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This book chapter delves into the application of machine learning techniques in wastewater treatment processes, examining the multifaceted challenges that often hinder seamless implementation. With an emphasis on comprehensiveness, the exploration encompasses a detailed analysis of technical, operational, and contextual hurdles. By dissecting these obstacles, the chapter aims to provide a nuanced understanding of the impediments faced by practitioners in the field. Additionally, strategic solutions and frameworks are proposed to overcome these challenges, fostering a more effective integration of machine learning methodologies in wastewater treatment systems. Through this comprehensive exploration, we contribute to the ongoing discourse on advancing sustainable water management practices, showcasing the potential of machine learning as a transformative tool in optimizing wastewater treatment processes.

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Machine Learning and Deep Learning Applied to Wastewater Treatment

  • 章节

  • 发表日期：

    2025年

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  • Nicolas Spogis,
  • Juliana Neves,
  • Sérgio Leonardo Butski Soares Santos,
  • Natan Padoin,
  • Cíntia Soares

摘要

This book chapter delves into the application of machine learning techniques in wastewater treatment processes, examining the multifaceted challenges that often hinder seamless implementation. With an emphasis on comprehensiveness, the exploration encompasses a detailed analysis of technical, operational, and contextual hurdles. By dissecting these obstacles, the chapter aims to provide a nuanced understanding of the impediments faced by practitioners in the field. Additionally, strategic solutions and frameworks are proposed to overcome these challenges, fostering a more effective integration of machine learning methodologies in wastewater treatment systems. Through this comprehensive exploration, we contribute to the ongoing discourse on advancing sustainable water management practices, showcasing the potential of machine learning as a transformative tool in optimizing wastewater treatment processes.