Offering the ability to process large or complex datasets, machine learning (ML) holds huge potential to reshape the whole status for solid ash management and recycling. Machine Learning for Solid Ash Management and Recycling is, as far as the author knows, the first published book about ML in solid ash management and recycling. This book highlights fundamental knowledge and recent advances in this topic, offering readers new insight into how these tools can be utilized to enhance their own work. The reference begins with fundamentals in solid ash, covering the status of solid ash generation and management. The book moves on to foundational knowledge on ML in solid ash management, which provides a brief introduction of ML for solid ash applications. The reference then goes on to discuss ML approaches currently used to address problems in solid ash management and recycling, including solid ash generation, clustering analysis, origin identification, reactivity prediction, leaching potential modelling and metal recovery evaluation, etc. Finally, potential future trends and challenges in the field are discussed.

This book provides an overview of paste tailings disposal at mine sites. It deals comprehensively with the characterization of sulphide-rich tailings, geotechnical and microstructural behaviour, surface tailings disposal applications, underground paste backfilling, and case studies. The authors place emphasis on the characterization, monitoring, disposal and treatment, as well as environmental considerations of problematic sulphidic tailings. The framework is supported by worldwide case studies.

This book provides an overview of paste tailings disposal at mine sites. It deals comprehensively with the characterization of sulphide-rich tailings, geotechnical and microstructural behaviour, surface tailings disposal applications, underground paste backfilling, and case studies. The authors place emphasis on the characterization, monitoring, disposal and treatment, as well as environmental considerations of problematic sulphidic tailings. The framework is supported by worldwide case studies.