Creating rapid, transparent, and updateable material flow analyses

2040013（库珀）。该项目的目标是提高材料流分析（MFA）的执行速度，并透明地传达和减少结果中的不确定性，从而实现常规和可更新的流程，以在任何规模（从工厂到供应链）进行MFA。我们的目标是量化MFA参数和网络结构的不确定性，并在MFA早期构思智能数据收集策略，从而降低高置信度MFA结果的成本。多边贸易协定是向循环经济过渡的关键工具。它们揭示了材料效率和共生的机会，以及供应链局部变化对系统级的影响（例如，电动汽车部署增加对锂提取和制造排放的影响）。详细的MFA目前是一个昂贵的，时间密集的数据收集过程，随后通常是对冲突和缺失数据的手动协调。随之而来的不确定性通常对最终用户是不可见的。 这项研究将开发一种方法，使用计算方法来降低数据收集成本，改善输出数据，并更好地管理不确定性。首先，贝叶斯推断将用于更新新数据收集时的不确定性。这种方法将与最优实验设计（OED）的原则相结合，以确定下一个数据记录收集，可以导致最大的不确定性降低，衡量为香农信息增益。第二，本项目将通过提出一组候选网络结构，然后通过贝叶斯模型选择来确定最合适的结构，从而研究MFA中的网络结构不确定性。OED还将被用于计划数据采集，以揭示最佳网络结构。第三，所开发的技术将用于生成历史年份的MFA，并使用误差传播来计算在用库存水平和回收率，以创建一个随时间变化的动态MFA。这些贡献将通过开源代码访问，并通过研究美国钢铁流和全球聚合物流来展示。这项工作将产生一个指南，用于从专家那里引出MFA变量的概率分布，使该领域远离概率分布的任意分配。通过使用《牛津英语词典》的原则进行有针对性的数据收集，提高多功能分析信心的成本将降低。此外，网络结构不确定性（即，MFA中两个节点之间是否存在节点或流）是普遍存在的，并且可能严重破坏协调流预测的可靠性，但尚未得到严格的研究。本研究将探讨网络结构的不确定性，形成一个合奏的网络结构候选人使用专家的意见和随机网络结构生成。OED还将被开发来规划数据采集，以揭示最佳的MFA网络结构，同时最大限度地减少数据采集成本。通过这项工作，新的计算算法将被创建，例如，一个有效的分类随机优化过程，结合域知识的MFA网络结构，和信息理论OED标准的模型选择需要一个三重嵌套蒙特卡罗估计。该研究旨在使公司，大学和政府能够使用廉价但统计上严格的物理流分析，为提高资源效率的政策，实践和投资决策提供信息。MFA成本和不确定性的降低将为更强大的预测打开大门。例如，跨时间的详细年度MFA可以揭示行业之间的动态（例如，钢、铝和水泥关系）和政策（例如，关税），需要更可靠的综合评估模型。为了提高影响力，将在国际工业生态学会会议上介绍方法和研究结果，制作的代码和数据集将存放在工业生态GitHub存储库中，学习工具（例如，关于静态和动态多边金融机构的信息）发布到在线学习C-SED网站。该项目将建立一个持续的，跨单位和多所大学的中西部合作基础设施，重点是数据科学的可持续发展决策的主题。Ypsilanti STEMM中学的一项推广计划将把可持续材料的关键课程与FIRST机器人设计原则相结合，帮助URM学生掌握追求可持续工程职业的技能。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

2040013 (Cooper). The goal of this project is to improve the speed at which material flow analysis (MFA) is performed and to transparently communicate and reduce the uncertainty in the results, thus enabling a routine and updatable process to make MFAs at any scale (from factories to supply chains). The objective is to quantify the uncertainty of MFA parameters and network structures, and to conceive intelligent data collection strategies early in the MFA, thus decreasing the cost for high-confidence MFA results. MFAs are critical tools in the transition towards a circular economy. They reveal opportunities for material efficiency and symbiosis, as well as the system-level impacts of localized changes to the supply chain (e.g., the effect of increased electric vehicle deployment on lithium extraction and manufacturing emissions). Detailed MFA is currently a costly, time-intensive data collection process followed by a typically manual reconciliation of conflicting and missing data. Attendant uncertainties are typically invisible to the end-user. This research will develop an approach where computational methods are used to reduce data collection costs, improve output data, and better manage uncertainty. First, Bayesian inference will be used to update uncertainty as new data is collected. This approach will be integrated with the principles of optimal experimental design (OED) to identify the next data records to collect that can lead to the largest uncertainty reduction, measured as the Shannon information gain. Second, the project will study the network structure uncertainty in MFAs by proposing a set of candidate network structures followed by Bayesian model selection to identify the most suitable structure. OED will also be adopted to plan data acquisition that reveals the best network structure. Third, the developed techniques will used to generate MFAs for historical years, and use error propagation to compute in-use stock levels and recycling rates in creating a time-dependent dynamic MFA. These contributions will be accessible through open-source code and demonstrated by studying U.S. steel flows and global polymer flows. The work will result in a guide for eliciting probability distributions for MFA variables from experts, moving the field away from the arbitrary allocation of probability distributions. The costs of improving MFA confidence will be reduced by performing targeted data collection using the principles of OED. Furthermore, network structure uncertainty (i.e., the existence or absence of nodes or flows between two nodes in an MFA) is pervasive and can severely undermine the reliability of the reconciled flow predictions, but has yet to be rigorously studied. This research will investigate network structure uncertainty by forming an ensemble of network structure candidates using both expert advice and randomized network structure generation. OED will also be developed to plan data acquisition that reveals the best MFA network structure while minimizing data collection costs. Through this work, new computational algorithms will be created, for example, an efficient categorical stochastic optimization procedure that incorporates domain knowledge about the MFA network structures, and an information-theoretic OED criterion for model-selection entailing a triple-nested Monte Carlo estimator. This research aims to enable companies, universities, and governments to use an inexpensive but statistically rigorous analysis of physical flows to inform policy, practice, and investment decisions that will increase resource efficiency. Reduced MFA costs and uncertainties will open the door to more powerful forecasting. For example, detailed annual MFAs across time can reveal the dynamics between industries (e.g., steel, aluminum, and cement nexuses) and policies (e.g., tariffs) that are needed for more reliable integrated assessment models. To promote impact, methods and findings will be presented at the Conference of the International Society of Industrial Ecology, produced codes and datasets will be deposited in the Industrial Ecology GitHub repository, and learning tools (e.g., on static and dynamic MFAs) posted to the online learning C-SED website. The project will build a sustained, cross-unit and multi-university Midwest collaboration infrastructure focusing on the topic of data science for sustainability decisions. An outreach program at Ypsilanti STEMM Middle College will integrate key lessons on sustainable materials with FIRST Robotics design principles, helping to empower URM students with the skills to pursue sustainable engineering careers.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.