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Full Description
Harnessing Artificial Intelligence / Machine Learning and IoT for Efficient Water Quality Monitoring and Membrane-Based Treatment: Current Trends and Future Developments in Bio-Membranes delves into the transformative potential of advanced technologies for sustainable water management. The book integrates AI, machine learning, and IoT to present innovative methodologies for real-time water quality monitoring, efficient wastewater treatment, and optimization of water filter membranes. Readers will discover effective solutions that ensure access to safe and clean water, addressing the pressing global water crisis head-on. The book is structured into five key sections exploring critical themes. Section I investigates the application of AI and machine learning in optimizing desalination processes. Section II highlights the challenges of biofouling in water treatment systems, showcasing IoT-enabled solutions and green membrane innovations. Section III focuses on smart effluent management systems driven by real-time data and machine learning algorithms. Section IV discusses green energy integration in water treatment practices, while Section V addresses the automation in adsorption processes, emphasizing AI's role in enhancing efficiency and sustainability. Harnessing Artificial Intelligence / Machine Learning and IoT for Efficient Water Quality Monitoring and Membrane-Based Treatment: Current Trends and Future Developments in Bio-Membranes is an invaluable resource for researchers, engineers, water treatment professionals, and policymakers, equipping them with the knowledge and tools necessary to navigate the complexities of modern water management, fostering innovative approaches to ensure a sustainable future.
Contents
Section I. Drivers challenges and evolving technologies in desalination
1. Solving global water crisis using desalination systems- a machine learning approach
2. Machine learning assisted screening of next generation advanced materials for water desalination
3. Implications of IoT based smart architecture for water desalination: a case study
4. Performance modelling of desalination system using machine learning
5. Effective energy management in desalination systems using deep learning
Section II. Potential risks and challenges in biofouling monitoring technologies
6. Membrane innovations using IoT for achieving global water sustainability
7. Membrane fouling prediction using machine learning: a case study
8. A review on smart and robust technologies for water treatment and monitoring
9. Early prediction of membrane fouling using machine learning: a critical review
10. Design and development of green and sustainable membrane materials with antifouling capacity using IoT
Section III. Technology based monitoring for design of smart effluent management systems
11. Intelligent prediction of carbon footprint of treatment plants using Machine learning
12. Technical innovations in treatment plants: driving towards smart city inclination
13. Use of machine learning for real time data processing in treatment plants
14. Secure surveillance in treatment plants using IoT
15. A critical review on ML/AI/ smart technologies for monitoring treatment plant performance
Section IV. Green energy for water treatment: practices, awareness, and challenges
16. Artificial intelligence and IoT enabled smart systems in water treatment
17. Integration of green energy and pioneering energy-efficient technologies in treatment plants using machine learning
18. IoT based smart energy and water management: a case study
19. Role of artificial intelligence in renewable energy integration in treatment plants
20. Impact of renewable energy utilization and AI in next generation sustainable treatment plants: a review
Section V. Automation in adsorption process
21. Role of AI in adsorption process automation: recent advances and future prospects
22. Exploring AI for characteristic analysis of heavy metal adsorption
23. Simulation of heavy metal adsorption on novel nanocomposites using AI
24. Prediction of adsorption using AI models
25. Deep learning models for predicting gas adsorption capacity of novel materials
