Harnessing Automation and Machine Learning for Resource Recovery and Value Creation: From Waste to Value provides a comprehensive understanding of how automation and machine learning technologies can be used to convert waste into valuable resources. This book gives insight in the opportunities offered by automation and machine learning technologies in waste management and how they can help address the challenges associated with waste management and to discuss the benefits and potential of automation technologies. It examines the potential of machine learning algorithms in analyzing waste management data, identifying patterns, predicting future waste generation, and optimizing waste management processes. Moreover, this book showcases case studies from different industries and regions, highlighting the revolutionary applications of automation and machine learning in waste management. This book is an indispensable resource for researchers, waste management professionals, and policymakers interested in learning more about how automation and machine learning can contribute to waste management and the creation of a sustainable future.
1. Introduction - p. xxiii 2. Chapter 1. Introduction to innovative technologies for waste-to-energy conversion using automation and machine learning - p. 1 3. Chapter 2. Basics of machine learning - p. 29 4. Chapter 3. Basics of automation - p. 65 5. Chapter 4. Waste classification into plastics, industrial, domestic, and agriculture waste - p. 91 6. Chapter 5. Plastics recycling and the automation role in the recycling process - p. 115 7. Chapter 6. Artificial intelligence for reutilizing the plastics - p. 165 8. Chapter 7. Handling metals waste to salvage with automation - p. 187 9. Chapter 8. Machine learning: a better means for metal waste to reprocess - p. 213 10. Chapter 9. Waste classification using convolutional neural networks tuned by modified metaheuristics algorithm - p. 237 11. Chapter 10. Exploring the potential of lightweight computer vision YOLOv8 models for effective waste classification and management - p. 263 12. Chapter 11. Waste to value added: role of automation in organic waste - p. 283 13. Chapter 12. Future of agriculture: smart vertical farming - p. 339 14. Chapter 13. Impact of artificial intelligence for the recycling of organic waste - p. 347 15. Chapter 14. Agriculture: the next machine-learning frontier - p. 363 16. Chapter 15. Recycling robots to tackle electrical waste - p. 381 17. Chapter 16. Machine learning for sustainable development in electronics - p. 403 18. Chapter 17. Automated sorting of recyclable domestic waste - p. 417 19. Chapter 18. Intelligent waste management: a comprehensive review of machine learning and deep learning applications in advanced recycling - p. 427 20. Chapter 19. Future aspects of machine learning/automation for the waste management - p. 461 21. Glossary - p. 475 22. Index - p. 487
Harnessing Automation and Machine Learning for Resource Recovery and Value Creation: From Waste to Value provides a comprehensive understanding of how automation and machine learning technologies can be used to convert waste into valuable resources. This book gives insight in the opportunities offered by automation and machine learning technologies in waste management and how they can help address the challenges associated with waste management and to discuss the benefits and potential of automation technologies. It examines the potential of machine learning algorithms in analyzing waste management data, identifying patterns, predicting future waste generation, and optimizing waste management processes. Moreover, this book showcases case studies from different industries and regions, highlighting the revolutionary applications of automation and machine learning in waste management. This book is an indispensable resource for researchers, waste management professionals, and policymakers interested in learning more about how automation and machine learning can contribute to waste management and the creation of a sustainable future.
1. Introduction - p. xxiii 2. Chapter 1. Introduction to innovative technologies for waste-to-energy conversion using automation and machine learning - p. 1 3. Chapter 2. Basics of machine learning - p. 29 4. Chapter 3. Basics of automation - p. 65 5. Chapter 4. Waste classification into plastics, industrial, domestic, and agriculture waste - p. 91 6. Chapter 5. Plastics recycling and the automation role in the recycling process - p. 115 7. Chapter 6. Artificial intelligence for reutilizing the plastics - p. 165 8. Chapter 7. Handling metals waste to salvage with automation - p. 187 9. Chapter 8. Machine learning: a better means for metal waste to reprocess - p. 213 10. Chapter 9. Waste classification using convolutional neural networks tuned by modified metaheuristics algorithm - p. 237 11. Chapter 10. Exploring the potential of lightweight computer vision YOLOv8 models for effective waste classification and management - p. 263 12. Chapter 11. Waste to value added: role of automation in organic waste - p. 283 13. Chapter 12. Future of agriculture: smart vertical farming - p. 339 14. Chapter 13. Impact of artificial intelligence for the recycling of organic waste - p. 347 15. Chapter 14. Agriculture: the next machine-learning frontier - p. 363 16. Chapter 15. Recycling robots to tackle electrical waste - p. 381 17. Chapter 16. Machine learning for sustainable development in electronics - p. 403 18. Chapter 17. Automated sorting of recyclable domestic waste - p. 417 19. Chapter 18. Intelligent waste management: a comprehensive review of machine learning and deep learning applications in advanced recycling - p. 427 20. Chapter 19. Future aspects of machine learning/automation for the waste management - p. 461 21. Glossary - p. 475 22. Index - p. 487
