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Papers
61,005 resultsShowing papers similar to AI Techniques Aid for Optimizing the Collection System of Industrial Plastic Waste
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.
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.
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.
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.
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.
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.
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.
Development of a Decision Support System Prototype for Time and Cost Reduction in Collecting Recyclable Waste
This study developed a decision support system to optimize routing for waste pickers collecting recyclable materials, reducing collection time and cost. Improving recyclable waste collection efficiency is important for diverting plastic waste from the environment where it would fragment into microplastics.
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.
Artificial intelligence for modeling and reducing microplastic in marine environments: A review of current evidence
This review examines how artificial intelligence is being applied to address marine microplastic pollution, including modeling accumulation zones, developing real-time detection systems using remote sensing and robotics, and creating AI-based filtration technologies. The study suggests that while AI holds significant promise for predicting microplastic flows and supporting targeted cleanup efforts, challenges remain around data availability, model refinement, and international collaboration.
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.
Optimizing plastics recycling networks
Researchers developed mathematical optimization models — including linear programming tools — to help plan efficient plastic recycling networks that can tolerate some contamination from mixed plastic waste streams. These models could help overcome a key barrier to large-scale recycling by intelligently matching waste sources with the plants best equipped to handle them.
Managing Marine Environmental Pollution using Artificial Intelligence
This review explores how artificial intelligence technologies are being developed to monitor and manage marine environmental pollution, including plastic contamination. The study suggests that AI-based approaches such as automated detection and predictive modeling offer promising opportunities for understanding ocean pollution and supporting sustainability goals.
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.
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.
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.
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.
GRUBin: Time-Series Forecasting-Based Efficient Garbage Monitoring and Management System for Smart Cities
Researchers developed GRUBin, a smart waste monitoring system using time-series forecasting with GRU neural networks to predict bin fill levels and optimize collection schedules, outperforming IoT-only approaches in reducing unnecessary waste collection trips in smart city environments.
Enhancing global microplastic pollution analysis using machine learning: a longitudinal study of seasonal trends and anomaly detection
This study used machine learning — specifically the L-BFGS-B optimization algorithm — combined with traditional environmental data to analyze global microplastic pollution patterns, forecast concentrations in data-sparse regions, and identify seasonal trends and anomalies. The approach generated high-resolution global pollution heatmaps and identified clusters of similarly affected areas, offering a way to prioritize monitoring and cleanup resources worldwide. Applying AI to environmental data in this way could dramatically improve our ability to understand and respond to the global scale of plastic pollution.
Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management
This review explores how artificial intelligence and Internet of Things sensors can be used to detect and monitor environmental pollutants, including microplastics, in air, water, and soil. Machine learning methods show promise for improving pollution tracking and prediction, but challenges remain around data sharing and model reliability. Advanced monitoring technology could play a key role in identifying and managing microplastic contamination in the environment.
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.
Artificial intelligence-empowered collection and characterization of microplastics: A review
This review examines how artificial intelligence tools like robots and machine learning are being used to collect, identify, and characterize microplastic pollution more efficiently. Better detection technology matters for human health because accurately measuring microplastic contamination in water and soil is the first step toward understanding and reducing our exposure.
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.
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.