Creating rapid, transparent, and updateable material flow analyses

该项目的目标是提高进行物流分析(MFA)的速度，并透明地传达和减少结果中的不确定性，从而使常规和可更新的流程能够在任何规模(从工厂到供应链)制造MFA。其目的是量化MFA参数和网络结构的不确定性，并在MFA的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。MFA是向循环经济过渡的关键工具。它们揭示了物质效率和共生的机会，以及供应链局部变化的系统一级影响(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、耗时的数据收集过程，然后通常是对冲突和丢失的数据进行人工核对。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，其中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，随着新数据的收集，贝叶斯推理将被用来更新不确定性。这种方法将与最优实验设计(OED)原则相结合，以确定下一步要收集的数据记录，这些记录可以导致最大的不确定度降低，以香农信息增益衡量。其次，该项目将通过提出一组候选网络结构来研究MFA中的网络结构不确定性，然后选择贝叶斯模型来确定最合适的结构。还将采用OED来规划数据采集，以揭示最佳网络结构。第三，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使用中的库存水平和回收率，以创建随时间变化的动态MFA。这些贡献将通过开放源代码获得，并通过研究美国钢铁流动和全球聚合物流动来展示。这项工作将产生一份从专家那里得出MFA变量的概率分布的指南，使该领域远离对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，将降低提高MFA信心的成本。此外，网络结构的不确定性(即MFA中两个节点之间是否存在节点或流)是普遍存在的，并且会严重破坏协调流预测的可靠性，但尚未得到严格的研究。这项研究将通过使用专家建议和随机网络结构生成来形成网络结构候选集合来研究网络结构的不确定性。还将开发OED以规划数据获取，以揭示最佳的MFA网络结构，同时最大限度地减少数据收集成本。通过这项工作，将创建新的计算算法，例如，结合关于MFA网络结构的领域知识的有效分类随机优化程序，以及需要三重嵌套蒙特卡罗估计器的模型选择的信息论OED准则。这项研究旨在使公司、大学和政府能够使用低成本但在统计上严格的物理流动分析，为政策、实践和投资决策提供信息，从而提高资源效率。减少MFA成本和不确定性将为更强大的预测打开大门。例如，随着时间的推移，详细的年度MFA可以揭示行业(例如钢铁、铝和水泥关联)和政策(例如关税)之间的动态，这是建立更可靠的综合评估模型所需的。该项目将建立一个持续的、跨单位和多所大学的中西部协作基础设施，重点是可持续发展决策的数据科学主题。

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.