GOALI: Data-driven design of recycling tolerant aluminum alloys incorporating future material flows

金属生产占全球温室气体排放量的8%。提高材料效率必须在金属脱碳生产中发挥作用，因为这种策略现在是可用的，并且比新的生产方法在更短的时间内实现。改善材料消耗的一个策略是通过利用再循环改善材料回收。回收利用对铝尤其有利，因为使用回收材料的能源效益比初级消费要高得多。在数量和质量上显著改善回收利用，可以在必要的温室气体减排时间表内实现脱碳目标。为此，金属生产行业制定了增加回收材料使用的目标。面对不断变化的废料流和产品需求的变化，如果不将合金设计与未来报废废料流的考虑相结合，这些目标将无法实现。然而，传统上合金设计的重点是提高性能，而不太考虑对环境的影响或材料的回收能力。该项目将通过将可回收性和性能指标纳入设计过程来设计新合金，并考虑到废料流在未来的发展情况。该项目的目标是创建一个设计管道，可以与传统合金设计一起使用，以创建可回收的合金。可回收合金被定义为在生产过程中可以吸收大量废料的合金（沉淀），但也可以用于生产许多其他合金（来源）。本研究将通过(i)创建一个材料分布模型来为回收和未来的废料流提供信息，（ii）开发一个贝叶斯优化算法来有效地探索合金空间，以及（iii）利用合金成分和性能的约束来确定设计空间，从而解决汇和源之间的权衡问题。材料分布模型将是材料流分析和混合模型的结合，它将告知未来的材料流（成分、数量、废料流的价格和未来的需求）以及如何使用这些材料生产新的合金。然后，该模型将使用有效的优化方法在合金空间上最大化。最后，寻找最佳合金的优化将受到成分、热力学量和性能的限制。总的来说，这项工作的重点是合金设计，通过整合材料分布模型、有效优化和受限设计空间，优化废料在预计材料流中的使用。由于这些元素从未集成在一起，因此不仅方法是独特的，而且集成涉及到每个这些领域的进展。因此，提出的工作代表了可持续合金设计的一步，并整合和创新了该领域先前开发的方法。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Metals production contributes to 8% of greenhouse gas emissions worldwide. Improving materials efficiency must play a role in decarbonizing metals production because such strategies are available now and achievable along a shorter time horizon than novel production methods. One strategy to improve material consumption is through improved materials recovery using recycling. Recycling is particularly beneficial for aluminum, where energy benefits from use of recycled materials are much improved relative to primary consumption. Significantly improved recycling, both in terms of quantity and quality, can play a role in achieving decarbonization targets over the necessary greenhouse gas reduction timeline. To this end, metals production industries have set goals to increase the use of recycled content. In the face of an evolving scrap stream and shifts in product demand, these targets will not be achievable without coupling alloy design with considerations of future end-of-life scrap streams. However, alloy design has traditionally focused on improving performance without much regard for environmental impact or the ability to recycle the materials. This project will design new alloys by including recyclability in addition to performance metrics into the design process accounting for how scrap streams are expected to evolve in the future.The goal of this project is to create a design pipeline that can be used alongside traditional alloy design to create recycling-friendly alloys. Recycling-friendly alloys are defined as alloys that can incorporate a lot of scrap in their production (sink) but that can also be used in the production of a lot of other alloys (source). This research will address this trade-off between sink and source by (i) creating a materials distribution model to inform recycling and future scrap streams, (ii) developing a Bayesian optimization algorithm to effectively explore the alloy space, and (iii) identifying the design space using constraints on alloys compositions and properties. The material distribution model will be a combination of material flow analysis and blending model that will inform the future material streams (compositions, quantities, prices of scrap streams and future demand) and how those can be used to produce new alloys. This model will then be maximized over the alloy space using the efficient optimization method. Finally, the optimization to find the best alloy will be subject to constraints on compositions, thermodynamic quantities, and properties. Overall, this work focuses on alloy design that optimizes scrap use in projected material flows by integrating a materials distribution model, efficient optimization, and constrained design space. Since these elements have never been integrated together, not only is the approach unique, but the integration involves advances in each of these areas. Thus, the proposed work represents a step forward in sustainable alloy design and integrates and innovates on previously developed approaches in this area.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.