Artificial Intelligence and Machine Learning for Smart and Sustainable Waste Treatment

Emerging Applications and Best Practices

Häftad, Engelska, 2027

3 009 kr

Kommande

Artificial Intelligence and Machine Learning for Smart and Sustainable Waste Treatment provides a much-needed, specialized resource on the application of Artificial intelligence (AI) and machine learning in sustainable waste management. The book provides methods to help streamline processes, improve recycling rates, reduce contamination, and lower operational costs along with Machine learning models that can enhance forecasting and planning, allowing municipalities to optimize collection schedules and waste processing strategies. By combining theoretical insights with practical solutions, this book closes the gap between advanced technology and real-world applications, providing a roadmap for transforming solid waste management through data-driven, AI-powered approaches and seeks to drive forward the integration of intelligent, sustainable solutions into waste management practices across the globe.

Clear Methodologies and Frameworks: The book provides structured methodologies for integrating AI and machine learning techniques into various facets of solid waste management
Actionable Case Studies and Real-World Examples: To help readers understand the practical applications of AI in waste management, the book presents detailed case studies from cities, companies, and research projects that have successfully implemented AI and machine learning
Tools for Optimization and Sustainability: The book equips readers with the tools they need to optimize waste management processes. This includes AI models that predict waste patterns, recommend sorting improvements, and streamline recycling operations

Produktinformation

Utgivningsdatum2027-02-01
Mått191 x 235 x undefined mm
Vikt450 g
FormatHäftad
SpråkEngelska
Antal sidor350
FörlagElsevier Science
ISBN9780443453700

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Tillverkningsteknik inom Naturvetenskap och teknik

Izharul Haq is working as an Assistant Professor at the Manipal University Jaipur, India, where he specializes in environmental biotechnology, with a focus on waste management using microbial processes and evaluating the toxicity of pollutants. He completed his PhD in Microbiology at the CSIR-Indian Institute of Toxicology Research, Lucknow, India, and later conducted postdoctoral research at the Indian Institute of Technology, Guwahati. His current research work is in the field of environmental microbiology and pollutant remediation. Krishna Chaitanya Maturi is an Assistant Professor in Department of civil engineering at Gati Shakti Vishwavidyalaya. He holds a B. Tech in Civil Engineering from JNTU Kakinada and an M. Tech in Civil/Environmental Engineering from IIT Guwahati. He completed his Ph.D. in Civil/Environmental Engineering, specializing in Solid Waste Management from IIT Guwahati. He has also served as a Research Associate at Hong Kong Baptist University and worked as a Project Scientist II in the Department of Civil Engineering at IIT Roorkee. His expertise spans environmental engineering, focusing on sustainable waste management practices, waste to energy, and Plastics and bioplastics in waste streams.

1. Introduction and Historical perspective to AI and ML in solid waste management industry and their practices2. Segregation, and implementation of zero bin cities using AI approaches3. AI driven strategy for implementing transfer stations and treatment technologies for SWM4. AI applications in implementing decision supporting system for high success rate of SWM projects5. Applications of deep learning in thermal and biological processes6. AI and ML applications in thermal conversion of organic waste7. Biological treatment optimizations using AI techniques8. Fusing AI and Mechanistic simulations for efficient bioenergy generation9. Forecasting Biochemical Processes for Advancing Biofuel, Bioproduct, and Biomaterial Innovation using AI techniques.10. Assessment of case studies on AI based solid waste management projects11. Incorporation of AI tools in Solid Waste Management: Case studies from a Waste Management perspective12. Cost-Benefit Analysis of Artificial Intelligence Applications in Solid Waste Management13. AI-Driven Innovations in Recycling, Regulatory Monitoring, and Smart Waste Technologies: Market Landscape and Application Insights14. Ethical, Governance, and Circular Economy Considerations in AI-Driven Solid Waste Management Systems15. Future perspective, Challenges, and Limitations of AI and ML applications in providing best practices for SWM.