Machine Learning and AI-Driven Water Quality Monitoring and Treatment

E3S Web of Conferences, Год журнала: 2024, Номер 505, С. 03012 - 03012

Опубликована: Янв. 1, 2024

This study examines the latest utilization of combination machine learning (ML) and artificial intelligence (AI) in monitoring upgrading water quality, which has become a crucial component environmental management. In this paper, thorough examination modern methods recent advancements fields algorithms, have considerably enhanced precision effectiveness quality tracking systems. The analyzes integration these innovations into treatment methods, focusing their ability to more efficiently identify reduce contaminants compared traditional techniques. paper collection case studies (AI)-powered devices been used, showcasing significant developments evaluation improved levels efficiency. present additionally various problems potential future Artificial Intelligence Machine Learning within particular domain. These challenges cover issues scalability, data security, as well importance for interdisciplinary collaboration. gives comprehensive analysis impact AI ML technologies on management, demonstrating transform current practices towards greater sustainability

Язык: Английский

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Research on the Application of Intelligent Sensors Based on the Internet of Things in Fault Diagnosis of Mechanical and Electrical Equipment

Measurement Sensors, Год журнала: 2025, Номер unknown, С. 101811 - 101811

Опубликована: Янв. 1, 2025

Язык: Английский

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Significance of automation in water treatment processes

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 175 - 194

Опубликована: Янв. 1, 2025

Язык: Английский

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Novel approach for AI-based N2O emission reduction in biological wastewater treatment relying on genetic algorithms and neural networks

Water Science & Technology, Год журнала: 2025, Номер 91(10), С. 1172 - 1184

Опубликована: Май 6, 2025

ABSTRACT The potential of measurement-based control strategies for achieving lower N2O emissions in biological wastewater treatment is limited due to strong temporal variations and a lack measurement data regarding influencing parameters. To address this issue, novel artificial intelligence (AI)-based process optimization method minimizing was developed, relying on genetic algorithm automatically determine the settings associated with minimum an individual operating situation. employs validated prediction model evaluate effect parameter sets other targets. For purpose, neural networks were trained using generated mechanistic model. This approach beneficial practical applications as could be successfully even if only available. developed also includes classification check reliability AI-suggested strategy. Two modeling studies confirm that application methodology holds considerable reduction (43% or 1,588 t CO2e/a) while still required effluent quality. Operational are identified less than 2 minutes so can applied large scale.

Язык: Английский

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A data-knowledge hybrid decision support system for wastewater treatment operations: The Acqua dei Corsari plant case study

Information Sciences, Год журнала: 2025, Номер unknown, С. 122321 - 122321

Опубликована: Май 1, 2025

Язык: Английский

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