
Harnessing Automation and Machine Learning for Resource Recovery and Value Creation
From Waste to Value
- 1st Edition - March 31, 2025
- Imprint: Elsevier
- Editors: Kishor Kumar Sadasivuni, Nebojsa Bacanin, Jaehwan Kim, Neha B Vashisht
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 7 3 7 4 - 2
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 7 3 7 5 - 9
Harnessing Automation and Machine Learning for Resource Recovery and Value Creation: From Waste to Value provides a comprehensive understanding of how automation and machine learni… Read more

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Request a sales quote- Provides insights into the potential of automation and machine learning in waste management inspiring readers to adopt sustainable waste management practices
- Offers a comprehensive understanding of how waste management can be transformed into a profitable business by adopting innovative and sustainable solutions
- Offers an opportunity to explore case studies from different industries and regions to showcase the revolutionary applications of automation and machine learning in waste management
- Provides guidance for waste management professionals, policymakers, and business leaders to optimize waste management processes and improve their bottom line
- Title of Book
- Cover image
- Title page
- Table of Contents
- Copyright
- List of Contributors
- About the editors
- Foreword
- Preface
- Acknowledgments
- Introduction
- Chapter 1. Introduction to innovative technologies for waste-to-energy conversion using automation and machine learning
- Abstract
- Introduction
- Fundamentals of waste-to-energy conversion
- Automation in waste-to-energy conversion
- Machine learning applications in waste-to-energy
- Integration of automation and machine learning
- Components of AutoML
- Prospects and emerging trends
- Conclusion
- References
- Chapter 2. Basics of machine learning
- Abstract
- Introduction
- Core concepts of machine learning
- Data preprocessing in machine learning
- Machine learning algorithms
- Practical aspects of machine learning
- Ethical implications and bias in machine learning
- Conclusion
- Abbreviations
- References
- Chapter 3. Basics of automation
- Abstract
- Introduction
- Fundamentals of automation
- Role of automation in the modern industry
- Architecture of industrial automation
- Industrial control system
- Input/Output Equipment
- Industrial sensor system
- Industrial actuator system
- Automatic controller
- Advantages and disadvantages of industrial automation
- Effects of the automation and artificial intelligence in COVID-19
- The future of automation: the power of artificial intelligence
- Conclusion
- References
- Chapter 4. Waste classification into plastics, industrial, domestic, and agriculture waste
- Abstract
- Introduction
- Related works
- Methodology
- Models
- Experimental setup
- Dataset description
- Data preprocessing
- TrashNet dataset for external validation
- Evaluation metrics
- Precision
- Recall
- F1 score
- Receiver Operating Characteristic curve
- Hyperparameters and training
- Results and discussion
- External validation
- Qualitative analysis
- Discussion and conclusion
- Limitations and future directions
- Conclusion
- Acknowledgment
- References
- Chapter 5. Plastics recycling and the automation role in the recycling process
- Abstract
- Introduction
- Scope and limitations
- Plastics recycling overview
- Environmental impact of plastics
- Need for recycling
- Current state of plastics recycling
- Challenges in collection
- Sorting technologies
- Processing and reprocessing
- Mechanical recycling
- Chemical recycling
- Advantages of chemical recycling of plastics
- Challenges related to chemical recycling of plastics
- Automation in plastics recycling
- Automation in washing and cleaning
- Automation in drying process
- Automated pelletizing systems
- Automated robotic sorting and handling
- Integration of artificial intelligence and machine learning in plastic recycling
- Remote monitoring and control
- Innovations in plastics recycling technologies
- Advanced sorting technologies
- AI-powered sorting algorithms
- Robotics and machine learning
- Emerging chemical recycling technologies
- Pyrolysis
- Solvent-based recycling
- Economic and environmental implications
- Cost-effectiveness of automation
- Reduction in greenhouse gas emissions
- Job impact and workforce transition
- Regulatory landscape
- International collaboration and standards
- Successful implementation of automation
- Challenges faced in real-world applications
- Future prospects
- Technological advancements
- Potential barriers to progress
- Conclusion
- Recap of key findings
- Recommendations for future research
- Abbreviations and acronyms
- References
- Chapter 6. Artificial intelligence for reutilizing the plastics
- Abstract
- Introduction
- Plastic pollution scenario 2024
- Machine learning in plastic composition analysis
- Mechanical recycling
- Recycling of organic materials
- Recycling of monomers
- Reusing outdated online content
- Robot recycling
- Valorization of waste heat
- Artificial intelligence
- Additional technology fixes
- Postconsumer textile waste chemical recycling: refibering
- Styrene butadiene styrene thermal technologies: waste heat valuation
- Several tactics using artificial intelligence to reduce and recycle plastic waste
- Automated classification
- Finding novel techniques for disposal
- Avoiding waste in production
- Procedure for recycling plastic bottles
- Smart waste technologies
- Robots for recycling
- Vacuum-powered waste pipes
- Trash compactors powered by sunlight
- E-waste kiosks
- Robotics and automation for plastic recycling
- Collaborative artificial intelligence platforms approach
- Real-time examples of artificial intelligence applications in plastic reutilization include
- Conclusion
- References
- Chapter 7. Handling metals waste to salvage with automation
- Abstract
- Introduction
- Types of scrap metals
- World iron scrap market (current trends)
- Electronic waste (e-waste)
- Future e-waste scenarios
- Recycling metals (significance)
- Urban mining
- Urban techniques available for the effective extraction
- Reduce and reuse of the metal waste
- Reducing metal waste
- Reusing metal waste
- Technological innovations
- Economic and policy considerations
- Importance of automation technology in metal waste management
- Case studies of automation technologies in industries for metal waste management
- Artificial intelligent technology
- Remediation of metal waste
- Metal waste contamination
- Reported artificial intelligence remediation techniques for the remediation of metal wastes
- Conclusions
- Future prospects
- References
- Further reading
- Chapter 8. Machine learning: a better means for metal waste to reprocess
- Abstract
- Introduction
- Fundamentals of metal waste reprocessing
- Role of machine learning in environmental sciences
- Machine learning applications in metal waste reprocessing
- Challenges, opportunities, and future directions
- Conclusion
- References
- Chapter 9. Waste classification using convolutional neural networks tuned by modified metaheuristics algorithm
- Abstract
- Introduction
- Related works
- Methods
- Simulation setup
- Experimental outcomes
- Conclusion
- Acknowledgment
- References
- Chapter 10. Exploring the potential of lightweight computer vision YOLOv8 models for effective waste classification and management
- Abstract
- Introduction
- Related works
- Methods
- Simulation configuration
- Simulation outcomes
- Conclusion
- References
- Chapter 11. Waste to value added: role of automation in organic waste
- Abstract
- Introduction
- Types of organic waste
- Challenges in organic waste management
- Automation technologies in organic waste management
- Composting systems
- Biogas production from organic waste
- Biofuel production from organic waste
- Case studies
- Value-added products from organic waste
- Economic and environmental benefits
- Case studies and success stories
- Future trends and innovations
- Conclusion
- References
- Further reading
- Chapter 12. Future of agriculture: smart vertical farming
- Abstract
- Introduction
- Vertical framing: components and types
- Suitable crops for vertical farming
- Smart vertical farming
- Automation
- Applications of Internet of Things (IoT) and artificial intelligence (AI) technologies
- Summary
- References
- Further Reading
- Chapter 13. Impact of artificial intelligence for the recycling of organic waste
- Abstract
- Introduction
- Types of organic waste
- Recycling of organic wastes
- Use of artificial intelligence in organic waste management and recycling
- Optimization of anaerobic digestion and biogas production
- Intelligent waste valorization and resource recovery
- Decision support systems for policy and planning
- Potential and challenges of artificial intelligence in the recycling of organic waste
- Potential opportunities
- Key challenges
- Summary
- References
- Chapter 14. Agriculture: the next machine-learning frontier
- Abstract
- Introduction and definition of the problem
- Artificial intelligence
- Why artificial intelligence in agriculture
- Artificial intelligence integration in agriculture machinery
- Limitations
- Future directions
- Conclusions
- References
- Chapter 15. Recycling robots to tackle electrical waste
- Abstract
- Introduction
- Electrical waste
- Recycling technologies of electrical wastes
- Robot technologies
- Summary
- References
- Chapter 16. Machine learning for sustainable development in electronics
- Abstract
- Introduction
- Foundations of machine learning in electronics
- Challenges in electronics for sustainable development
- Applications of machine learning for sustainable electronics
- Case studies and success stories
- Barriers and limitations
- Future trends and innovations
- Integration of machine learning into circular economy models
- Conclusion
- AI disclosure
- References
- Chapter 17. Automated sorting of recyclable domestic waste
- Abstract
- Introduction
- Recycle domestic wastes
- Automated sorting
- Automated waste categorization
- Automated waste sorting methods
- References
- Chapter 18. Intelligent waste management: a comprehensive review of machine learning and deep learning applications in advanced recycling
- Abstract
- Introduction
- Evolving landscape: the need for advanced solutions
- Unveiling machine learning: revolutionizing waste management
- Navigating neural networks: a deep dive into smart solutions
- Defining objectives: the scope and purpose of the review
- Machine learning in waste management
- Unpacking machine learning: concepts and algorithms
- Time travel through data: historical applications in waste management
- Riding the wave: recent advances and trends in machine learning for waste management
- Smart bins and beyond: real-world implementations and success stories
- Neural networks in waste management
- Building blocks: understanding the basics of neural networks
- Sorting the future: types of neural networks in waste management
- Neural innovations: cutting-edge applications in waste sorting and recycling
- Success in layers: case studies highlighting neural network achievements
- Decision-making in waste management
- The decision dilemma: challenges in traditional waste management decision-making
- Beyond the status quo: role of machine learning in decision support
- Smart choices: integrating neural networks for informed decision-making
- Case in point: improved decision-making scenarios in waste management
- Advanced recycling techniques
- Recycling realities: a snapshot of traditional methods
- Breaking the mold: introduction to advanced recycling techniques
- Machine learning’s green thumb: enhancing recycling processes
- Innovation showcase: examples of cutting-edge recycling technologies
- Integration of machine learning, neural networks, and decision-making
- Power trio: synergies and complementarities between machine learning, neural networks, and decision support
- Tech Tango: navigating challenges in integrating advanced technologies
- Real-world impact: success stories of integrated waste management systems
- Challenges and future directions
- Obstacles ahead: current challenges in applying machine learning and neural networks to waste management
- The ethical landscape: balancing technological advancements with environmental responsibility
- Visionary paths: potential future developments and research frontiers
- Conclusion
- References
- Chapter 19. Future aspects of machine learning/automation for the waste management
- Abstract
- Introduction
- Aspects of machine learning
- Types of machine learning
- Types and automation in waste management
- Opportunities in machine learning and automation
- References
- Glossary
- Index
- Edition: 1
- Published: March 31, 2025
- Imprint: Elsevier
- No. of pages: 530
- Language: English
- Paperback ISBN: 9780443273742
- eBook ISBN: 9780443273759
KS
Kishor Kumar Sadasivuni
NB
Nebojsa Bacanin
Dr. Nebojsa Bacanin received his Ph.D. degrees from Faculty of Mathematics, University of Belgrade in 2015 (study program Computer Science, average grade 10,00). He was the vice-dean of the Graduate School of Computer Science and Faculity of Informatics and Computing in Belgrade, Serbia. He currently works as a Full Professor and as a Vice-Rector for Scientific Research at Singidunum University. He is involved in scientific research in the field of computer science and his specialty includes artificial intelligence, machine learning, deep learning, stochastic optimization algorithms, swarm intelligence, soft-computing, optimization and modeling, image processing, computer vision and cloud and distributed computing. He actively works in the domain of novel and prospective research field, hybrid methods between machine learning and metaheuristics, where metaheuristics are applied for addressing non-deterministic polynomial hard (NP-hard) challenges from machine learning domain such as hyper-parameters optimization (tuning), training and feature selection. Besides improving machine learning/deep learning models for tackling various practical tasks for classification and regression, his research also involves optimized deep learning models for univariate and multivariate time-series forecasting. Moreover, he is an expert from the area of metaheuristics, and he has been actively doing research in enhancing swarm intelligence, as well as other types of metaheuristics, by incorporating minor changes (e.g., modification in exploitation/exploration expressions, parameters’ adjustments, etc.) and/or major modifications by performing hybridization with other methods (e.g., low-level and high-level hybrid metaheuristics methods). He has been applying his methods to wide variety of practical research areas, e.g., cloud computing scheduling, wireless sensor networks (WSNs) localization, coverage and energy consumption, X-ray images classification, stock price forecasting, portfolio optimization, as well as many others.
JK
Jaehwan Kim
NV