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SBIR Phase II: Artificially Intelligent Solution to Maximize Value Creation and Upcycling Potential of Aluminum Scrap

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 2026106
Agency Tracking Number: 2026106
Amount: $992,485.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: MI
Solicitation Number: N/A
Timeline
Solicitation Year: 2020
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-09-15
Award End Date (Contract End Date): 2022-08-31
Small Business Information
104 Prescott St.
Worcester, MA 01605
United States
DUNS: 080880428
HUBZone Owned: Yes
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Sean Kelly
 (508) 733-1808
 Sean.kelly@solvusglobal.com
Business Contact
 Sean Kelly
Phone: (508) 733-1808
Email: Sean.kelly@solvusglobal.com
Research Institution
N/A
Abstract

This Small Business Innovation Research Phase II project will develop an artificially intelligent sorting software (AISS) for the metal scrap processing industry. Scrap packages of unknown composition can result in costly melt losses and increased consumption of primary aluminum to balance out composition. Recycled aluminum production consumes 5% of the total energy required to form primary aluminum and yields significantly less waste per ton. The AISS aims to (1) enable production of high-quality, maximum-value scrap by combining market and compositional data to optimize sorting criteria and (2) use artificial intelligence to predict optimally salable scrap packages; the estimated value of this information is $1 billion, representing over 4% of total industry revenue. This SBIR Phase II project will advance translation of a system combining market value and compositional data to produce maximum-value nonferrous scrap sortation decisions. The proposed work will deliver the AISS to scrap processors for identification, in real time, of maximum-value commodity packages by analyzing several data streams. It will recommend optimized sorting criteria for maximum profit generation, predict scrap stream composition, and monitor scrap-package composition for guaranteed quality. This project will: (1) enhance the AISS to include stream prediction and real-time data integration, (2) scale integration and testing to validate the AISS hardware package, and (3) complete integration, testing, and commercial-scale optimization of the AISS software-hardware package with sensor-sorting systems. This project will develop the first real-time adaptive sortation algorithm introduced to the non-ferrous scrap sortation industry. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

* Information listed above is at the time of submission. *

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