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Papers
61,005 resultsShowing papers similar to A Systematic Review of Solid Waste Management (SWM) and Artificial Intelligence approach
ClearArtificial 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.
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.
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.
AI Techniques Aid for Optimizing the Collection System of Industrial Plastic Waste
This study applied artificial intelligence techniques to optimize collection routes and predict demand for industrial plastic waste pickup. AI methods outperformed traditional statistical approaches in accuracy and route efficiency. Smarter collection systems could significantly reduce costs and improve recovery rates for industrial plastic waste.
A Smart Garbage Classification based on Deep Learning
Researchers developed an AI-powered garbage classification system using deep learning to automatically sort waste categories. Accurate automated waste sorting could improve plastic recycling rates, reducing the amount of plastic that eventually breaks down into environmental microplastics.
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.
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.
Artificial Intelligence-Based Robotic Technique for Reusable Waste Materials
This paper describes an AI-based robotic arm system that uses a customized deep learning model to classify and sort waste materials including plastics and cartons by material type for automated recycling. The integrated system combines gripping, motion control, and AI-driven material classification into a full-automation architecture for waste recovery.
Enhancing Waste Management with a Deep Learning-based Automatic Garbage Classifier
This paper is not about microplastics; it presents a deep learning convolutional neural network system for automatically classifying garbage by material type to improve waste sorting efficiency and reduce the labor burden of manual waste management.
Application of AI-Enabled Computer Vision Technology for Segregation of Industrial Plastic Wastes
Researchers developed an AI-powered computer vision system to segregate mixed industrial plastic wastes by polymer type, addressing a key barrier to effective plastic recycling. The system achieved high classification accuracy across common plastic categories, demonstrating that machine vision can improve sorting efficiency and recycled plastic quality.
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.
Global Plastic Waste Management: Analyzing Trends, Economic and Social Implications, and Predictive Modeling Using Artificial Intelligence
This study analyzed global plastic waste management practices and used artificial intelligence models to predict future waste trends. The researchers found that current waste management systems are struggling to keep up with rising plastic production, posing threats to ecosystems, human health, and the economy. The AI models help forecast where waste generation is headed, which could inform better policy decisions.
Artificial Intelligence-Driven Optimization and Decision Support for Integrated Waste-to-Energy Systems in Climate-Vulnerable Megacities: A Case Study of Dhaka, Bangladesh
This study explored how artificial intelligence could optimize waste-to-energy systems in Dhaka, Bangladesh, a rapidly growing city facing severe waste management and energy challenges. Researchers evaluated AI-driven approaches for improving waste sorting, conversion efficiency, and energy output from municipal solid waste. The findings suggest that integrating AI into waste management infrastructure could help climate-vulnerable cities reduce landfill dependence and associated plastic pollution while generating cleaner energy.
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.
AI Applied to the Circular Economy: An Approach in the Wastewater Sector
This review explored how artificial intelligence can be applied to circular economy objectives in the wastewater sector, examining AI-driven approaches for optimizing water treatment, resource recovery, and system efficiency. The paper identified opportunities and challenges for integrating machine learning into water utility operations within an ecological transition framework.
An Automatic Garbage Classification System Based on Deep Learning
Researchers developed an automated garbage classification system using a deep learning algorithm based on ResNet-34, achieving 99% classification accuracy with a processing time of under one second per item. Automated waste sorting technology like this could improve the efficiency of plastic waste recovery and reduce mismanaged plastic that eventually becomes environmental pollution.
An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation
This review examines the growing application of artificial intelligence and machine learning in maritime and marine environment management. The study covers how AI technologies are being used to improve sustainability, efficiency, and regulatory compliance in the marine industry, including environmental monitoring relevant to pollution tracking.
A Critical Review on Artificial Intelligence—Based Microplastics Imaging Technology: Recent Advances, Hot-Spots and Challenges
Researchers reviewed the use of artificial intelligence and machine learning techniques for detecting and identifying microplastics in environmental samples. The study found that AI-based imaging tools can significantly speed up analysis and improve accuracy compared to traditional manual methods. However, challenges remain around standardizing datasets and making these tools accessible for routine environmental monitoring.
The supporting role of Artificial Intelligence and Machine/Deep Learning in monitoring the marine environment: a bibliometric analysis
This review examines the supporting role of artificial intelligence and machine learning in monitoring and managing plastic pollution, covering applications in remote sensing, image-based plastic detection, and predictive modeling of plastic fate. The authors identify deep learning for image classification and satellite-based detection as the most rapidly advancing AI applications in plastic pollution science.
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.
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.
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.
AI for Monitoring Ocean Plastic Pollution
This review assessed how artificial intelligence technologies—including satellite image analysis, computer vision, and machine learning—are being applied to monitor ocean plastic pollution. The authors found AI can dramatically expand spatial coverage and detection speed compared to traditional ship-based surveys, though ground-truth validation and data standardization remain challenges.
A Machine Arm to Assist in Trash Sorting using machine Learning and Object Detection
Not relevant to microplastics — this paper describes a robotic arm system that uses machine learning and computer vision to sort recyclable waste materials, focused on automation of waste sorting processes.