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Papers
61,005 resultsShowing papers similar to AI Applied to the Circular Economy: An Approach in the Wastewater Sector
ClearAI-based wastewater treatment for a circular economy and sustainable management of PFAS, heavy metals, microplastics, and antibiotics
This review examined how artificial intelligence can be integrated into wastewater treatment systems to improve removal of emerging contaminants including PFAS, heavy metals, microplastics, and antibiotics. The authors conclude that AI-driven optimization offers significant potential for a circular economy approach to water treatment.
A Review on Applications of Artificial Intelligence in Wastewater Treatment
This review summarizes how artificial intelligence models are being applied to improve wastewater treatment processes, including the removal of microplastics and other pollutants. Researchers found that machine learning and neural networks can effectively predict treatment efficiency, optimize operations, and reduce energy costs. The study suggests that AI-driven approaches could make water treatment systems more adaptive and cost-effective in handling emerging contaminants.
Water reuse: a pillar of the circular water economy
This review argues that water reuse is a foundational pillar of the circular water economy, shifting the paradigm from dissipative pollutant removal toward resource recovery and closed-loop water management. The authors examine how conventional wastewater treatment approaches must be reimagined to enable sustainable water circularity.
An Overview of the Latest Developments and Potential Paths for Artificial Intelligence in Wastewater Treatment Systems
This review surveys recent developments in applying artificial intelligence to wastewater treatment, including models for water quality prediction, process optimization, and contaminant removal. The study covers neural networks, support vector machines, decision trees, and deep learning approaches used to improve efficiency and reduce costs. The authors highlight that AI-driven methods show promise for optimizing the removal of emerging contaminants, including microplastics, from wastewater systems.
Circular Economy in Wastewater Treatment Plant—Water, Energy and Raw Materials Recovery
This review proposes a conceptual framework for future wastewater treatment plants operating as resource recovery facilities within a circular economy, focusing on technologies for recovering water, energy, and raw materials including nutrients and biopolymers.
Exploring the Role of Artificial Intelligence in Wastewater Treatment: A Dynamic Analysis of Emerging Research Trends
Researchers conducted a large-scale analysis of over 4,300 publications on artificial intelligence applications in wastewater treatment, spanning from 1985 to 2024. They found that AI techniques like neural networks and genetic algorithms are increasingly used to optimize processes such as contaminant removal, energy consumption, and membrane fouling control. The study identifies real-time process monitoring and AI-driven effluent prediction as key areas for future development in sustainable water management.
The Use of Artificial Intelligence and Machine Learning in Creating a Roadmap Towards a Circular Economy for Plastics
This paper examines how artificial intelligence and machine learning can help transition the plastics industry toward a circular economy. AI tools can optimize recycling processes, predict material degradation, and identify opportunities to reduce plastic waste before it enters the environment.
Source separation, transportation, pretreatment, and valorization of municipal solid waste: a critical review
Researchers reviewed the full chain of municipal solid waste management — from source separation through collection, pretreatment, and valorization — finding that AI and the Internet of Things are emerging as powerful tools for optimizing collection routing and sorting efficiency within circular waste management systems.
Machine learning and circular bioeconomy: Building new resource efficiency from diverse waste streams
Researchers reviewed how machine learning is being applied across four key biorefinery systems — composting, fermentation, anaerobic digestion, and thermochemical conversion — finding that AI-based models consistently outperform traditional mechanistic approaches in handling the high-dimensional complexity of circular bioeconomy processes.
Wastewater and sludge valorisation: a novel approach for treatment and resource recovery to achieve circular economy concept
This review highlights novel approaches for wastewater and sludge valorisation within a circular economy framework, focusing on recovering value-added products including biopolymers, nutrients, and energy to achieve sustainable development goals and combat water scarcity.
The Role of Conventional Methods and Artificial Intelligence in the Wastewater Treatment: A Comprehensive Review
This review provides a comprehensive overview of both conventional and artificial intelligence-based approaches to wastewater treatment, covering methods for removing contaminants including microplastics, heavy metals, and organic pollutants. Researchers found that AI and machine learning tools can optimize treatment processes, predict outcomes, and reduce costs compared to traditional trial-and-error approaches. The study highlights how digital technologies are transforming water treatment to meet growing demands for clean water.
Removal of microplastics from wastewater: available techniques and way forward
This review surveys available techniques for removing microplastics from wastewater within a circular economy framework, discussing innovative treatment technologies, integrated risk-based approaches, and regulatory and economic guidelines needed to advance water resource recovery facilities beyond conventional pollutant removal.
A Systematic Review of Solid Waste Management (SWM) and Artificial Intelligence approach
This systematic review found that artificial intelligence and machine learning are increasingly being applied to solid waste management for tasks like waste classification, collection route optimization, and landfill monitoring. AI-based approaches showed significant improvements over traditional methods in sorting accuracy and operational efficiency.
Wastewater Valorization: Practice around the World at Pilot- and Full-Scale
This review summarizes pilot- and full-scale wastewater valorization practices globally, focusing on how water resource recovery facilities recover nutrients, energy, and bio-based materials from sewage and sludge to contribute to a circular economy. The authors identify effective technological strategies that are being implemented or scaled up worldwide.
A systematic review of industrial wastewater management: Evaluating challenges and enablers
This systematic review of 66 studies on industrial wastewater management found that while treatment technologies are advancing, major challenges remain in regulation enforcement, cost-effectiveness, and integration of circular economy principles. The research highlights that inadequate industrial wastewater treatment is a significant source of environmental pollutants, including microplastics, entering waterways.
Artificial Intelligence Methods for Analysis and Optimization of CHP Cogeneration Units Based on Landfill Biogas as a Progress in Improving Energy Efficiency and Limiting Climate Change
This paper reviews artificial intelligence methods applied to the analysis and optimization of combined heat and power (CHP) cogeneration systems, exploring how AI can improve the efficiency of simultaneous electricity and thermal energy production.
Editorial: Emerging approaches for sustainable management for wastewater
This editorial introduces a research collection on emerging approaches for sustainable wastewater management, highlighting advances in nutrient recovery, contaminant removal, and resource valorisation within circular economy frameworks.
Water Quality Management in the Age of AI: Applications, Challenges, and Prospects
This review examines how artificial intelligence is transforming water quality management through improved monitoring, prediction, and pollution tracking. Researchers found that combining AI with technologies like the Internet of Things and remote sensing has significantly enhanced real-time water quality analysis and early warning systems. However, major challenges remain around data quality, model transparency, and the ability to detect emerging pollutants like microplastics.
Next-Generation AI-IoT Integrated Systems for Dynamic Optimization of Water Disinfection and Removal of Emerging Contaminants
Researchers explored the integration of artificial intelligence and Internet of Things technologies into water management systems to improve disinfection and removal of emerging contaminants. The study found that AI-IoT integrated systems enable dynamic, real-time optimization of water treatment processes, offering more effective responses to complex water quality challenges.
Artificial intelligence for waste management in smart cities: a review
Researchers reviewed how artificial intelligence (AI) is being applied to nearly every aspect of waste management, from sorting recyclables with up to 99.95% accuracy to cutting transportation costs by over 36%. Their findings show AI could dramatically improve how cities handle plastic and other waste, reducing pollution and public health burdens.
A nanotechnology roadmap for circular wastewater management
This review paper summarizes research on using tiny particles called nanoparticles to make wastewater treatment more efficient and environmentally friendly. The technology could help clean water while also recovering valuable materials like nutrients and energy, but scientists still need to solve problems like how to use it safely on a large scale. Better wastewater treatment matters for human health because it helps ensure our water supply stays clean and reduces pollution in the environment.
Advancing environmental sustainability through emerging AI-based monitoring and mitigation strategies for microplastic pollution in aquatic ecosystems
This review explores how artificial intelligence technologies, including machine learning, computer vision, and remote sensing, can improve the detection, tracking, and removal of microplastic pollution in waterways. Researchers found that AI-based approaches offer significant advantages over traditional monitoring methods for identifying microplastic distribution patterns. The study highlights the potential of AI-driven robotic systems to support more efficient and scalable environmental cleanup efforts.
Nanotechnology and AI Impact on Waste Management
This review examines how artificial intelligence and nanotechnology are being combined to transform solid waste management, offering more efficient and sustainable approaches to one of the world's most pressing environmental and public health challenges.
Revolutionizing Wastewater Reuse: A Critical Review of Innovative Treatment Technologies for a Sustainable Energy-Water Nexus
This review critically examines innovative wastewater treatment technologies for sustainable reuse, covering advances in membrane filtration, electrochemical processes, advanced oxidation, and emerging contaminant removal including microplastics, in the context of addressing global water scarcity.