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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Dedication - p. v 7. Preface - p. xiii 8. Acknowledgments - p. xv 9. 1. Industrial solid ashes generation - p. 1 10. 2. Properties of industrial solid ashes - p. 21 11. 3. Ash management, recycling, and sustainability - p. 47 12. 4. Emerging innovative techniques for ash management - p. 69 13. 5. Legal framework for ashes - p. 79 14. 6. Background of machine learning - p. 93 15. 7. Machine learning modeling methodology for industrial solid ash - p. 131 16. 8. The application of clustering algorithms for industrial solid ashes based on physicochemical properties - p. 183 17. 9. The accurate production forecast of solid ashes: application and comparison of machine learning techniques - p. 193 18. 10. FIELD: fast mobility evaluation and environmental index for solid ashes with machine learning - p. 209 19. 11. Identifying the amorphous content in solid ashes: a machine learning approach using an international dataset - p. 225 20. 12. The reactivity classification of coal fly ash based on the random forest method - p. 239 21. 13. Forecasting the uniaxial compressive strength of solid ash-based concrete - p. 253 22. 14. Challenges and future perspectives of machine learning in industrial solid ashes management - p. 265 23. Appendix - p. 271 24. Index - p. 289
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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Dedication - p. v 7. Preface - p. xiii 8. Acknowledgments - p. xv 9. 1. Industrial solid ashes generation - p. 1 10. 2. Properties of industrial solid ashes - p. 21 11. 3. Ash management, recycling, and sustainability - p. 47 12. 4. Emerging innovative techniques for ash management - p. 69 13. 5. Legal framework for ashes - p. 79 14. 6. Background of machine learning - p. 93 15. 7. Machine learning modeling methodology for industrial solid ash - p. 131 16. 8. The application of clustering algorithms for industrial solid ashes based on physicochemical properties - p. 183 17. 9. The accurate production forecast of solid ashes: application and comparison of machine learning techniques - p. 193 18. 10. FIELD: fast mobility evaluation and environmental index for solid ashes with machine learning - p. 209 19. 11. Identifying the amorphous content in solid ashes: a machine learning approach using an international dataset - p. 225 20. 12. The reactivity classification of coal fly ash based on the random forest method - p. 239 21. 13. Forecasting the uniaxial compressive strength of solid ash-based concrete - p. 253 22. 14. Challenges and future perspectives of machine learning in industrial solid ashes management - p. 265 23. Appendix - p. 271 24. Index - p. 289
