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

SBIR 第二阶段：人工智能解决方案，最大限度地提高废铝的价值创造和升级回收潜力

• 批准号: 2026106
• 负责人: Sean Kelly
• 金额: $ 99.25万
• 依托单位: SOLVUS GLOBAL LLC
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026106&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Artificially; Intelligent

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.

这个小企业创新研究二期项目将为金属废料加工行业开发人工智能分拣软件(AISS)。成分未知的废铝包装可能会导致昂贵的熔体损失，并增加原铝的消耗，以平衡成分。再生铝的生产消耗了形成原铝所需总能源的5%，而且每吨产生的废物明显减少。AISS的目标是(1)通过结合市场和成分数据来优化分类标准，从而能够生产高质量、最高价值的废钢；(2)使用人工智能来预测最适合销售的废钢包装；这些信息的估计价值为10亿美元，占行业总收入的4%以上。这个SBIR第二阶段项目将推进一个结合了市场价值和成分数据的系统的翻译，以产生最大价值的有色金属废料分类决策。拟议的工作将向报废处理商提供AISS，以便通过分析几个数据流实时识别最大价值的商品包。它将推荐优化的分拣标准，以最大限度地产生利润，预测废品流成分，并监控废品包装成分，以保证质量。该项目将：(1)增强AISS，以包括流量预测和实时数据集成；(2)规模集成和测试，以验证AISS硬件包；以及(3)AISS软硬件包与传感器分拣系统的完全集成、测试和商业规模优化。该项目将开发第一个引入有色金属废料分类行业的实时自适应分类算法。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。