Provided herein is a method for reducing radiologically-contaminated waste. The method comprises treating radiologically-contaminated surfaces, wherein the radiologically-contaminated surfaces are treated with a surface treatment agent; treating radiologically-contaminated subsurfaces, wherein the radiologically-contaminated subsurfaces are treated with a surface/subsurface treatment agent; consolidating soil waste; employing real-time scanning technology to classify waste, wherein the classifying is based at least in part on a threshold of radiological contamination, and wherein the classified waste is sorted based on the classification; and disposing of the waste via at least one of different disposal routes, based at least in part on the classification.

Systems and methods for classifying materials utilizing one or more sensor systems, which may implement a machine learning system in order to identify or classify each of the materials, which may then be sorted into separate groups based on such an identification or classification. The machine learning system may utilize a neural network, and be previously trained to recognize and classify certain types of materials.

Provided is an apparatus for analyzing composition of electronic and electrical device part scraps which can determine a composition of part scraps in the electronic and electrical device part scraps in a short time, a device for processing electronic and electrical device part scraps, and a method for processing electronic and electrical device part scraps using those devices. An apparatus for analyzing a composition of electronic and electrical device part scraps including a classification data storage means for storing a classification data for extracting images of a plurality of component types of electronic and electrical device part scraps from a captured image of electronic and electrical device part scraps composed of the plurality of component types and classifying extracted images into each of the plurality of component types, a classification means for classifying the extracted images into each of the plurality of component types extracted from the captured image of the electronic and electrical device part scraps according to the classification data, and analysis means for analyzing at least one of an area, a number, an average particle size, and weight ratio of each of the plurality of component types classified by the classification means.

An aggregate production method includes procuring stone waste by-products, sorting the stone waste by-products by type and color, and subjecting the sorted stone waste by-products to a two-stage crushing process to produce aggregate. The aggregate is screened into different sizes and placed according to size into a bag or container.

A sorting apparatus is provided for sorting selected magnetically attractable articles from a stream of articles including non-selected magnetically attractable articles. The apparatus may include a conveyor for conveying the stream of articles. The conveyor may include a conveyor belt formed in an endless loop including a discharge end configured to launch the stream of articles off the conveyor. A conveyor guide may be located inside of the endless loop adjacent the discharge end. The conveyor guide may be configured to support the conveyor belt such that the conveyor belt slides on the conveyor guide along a downwardly curved path. An array of magnets may be arranged inside of the endless loop for interacting with the stream of articles as the stream of articles passes off the discharge end.

A scrap discrimination system and a scrap discrimination method that can improve scrap discrimination technology are provided. A scrap discrimination system includes a scrap part extraction model (221) that extracts, based on a camera image, a scrap part located in a central portion included in the camera image with reference to a window (107) defined in advance in an image, a scrap discrimination model (222), generated by teacher data including training images, that sorts grades of scrap and a ratio of each grade from a scrap image extracted by the scrap part extraction model (221), and an output interface (24) that outputs information on the grades of scrap and the ratio of each grade as discriminated based on the scrap image using the scrap discrimination model (222).

The present disclosure relates to a waste management system comprising a first waste bin having a first camera operatively configured to a controller. Images of waste captured by the first camera are analyzed by the controller to ensure that the waste is a plastic waste. Further, a categorization mechanism executing at the controller classifies the plastic waste in at least one of a plastic category using a machine learning mechanism. The system comprises a motor for shredding the plastic waste and a sieve for collecting the shredded plastic waste. Containers attached at an end of the sieve receive and store the shredded plastic waste. The system comprises a second waste bin having a second camera for capturing images of the general/wet waste and categorizing the images to alert user about potential food waste. Categorized wet waste is collected in the second bin.

An automated process for separating and recycling a broad mix of waste material including, but not limited to, paper and flexible plastics. A materials recovery facility (MRF) collects, presorts, and separates into individual commodities the material, then separates partially processed 2D blends from 3D containers, while refraining from any quality control of the 2D blends, and bales the 2D blends. A fiber center receives the bales from the MRF, aggregates the partially processed 2D blends, removes recyclable commodities including flexible plastics from the 2D blends, aggregates each recyclable commodity that is removed from the 2D blends, and forms separate commodity streams of clean paper and clean flexible plastics. Also provided are a related fiber center (and an infrastructure including the center) and at least one computer-readable non-transitory storage medium embodying software for performing the process.

An object recognition device includes an illuminator configured to illuminate an object, an imager configured to generate an image of the object such that overexposure occurs in the image, and circuitry configured to calculate a position of the object based on the image.

A suction head (1) for a waste sorting system, comprising a main body (2) for connection to a robot manipulator (3), and a resilient and flexible elongated suction cup (4) having a connecting end (5) and an opposing suction end (6). The connecting end (5) being releasably connected to the main body (2) and the suction end (6) is configured for engaging waste items. The main body (2) comprises a main connector (7) connectable to a suction system and the suction head (1) further comprises a suction channel (8) extending through the main connector (7), the main body (2) and the suction cup (4). The suction cup (4) comprises a plurality of corrugations (4a, 4b, 4c, 4d) providing moveability of the suction end (6) with respect to the main body (2) in a lateral X direction, a lateral Y direction, and longitudinal Z direction of the suction cup (4).