发表机构
Coactive Inc.; Pacific Northwest National Laboratory（Coactive 公司; 太平洋西北国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出决策聚焦主动学习，通过预期降低下游贝叶斯风险来选择实验批次，以更少实验实现关键材料回收的规模放大优化。
AI 中文摘要
选择用于规模放大的回收过程需要将实验室结果与产品要求、过程成本和规模效应联系起来。我们分析了太平洋西北国家实验室的“关键元素回收与优化计算机智能”（CICERO）工作流中用于自主选择性沉淀的记录。主动学习利用先前的结果来选择实验。在一个条件回顾性基准测试中，使用拟合模型和回收的钕铁硼（NdFeB）磁体记录，主动学习以比非自适应空间填充更少的实验找到了最佳记录结果。富集度是所选稀土与铁的比例相对于进料中的比例。自适应策略在16至24个孔（单个实验）内达到记录的最大富集度，而非自适应空间填充则需要48个孔。我们的两阶段重构在16个孔处与两种自适应替代方案持平。对回收的钐钴（SmCo）磁体的条件分析显示，第2轮在纯度和标称产率（根据假设的起始量计算的回收率）之间存在权衡——NdFeB第1轮的路线在富集度上有所不同。来自油气开采的产出水的排名取决于需要确认的相和稀释假设。我们提出根据批次对下游贝叶斯风险的预期降低来选择批次：即在当前信念下，可用过程决策中的最小期望损失。在探索性模拟中，一种过滤候选的混合方法比已实现的跨路线和条件的联合搜索具有更低的估计损失。涉及合成两阶段策略的差异相对于估计不确定性而言较小。我们概述了一个预注册的前瞻性测试，采用共享损失和日志记录标准，要求明确的测量和记录、定义的过程决策和相关输出、可信的经济输入，以及在预期规模上的验证。
英文摘要
Choosing a recovery process for scale-up requires connecting laboratory results with product requirements, process costs, and scale effects. We analyze records from Pacific Northwest National Laboratory's Computer Intelligence for Critical Element Recovery and Optimization (CICERO) workflow for autonomous selective precipitation. Active learning uses prior results to choose experiments. In a conditional retrospective benchmark with fitted models and recycled neodymium-iron-boron (NdFeB) magnet records, active learning finds the best recorded result with fewer experiments than nonadaptive space filling. Enrichment is the selected rare-earth-to-iron ratio relative to that in the feed. Adaptive policies reach the recorded enrichment maximum by 16 to 24 wells (individual experiments), versus 48. Our two-stage reconstruction ties two adaptive alternatives at 16 wells. Conditional analyses of recycled samarium-cobalt (SmCo) magnets show a Round 2 tradeoff between purity and nominal yield, the recovery fraction calculated from an assumed starting amount - NdFeB Round 1 routes differ in enrichment. Rankings for produced water from oil and gas extraction depend on phase and dilution assumptions requiring confirmation. We propose choosing batches by their expected reduction in downstream Bayes risk: the minimum expected loss among available process decisions under current beliefs. In exploratory simulations, a hybrid that filters candidates has lower estimated loss than the implemented joint search across routes and conditions. Differences involving the synthetic two-stage policy are small relative to estimation uncertainty. We outline a pre-registered prospective test under a shared loss and logging standard, requiring clarified measurements and records, a defined process decision and relevant outputs, credible economic inputs, and validation at the intended scale.
