Harnessing Artificial Intelligence / Machine Learning and Iot for Efficient Water Quality Monitoring and Membrane-Based Treatment

Current Trends and Future Developments in Bio-Membranes
Erscheinungsjahr 2027
ISBN: 978-0-443-44518-7
Verlag: Elsevier Science

Buch, Englisch, 425 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 449 g

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.

Autoren/Hrsg.

Isloor, Arun M

Basile, Angelo

Hegde, Roopa B

Nayak, Sneha

Al-Ahmed, Amir

Fachgebiete

• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Verfahrenstechnik, Chemieingenieurwesen

Inhaltsverzeichnis

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

Über Autor(innen)

Isloor, Arun M
Arun M. Isloor is a Fellow of Royal Society of Chemistry and serving as a Professor in the Department of Chemistry, National Institute of Technology Karnataka, India, since last 16 years. His research interests includes Membrane technology, Nanomaterials, Medicinal Chemistry & Polymer chemistry.

Al-Ahmed, Amir
Amir Al Ahmed is working as a Research Scientist-I in the IRC-Renewable Energy and Power Systems (IRC-REPS), at King Fahd University of Petroleum & Minerals (KFUPM), Saudi Arabia. He completed his Ph.D. (2003) degree in Applied Chemistry from the Department of Applied Chemistry, AMU, India, followed by three consecutive postdoctoral fellowships in South Africa and Saudi Arabia. During this period, he worked on various multidisciplinary projects, in particular, conducting polymer, electrochemical sensors, nano-materials, polymeric membranes, electro-catalysis and solar cells. At present, his research activity is fundamentally focused on 3rd generation solar cell devices, such as, low band gap semiconductors, quantum dots, perovskites, and silicon nanowire based tandem cells. At the same time, he is also having projects on energy storage technologies, such as, electricity, hydrogen (in porous materials) and heat. He has worked on different NSTIP, KACST and Saudi Aramco funded projects in the capacity of a principle and co-investigator. Dr. Amir has 8 US patents.

Hegde, Roopa B
Dr. Roopa B Hegde is an Associate Professor in the Department of Electronics and Communication Engineering at NITTE (Deemed to be University), NMAM Institute of Technology. Her expertise spans image processing, medical device development, pattern recognition, machine learning, and deep learning. A life member of the Indian Society for Technical Education (ISTE), she has received funding from the Karnataka State Council for Science and Technology and holds multiple patents. Dr. Hegde has been recognized for her research contributions, receiving awards such as the Best Paper Award at VSPICE 2020. She has numerous publications in esteemed international journals and conferences.

Basile, Angelo
Angelo Basile, a Chemical Engineer with a Ph.D. in Technical Physics, was a senior Researcher at the ITM-CNR as a responsible for the research related to both ultra-pure hydrogen production and CO2 capture using Pd-based Membrane Reactors. He is a reviewer for 165 int. journals, an editor/author of more than 50 scientific books and 140 chapters on international books on membrane science and technology; with various patens (7 Italian, 2 European, and 1 worldwide). He is a referee of 1more than 150 international scientific journals and a Member of the Editorial Board of more than 20 of them. Basile is also an associate editor of the: Int. J. Hydrogen Energy; Asia-Pacific Journal of Chemical Eng.; journal Frontiers in Membrane Science and Technology; and co-Editor-in-chief of the Int. J. Membrane Science & Technol.