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

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. 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. 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. Dr. Sneha Nayak is an assistant Professor at NMAM Institute of Technology, Nitte deemed to be University, Nitte, specialized in the field of Nanobiotechnology. Her research interests varies from green synthesis of metallic nanoparticles for environmental remediation applications to formulation of a herbal shampoo which aims at reaching out the shampoo benefits to a larger crowd. Dr. Sneha Nayak is a recipient of Research grant for scientist faculty (RGS/F) grant of Rs 500000 from VGST GoK for the year 2017-18 for the project entitled “Biosynthesis of nanoparticles and its application in detection of heavy metals in water samples and effluents”. She has published her research work in leading journals such as Bioresource Technology, Food and chemical Toxicology and marine pollution bulletin. 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.

• Section I. Drivers challenges and evolving technologies in desalination1. Solving global water crisis using desalination systems– a machine learning approach2. Machine learning assisted screening of next generation advanced materials for water desalination3. Implications of IoT based smart architecture for water desalination: a case study4. Performance modelling of desalination system using machine learning5. Effective energy management in desalination systems using deep learningSection II. Potential risks and challenges in biofouling monitoring technologies6. Membrane innovations using IoT for achieving global water sustainability7. Membrane fouling prediction using machine learning: a case study8. A review on smart and robust technologies for water treatment and monitoring9. Early prediction of membrane fouling using machine learning: a critical review10. Design and development of green and sustainable membrane materials with antifouling capacity using IoTSection III. Technology based monitoring for design of smart effluent management systems11. Intelligent prediction of carbon footprint of treatment plants using Machine learning12. Technical innovations in treatment plants: driving towards smart city inclination13. Use of machine learning for real time data processing in treatment plants14. Secure surveillance in treatment plants using IoT15. A critical review on ML/AI/ smart technologies for monitoring treatment plant performanceSection IV. Green energy for water treatment: practices, awareness, and challenges16. Artificial intelligence and IoT enabled smart systems in water treatment17. Integration of green energy and pioneering energy-efficient technologies in treatment plants using machine learning18. IoT based smart energy and water management: a case study19. Role of artificial intelligence in renewable energy integration in treatment plants20. Impact of renewable energy utilization and AI in next generation sustainable treatment plants: a reviewSection V. Automation in adsorption process21. Role of AI in adsorption process automation: recent advances and future prospects22. Exploring AI for characteristic analysis of heavy metal adsorption23. Simulation of heavy metal adsorption on novel nanocomposites using AI24. Prediction of adsorption using AI models25. Deep learning models for predicting gas adsorption capacity of novel materials