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              <text>Advancing Nutrient Removal and Resource Recovery Through Artificial Intelligence: A Comprehensive Analysis and Future Perspectives</text>
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              <text>Artificial intelligence (AI); Environmental engineering; Nutrient removal; Resource recovery; Sustainable innovations; Wastewater treatment</text>
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              <text>The increasing difficulties associated with effectively controlling wastewater treatment operations while simultaneously satisfying the imperatives of nutrient removal and resource recovery have necessitated the use of advanced technology. This book chapter provides a comprehensive analysis of the use of artificial intelligence (AI) methods within this complex context. Utilizing a vast array of scholarly investigations and real-world implementations, this study explores the intricate domain of wastewater treatment, providing a comprehensive understanding of how artificial intelligence algorithms are used to enhance the efficiency of nutrient removal procedures and expedite the recovery of valuable resources. This chapter presents a thorough examination of the impact of artificial intelligence (AI) on sustainable innovations in wastewater treatment facilities. It accomplishes this through a comprehensive analysis of relevant data and the inclusion of real-world case studies. The findings of this research highlight the transformative effect of AI on conventional approaches to wastewater treatment, enabling the adoption of environmentally friendly and resource-efficient practices. The integration of artificial intelligence (AI) with wastewater management offers a fascinating story that highlights the shifting paradigm in the field of environmental engineering and the efficient exploitation of resources.  The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.</text>
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              <text>Hemanth K.S.; Deepika T.; Lakshmi V.</text>
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              <text>Springer Water, Vol-Part F3340, pp. 83-116.</text>
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              <text>&lt;a href="https://doi.org/10.1007/978-3-031-67237-8_4" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-3-031-67237-8_4&lt;/a&gt;
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              <text>ISSN: 23646934</text>
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              <text>Hemanth K.S., School of Computer Science and Applications, REVA University, Bengaluru, India, Department of Computer Science, Christ University, Bangalore, India; Deepika T., School of Computer Science and Applications, REVA University, Bengaluru, India; Lakshmi V., School of Computer Science and Applications, REVA University, Bengaluru, India</text>
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