Technological advancements and a growing global population are increasing material consumption at a rapid rate. Scrap metal current practices are unable to maximize material value recovery, meet increasing consumer specifications, or adjust sorting criteria in real time as markets shift.
VALIS is a machine learning model fed by multiple critical data streams to make more intelligent and optimized sorting decisions with guaranteed profitability and quality. We aim to will develop the first real-time adaptive sortation algorithm introduced to the non-ferrous scrap sortation industry.
Solvus Global has been awarded an NSF SBIR Phase 1 grant to develop an artificial intelligence algorithm to support the advancement of scrap metal sorting.
Embarking into NSF SBIR Phase II, VALIS seeks to complete integration, testing, and commercial-scale optimization of the VALIS software-hardware package with sensor-sorting systems.
VALIS will enable domestic scrap processors to become more competitive within the material supply chain by giving them the ability to adapt, in real-time, to an ever-changing material consumption climate. By bringing value and intelligence to the world of recycling, VALIS can completely automate scrap sortation for the next generation.