Machine Learning Applications in Industrial Solid Ash

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. - Helps readers increase their existing knowledge on data mining and ML - Teaches how to apply ML techniques that work best in solid ash management and recycling through providing illustrative examples and complex practice solutions - Provides an accessible introduction to the current state and future possibilities for ML in solid ash management and recycling

Chongchong Qi is a professor in School of Resources and Safety Engineering at the Central South University. Prof. Qi's research and writing is focused in the areas of solids waste management in the mining industry, innovative strategies for solids waste reusing and recycling, artificial intelligence, and its applications in the mining. With more than 70 high-quality SCI papers being published, Prof. Qi announces the idea of 'intelligent design system for backfill mining'. He serves on various international committees and works as the editorial board member for three SCI journals. Prof. Qi also received various funds home and abroad for the innovative application of AI in the mining industry.