SmartSort utilizes machine learning algorithms and computer vision to identify and categorize a wide array of non-standard recyclable materials in real-time.

Cameras and sensors placed along the sorting conveyor belt capture images and other data about incoming materials.

These data are processed using AI models that have been trained on extensive datasets of various recyclables, allowing the system to differentiate between different materials based on shape, size, color, and density.

Once identified, robotic arms or pneumatic systems sort these items into appropriate categories, significantly reducing human labor and improving sorting accuracy.

The system continuously learns and improves from the materials it processes, increasing its efficiency over time.

SmartSort offers unprecedented accuracy and efficiency in processing non-standard recyclables, reducing operational costs and labor demands.

By automating the sorting process with machine learning, it complements sustainability goals and increases material recovery rates.

The technology is grounded in existing machine learning and computer vision advances, but requires substantial initial investment to build and train the AI models specifically for varied material types.

Scaling production and installation across facilities will involve significant capital and partnership with industry stakeholders.