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61,005 resultsShowing papers similar to An Overview of the Latest Developments and Potential Paths for Artificial Intelligence in Wastewater Treatment Systems
ClearA 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.
Application of Artificial Intelligence in the Management of Coagulation Treatment Engineering System
Researchers reviewed the application of artificial intelligence and neural networks in water treatment coagulation systems. The study found that AI-based approaches can effectively predict water quality parameters and optimize chemical dosing, potentially improving the removal of contaminants including microplastics from drinking water treatment processes.
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.
Innovative approaches to greywater micropollutant removal: AI-driven solutions and future outlook
This review examines how artificial intelligence, particularly neural networks, can improve the treatment of greywater (household wastewater from sinks, showers, and laundry) that contains microplastics and other pollutants. AI models offer better predictions of treatment outcomes than traditional methods, which could lead to more effective removal of microplastics from wastewater. Since greywater is a significant source of microplastics entering the environment, better treatment could reduce human exposure.
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.
AI-assisted Microplastics Removal
This review explored how artificial intelligence is being used to improve the detection and removal of microplastics from water and the environment. Researchers found that machine learning techniques can enhance the identification of microplastic particles and optimize treatment processes like filtration and coagulation. The study suggests that AI-driven approaches could overcome many of the efficiency and cost limitations of conventional microplastic removal methods.
AI-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.
Prediction and Optimization of Process Parameters using Artificial Intelligence and Machine Learning Models
This review examined how artificial intelligence and machine learning models are being used to predict and optimize parameters for removing heavy metals and textile dyes from water. Researchers evaluated common AI approaches including artificial neural networks and genetic algorithms for improving water treatment efficiency. The study highlights the growing role of computational tools in designing more effective environmental remediation processes.
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.
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.
Design of an Efficient Model for Microplastic Removal in Wastewater using Advanced Filtration, Nanotechnology, and Bioremediation
This paper proposed an advanced machine learning model to design and optimize microplastic removal in wastewater treatment, using process parameters to predict removal efficiency. The intelligent model outperformed conventional design approaches in predicting treatment outcomes.
The Role of Artificial Intelligence in Microplastic Pollution Studies and Management
This review explores how artificial intelligence is transforming microplastic research, from automating detection in microscopy images and spectral analysis to predicting how plastics interact with pollutants and living organisms. AI-powered sensors and real-time monitoring systems are also being integrated into wastewater treatment to reduce microplastic release, making the technology a powerful tool for both understanding and managing plastic pollution.
Smart Water, Smart Models: Algorithmic Assessment of Water Quality under Evolving Chemical and Industrial Stressors
This review examines how machine learning approaches — including deep neural networks, hybrid physics-data models, and reinforcement learning — can be applied to detect and predict emerging chemical pollutants such as microplastics and recycling byproducts in water quality monitoring systems.
Comparing the Performance of Machine Learning and Deep Learning Algorithms in Wastewater Treatment Process
This study compared machine learning and deep learning algorithms for predicting wastewater treatment plant performance, finding that modified ensemble and stacked models performed best. Machine learning approaches for optimizing wastewater treatment could improve the removal of microplastics alongside conventional pollutants.
Advancing microplastic pollution management in aquatic environments through artificial intelligence
This review examines how artificial intelligence and robotics are being applied to tackle microplastic pollution in aquatic environments, covering waste collection, particle identification, and degradation monitoring. Researchers highlight several successful AI-driven projects deployed by countries and organizations around the world. The study suggests that integrating AI with traditional environmental methods holds significant promise for improving both the speed and accuracy of microplastic management.
Alleviating Health Risks for Water Safety: A Systematic Review on Artificial Intelligence-Assisted Modelling of Proximity-Dependent Emerging Pollutants in Aquatic Systems
This systematic review summarizes how artificial intelligence can help track emerging pollutants, including microplastics, in water systems. It highlights that AI-driven models can predict contamination patterns more efficiently than traditional methods, which could help protect drinking water safety and public health.
Recent developments in water purification
This review surveys the latest advanced water purification technologies developed in response to growing global water contamination challenges, including pollution from microplastics, dyes, heavy metals, pesticides, and pharmaceuticals. It covers approaches such as hybrid oxidation systems, novel membrane technologies, plasma-based treatment, and AI-driven smart purification systems. While not a primary research study on microplastics specifically, the review situates microplastic removal within the broader landscape of water treatment innovation.
Detecting Chemical Contaminants in Water Using AI
This review examines how artificial intelligence and machine learning tools are being applied to detect chemical contaminants in water, including microplastics, covering sensor technologies, data processing approaches, and the potential for real-time monitoring systems.
Harnessing Deep Learning for Real-Time Water Quality Assessment: A Sustainable Solution
Researchers developed a deep learning system that can predict water quality in real time based on measurements like pH, turbidity, and dissolved solids. While not directly about microplastics, this kind of AI-powered monitoring tool could eventually be adapted to detect microplastic contamination in water supplies more quickly and affordably than current lab-based methods.
Emerging Technologies in WWTP Control Systems for Sustainable Water Management
This review examines emerging control technologies for wastewater treatment plants -- including AI, machine learning, SCADA, and IoT sensors -- contrasting them with traditional PID controllers and highlighting their potential for real-time monitoring, predictive analytics, and improved microplastics removal performance.
Enhancing water quality prediction: a machine learning approach across diverse water environments
Researchers compared seven machine learning models for predicting water quality parameters using six years of wastewater treatment plant data. The gradient boosting model performed best overall, accurately predicting parameters related to water contamination. While the study focuses on general water quality rather than microplastics specifically, these predictive tools could be applied to monitoring microplastic-relevant conditions in treatment systems.
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.
Harnessing Artificial Intelligence for Microplastic Pollution Control in Lakes: Detection, Prediction and Removal
This review examines how artificial intelligence techniques are being applied to detect, predict, and assist in the removal of microplastic pollution in lake environments, surveying current AI-driven approaches to environmental monitoring and remediation.
Role of AI in Microplastic Pollution Detection and management studies
This review assessed how artificial intelligence approaches—including machine learning and deep learning—are being applied to detect, identify, and monitor microplastics in environmental and biological samples. The authors found AI substantially accelerates microplastic characterization workflows but that training data quality and standardization across studies remains a limiting factor.