The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is increased revenue and processing potential for scrap recyclers in the US. The artificially-intelligent algorithm designed in this project 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. With unstable international commodity trade, US scrap processors must reduce reliance on exporting low-value scrap to maintain profitable business models. Additionally, US scrap must exercise optimal processing schedules to prevent scrap surplus domestically while providing consumers with recycled products that are functionally equivalent to new products. The latter offers an environmentally-friendly scrap-to-product option that reduces the energy required for production and the amount of harmful CO2 released. Aluminum scrap recycling has been practiced for decades; however, the majority of post-consumer scrap is downcycled leaving revenue and environmental benefits untapped. Non-ferrous auto-shred was, on average, sold for $0.33/lb. less than its actual value in 2017, which equates to nearly $1 billion in opportunity cost. Artificially intelligent sorting systems will enable scrap processors to reach higher profit margins and meet environmental goals. The proposed project will completely automate scrap sortation. The advent of multi-sort capability encourages the need for preliminary research to identify how to operate sensor-based sorters optimally. Artificial intelligence can meet this need. The intellectual merit of this project is the development of an artificially-intelligent algorithm that is capable of optimizing scrap sortation in real-time. The research objectives include:?(1) identify all data sources in the scrap recycling process that ?can ?influence intelligent decision making (2)?design a database to host identified data sources such as compositional, market, inventory, and sales data and (3) design a customized artificially-intelligent algorithm for the? scrap ?recycling industry to develop sorting criteria in real-time. To meet these objectives, a dynamic material flow model will be designed to analyze and host all relevant sensor, market, and experimental data streams to minimize data pre-processing requirements. Concurrently, aluminum scrap will be characterized to investigate how frequently and to what degree composition fluctuates. The database that hosts all supportive data streams will be designed to store and integrate all relevant data streams seamlessly. Finally, using an 80/20 training/testing data split with 5-fold cross-validation, the machine learning algorithm will be selected and optimized to provide the lowest error rate for suggested sorting criteria. 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.
