Description
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.- Provides knowledge on various emerging pollutants which are hazardous to the environment, human health, and the applications of different artificial intelligence (AI) tools for the bioremediation of contaminant removal- Explores different aspects of biological methods using AI tools, including solutions for recycling and reuse- Highlights ways AI can assist scientists to make smart and effective decisions on the identification of suitable sorbents to enhance the performance of the sorption process and protect our environment
Table of Contents
1. Artificial intelligence: Introduction of technology, tools and need and significance for environmental sustainability2. Artificial intelligence: historical background, types of tool and application for sustainable future3. Artificial intelligence in pollution control and management: status and future prospects4. Recent advances in use of artificial intelligence for optimization and automation of adsorption processes for wastewater treatment5. Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment system6. Applications of artificial intelligence tools for the adsorption of dyes from industrial effluent7. Application of AI tools and smart technology in adsorption of heavy metals by using biochar8. Current applications and future impacts of artificial intelligence and machine learning tools in removal of emerging contaminants9. Applications of AI for the adsorption of pharmaceutical compounds from wastewater10. Use of AI tools for the adsorption of organic pollutants from wastewater11. Use of machine learning methods for dye adsorption prediction onto agricultural waste /biochar/ activated carbon12. Comparison between conventional methods and artificial intelligence tools for wastewater treatment13. Artificial neural networks (ANN) for the prediction of biochar yield and its application in bioremediation14. Application of different AI models like fuzzy logic, genetic programming, model tree for prediction and removal of contaminants from wastewater treatment plants15. Challenges with artificial intelligence and machine learning methods for implementation in water treatment and monitoring16. Artificial intelligence technologies for forecasting air pollution and human health17. Applications of artificial intelligence-based modeling for bioenergy system18. Use of artificial intelligence tools in agro-waste management19. Application of artificial intelligence and machine learning technologies for development of circular economy20. Artificial intelligence: economical approaches for waste management and disposal21. Artificial intelligence and machine learning tools: Patents and technologies transferred to the industries22. Case studies from developed and developing nations: economic evaluation of contaminants removal/ waste recycling by AI tools23. Case study on the use of artificial intelligence tools to reduce pesticides/synthetic fertilizers application in agricultural fields24. Role of AI tools in a future bioeconomy for environmental clean-up: regulations and policy framework
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