Applications of Artificial Intelligence in Removal of Emerging Contaminants: Sustainable Approach for Environmental Clean-up and Circular Economy focuses on the exploitation of various Artificial Intelligence (AI) tools to treat emerging contaminants and the restoration of contaminated sites. The book addresses critical environmental issues and challenges faced by society to meet such challenges. It highlights artificial intelligence and machine learning tools in the removal of environmental contaminants, such as industrial effluents, air, and to overcome limitations of conventional methods, predict water quality parameters and decrease the chances of error.
Advanced tools, updated information, and future directions for researchers and scientists working in the bioremediation of emerging contaminants, bioenergy and reutilization of waste biomass in the production of value-added products for environmental safety are also thoroughly discussed.
1. Artificial intelligence: Introduction of technology, tools and need and significance for environmental sustainability
2. Artificial intelligence: historical background, types of tool and application for sustainable future
3. Artificial intelligence in pollution control and management: status and future prospects
4. Recent advances in use of artificial intelligence for optimization and automation of adsorption processes for wastewater treatment
5. Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment system
6. Applications of artificial intelligence tools for the adsorption of dyes from industrial effluent
7. Application of AI tools and smart technology in adsorption of heavy metals by using biochar
8. Current applications and future impacts of artificial intelligence and machine learning tools in removal of emerging contaminants
9. Applications of AI for the adsorption of pharmaceutical compounds from wastewater
10. Use of AI tools for the adsorption of organic pollutants from wastewater
11. Use of machine learning methods for dye adsorption prediction onto agricultural waste /biochar/ activated carbon
12. Comparison between conventional methods and artificial intelligence tools for wastewater treatment
13. Artificial neural networks (ANN) for the prediction of biochar yield and its application in bioremediation
14. Application of different AI models like fuzzy logic, genetic programming, model tree for prediction and removal of contaminants from wastewater treatment plants
15. Challenges with artificial intelligence and machine learning methods for implementation in water treatment and monitoring
16. Artificial intelligence technologies for forecasting air pollution and human health
17. Applications of artificial intelligence-based modeling for bioenergy system
18. Use of artificial intelligence tools in agro-waste management
19. Application of artificial intelligence and machine learning technologies for development of circular economy
20. Artificial intelligence: economical approaches for waste management and disposal
21. Artificial intelligence and machine learning tools: Patents and technologies transferred to the industries
22. Case studies from developed and developing nations: economic evaluation of contaminants removal/ waste recycling by AI tools
23. Case study on the use of artificial intelligence tools to reduce pesticides/synthetic fertilizers application in agricultural fields
24. Role of AI tools in a future bioeconomy for environmental clean-up: regulations and policy framework
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