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

Purchase options

BLOOM INTO SPRING SAVINGS

Save up to 25% on books and eBooks!

Shop now

Institutional subscription on ScienceDirect

Request a sales quote

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.

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

Kishor Kumar Sadasivuni

Dr. Kishor Kumar Sadasivuni is currently working as a Research Assistant Professor and the group leader of SmartNanoSolutions at Center for Advanced Materials, Qatar University. Dr. Kishor’s research has its roots from the analytical chemistry he mastered during his Master’s degree in Andhra University, India. He received his Ph.D. in Materials Science and Engineering from University of South Brittany at Lorient, France in 2012 under the supervision of the Prof. Yves Grohens and Prof. Sabu Thomas. He has to his credit a vast experience of postdoctoral research as he has been to this position firstly at the Inha University at South Korea (Professor Jeahwan Kim Supervisor, 2014) and then two times at Qatar University (Professor Mariam Al-Madeed,2015 and Dr. John-John Cabibihan,2017). He was appointed to the Center for Advanced Materials at Qatar University in 2018 as a Research Associate from which he has been promoted to his present designation as Research assistant Professor in June 2021. He has been included in the world’s top 2 % scientists according to a list compiled by Stanford University in the year 2019 and for this he was recently honored by the QU. He has enormous knowledge and valuable experience in his field owing to over 12 years of active research research experience of 12 years. Dr. Kishor has more than 250 research articles published in international peer reviewed journals with a total citation of 4850 and h-index 37. He is also the author of 20 book chapters and the editor of 8 books. His books were included in Springer Top 25 e-book download of the year 2020. Some of Dr. Kishor’s inventions are protected by 2 US patents and 2 Indian patents and presently 5 patent disclosures have been submitted by him. He is a group leader and is presently the lead principal investigator for 11 research projects, covering the NPRP, UREP, IRCC grants of Qatar National Research Fund and the Qatar University. He is also involved in other three research grants as the principal investigator, summarizing a total grant amount of 3M$. Dr. Kishor is a team player and has collaborated actively with researchers (more than 450 co-authors as evident from the Scopus data) in several disciplines of computer science, biomedical sciences, industrial engineering, and electrical engineering from all over the world (USA, France, South Korea, Oman, Spain, Italy, Australia, Malaysia). Dr. Kishor’s achievements have been recognized by several awards such as Tyre & Rubber Industry Leadership Acknowledgement Awards (TRILA); Young Research Scholar of the Year 2017.

Affiliations and expertise

Analytical chemist, Polymer and materials scientist, Center for Advanced Materials, Qatar University, Doha, Qatar

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.

Affiliations and expertise

Professor and Vice-Rector for Scientific Research, Singidunum University, Serbia

Jaehwan Kim

Jaehwan Kim serves as an Inha Fellow Professor at the Department of Mechanical Engineering at Inha University, Korea. He is also designated as Director of CRC for NanoCellulose Future Composites. Dr. Kim is a Fellow of The Korean Academy of Science and Technology, the National Academy of Engineering of Korea, and the Institute of Physics. He is an Associate Editor of Smart Materials and Structure as well as Smart Nanosystems in Engineering and Medicine and Editor of International Journal of Precision Manufacturing and Engineering, Helyon and Actuators. He has been the Director of Creative Research Center for EAPap Actuator funded by the National Research Foundation of Korea (NRF). Recently, he started the Creative Research Center for Nanocellulose Future Composites, sponsored by NRF. His research interests are smart materials, structures and devices, biomaterial-based smart materials, cellulose, electroactive polymers, power harvesting, biomimetic actuators, biosensors, tactile sensors, and flexible electronics. He has published more than 320 journal papers, presented 360 international conference papers, and filed more than 40 patents.

Affiliations and expertise

Professor, Inha University, South Korea

Neha B Vashisht

Dr. Neha Vashisht is working as Researcher at as a researcher at Smart Nano Solution group, Centre for Advanced materials (CAM), Qatar University. She received her PhD in 2017 from Department of Basic & Applied Sciences, Guru Gobind Singh Indraprastha University of Science and Technology, New Delhi, India. During her Ph.D, she worked on Synthesis and Characterization of Plasmonic noble metal-Tin oxide hybrid nanocomposites for sensing applications. She has many research publications in leading scientific research journals with good impact factors. Her area of interest are synthesis and characterization of new smart hybrid materials, nanocomposites, their modifications, and optics. She has actively participated in and presented her research work at several international conferences and seminars.

Affiliations and expertise

Center for Advanced Materials, Qatar University, Doha, Qatar

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health

Harnessing Automation and Machine Learning for Resource Recovery and Value Creation

From Waste to Value

Purchase options

BLOOM INTO SPRING SAVINGS

Institutional subscription on ScienceDirect

Kishor Kumar Sadasivuni

Nebojsa Bacanin

Jaehwan Kim

Neha B Vashisht

Related books