Material Separation

Material separation research focuses on developing efficient and accurate methods for sorting diverse materials, driven by needs in recycling, chemical purification, and manufacturing. Current efforts leverage machine learning, particularly deep learning models like convolutional neural networks and reinforcement learning algorithms (e.g., PPO, DQN), alongside advanced signal processing techniques (e.g., hyperspectral imaging) and robotic manipulation to analyze and separate materials based on physical and chemical properties. These advancements offer significant potential for improving resource recovery, streamlining industrial processes, and reducing environmental impact across various sectors.