We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Neural network based prediction of the efficacy of ball milling to separate cable waste materials
Summary
Researchers developed a neural network — a type of artificial intelligence — to predict how well ball milling separates copper from plastic (PVC) in cable recycling, finding that the weight of cables loaded and the force of impact were the most critical factors. Machine learning tools like this could help scale up plastic and metal recycling to industrial levels.
Abstract Material recycling technologies are essential for achieving a circular economy while reducing greenhouse gas emissions. However, most of them remain in laboratory development. Machine learning (ML) can promote industrial application while maximising yield and environmental performance. Herein, an asynchronous-parallel recurrent neural network was developed to predict the dynamic behaviour when separating copper and poly(vinyl chloride) components from the cable waste. The model was trained with six datasets (treatment conditions) at 3600 epochs. High accuracy was confirmed based on a mean-square error of 0.0015–0.0145 between the prediction and experimental results. The quantitative relationship between the input features and the separation yield was identified using sensitivity analysis. The charged weight of cables and impact energy were determined as the critical factors affecting the separation efficiency. The ML framework can be widely applied to recycling technologies to reveal the process mechanism and establish a quantitative relationship between process variables and treatment outputs.
Sign in to start a discussion.
More Papers Like This
Assessment of machine learning-based methods predictive suitability for migration pollutants from microplastics degradation
Researchers assessed the usefulness of machine learning methods for predicting the migration of chemical pollutants from microplastics. The study found that artificial neural networks and support vector methods showed strong potential for modeling and predicting the leaching of plasticizers and other contaminants, which could reduce the need for extensive laboratory analyses.
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.
The use of artificial neural networks in modelling migration pollutants from the degradation of microplastics
Researchers used artificial neural networks to model the emission of additives from degrading microplastics, finding that machine learning could predict migration patterns from the vast range of polymer types, chemical structures, and environmental conditions involved. This approach could reduce the need for extensive laboratory testing by identifying high-risk scenarios for further investigation.
Evaluation and Prediction of Production Yields in Plastic Manufacturing Industry Using Artificial Neural Network
This study evaluated and predicted production yield in a plastic manufacturing company using artificial neural network modeling. Predictive tools that improve manufacturing efficiency can reduce material waste and off-specification plastic products that may contribute to environmental plastic pollution.
AI-Driven Framework Development for Predictive Classification of Microplastic Concentration of Aquatic Systems in the United States
Researchers compared four machine learning models—logistic regression, random forest, support vector machine, and a neural network—for predicting microplastic density in US coastal waters across three regions. The support vector machine performed best with 93.94% average accuracy, demonstrating the potential of AI-driven tools for microplastic monitoring.