Creating rapid, transparent, and updateable material flow analyses

创建快速、透明且可更新的物料流分析

• 批准号: 2040013
• 负责人: Daniel Cooper
• 金额: $ 37.54万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-15 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2040013&HistoricalAwards=false
• 关键词: Creating; rapid; transparent; updateable; material

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.

2040013（库珀）。该项目的目的是提高执行材料流分析（MFA）的速度，并透明地沟通并减少结果的不确定性，从而实现常规和可更新的过程，以在任何规模（从工厂到供应链）进行任何规模的MFA。目的是量化MFA参数和网络结构的不确定性，并在MFA的早期构想智能数据收集策略，从而降低高信任MFA结果的成本。 MFA是向循环经济过渡的关键工具。它们揭示了材料效率和共生的机会，以及局部变化对供应链的系统级影响（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目前是一个昂贵的，耗时的数据收集过程，然后是相互冲突和丢失数据的典型手动核对。最终用户通常是不可见的。 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，提高输出数据并更好地管理不确定性。首先，随着收集新数据，贝叶斯推论将用于更新不确定性。这种方法将与最佳实验设计（OED）的原理集成在一起，以确定可以收集的下一个数据记录，这些数据记录可能导致最大的不确定性降低，这是根据香农信息增益来衡量的。其次，该项目将通过提出一组候选网络结构，然后进行贝叶斯模型选择来研究MFA中的网络结构不确定性，以识别最合适的结构。 OED也将采用用于计划揭示最佳网络结构的数据采集。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在创建时间依赖时间的动态MFA时计算中使用的库存水平和回收率。这些贡献将通过开源法规访问，并通过研究美国钢流和全球聚合物流量来证明。这项工作将为启发专家的MFA变量引发概率分布提供指南，从而使现场远离概率分布的任意分配。通过使用OED原理进行有针对性的数据收集，将降低提高MFA置信度的成本。此外，网络结构的不确定性（即MFA中两个节点之间的节点或流的存在或不存在）是普遍的，并且可能严重破坏了和解流动预测的可靠性，但尚未严格研究。这项研究将通过使用专家建议和随机网络结构生成组成网络结构的候选组合来研究网络结构的不确定性。 OED还将开发用于计划数据采集，以揭示最佳的MFA网络结构，同时最大程度地降低数据收集成本。通过这项工作，将创建新的计算算法，例如，一种有效的分类随机优化过程，该过程结合了有关MFA网络结构的域知识，以及用于模型选择的信息理论OED标准，需要三局的Monte Carlo估计器。这项研究旨在使公司，大学和政府能够使用廉价但统计上严格的物理流量分析来为政策，实践和投资决策提供信息，从而提高资源效率。降低的MFA成本和不确定性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（例如钢铁，铝和水泥连接）与更可靠的综合评估模型所需的策略（例如关税）之间的动态。为了促进影响，将在国际工业生态学会会议上提出方法和发现，生产的代码和数据集将存入工业生态学GitHub存储库中，以及学习工具（例如，张贴在静态和动态的MFA上）将其存放到在线学习C-SED网站上。该项目将建立一个持续的，跨单元和多元大学的中西部协作基础设施，重点介绍了可持续性决策数据科学主题。 Ypsilanti Stemm中学的一项外展计划将与第一机器人设计原则结合有关可持续材料的关键课程，有助于使URM学生具有从事可持续工程职业的技能。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子的智力和广泛的影响来评估Criteria criteria criteria criteria。

项目成果

Expert elicitation and data noise learning for material flow analysis using Bayesian inference

使用贝叶斯推理进行物质流分析的专家启发和数据噪声学习

• 发表时间: 2023
• 期刊: Journal of industrial ecology
• 影响因子: 5.9
• 作者: Dong, Jiayuan;Liao, Jiankan;Huan, Xun;Cooper, Daniel
• 通讯作者: Cooper, Daniel

A Bayesian Approach to Modeling Unit Manufacturing Process Environmental Impacts using Limited Data with Case Studies on Laser Powder Bed Fusion Cumulative Energy Demand

使用有限数据对单元制造过程环境影响进行建模的贝叶斯方法以及激光粉床聚变累积能量需求的案例研究

• 发表时间: 2023
• 期刊: Procedia CIRP
• 作者: Liao, Jiankan;Huan, Xun;Haapala, Karl;Cooper, Daniel
• 通讯作者: Cooper, Daniel