time, advice, and insightful comments. They have provided a wealth of knowledge, which I will never forget. Both Fernando Campos and Lucas Santos assisted with the generation of much of the data presented in this thesis. Their hard work and painstaking effort helped to make this project successful. Fernando and Lucas are both owed a debt of gratitude for their help and support. This work would not have been accomplished if not for my lovely wife Rachel. Her encouragement, quiet patience, and unwavering love were undeniably the bedrock upon which the last eight years of my life have been built. Rachel's tolerance for my occasional crude temperament is a testament in itself of her unyielding devotion and love. Finally, I would like to thank my father Casey. Dad has taught me the value of a solid work ethic and provided an example of a high ethical standard which has been the foundation of the many accomplishments I have achieved. Without his guidance and support I would undoubtedly be far less of a man than I am today.