Decision-Focused Active Learning for Scale-Aware Critical-Materials Recovery

面向规模感知关键材料回收的决策导向主动学习

Abstract: 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.

摘要： 选择用于规模化生产的回收工艺，需要将实验室结果与产品需求、工艺成本以及规模效应联系起来。我们分析了太平洋西北国家实验室（PNNL）用于自主选择性沉淀的“关键元素回收与优化计算机智能”（CICERO）工作流中的记录。

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.

主动学习利用先前的结果来选择实验。在结合拟合模型和回收钕铁硼（NdFeB）磁体记录的条件回顾性基准测试中，主动学习比非自适应空间填充法能以更少的实验次数找到最佳记录结果。“富集度”是指所选稀土与铁的比例相对于原料中比例的比值。自适应策略在 16 到 24 个孔（单次实验）内即可达到记录的富集最大值，而对比组则需要 48 个孔。我们的两阶段重构方法在 16 个孔时与两种自适应替代方案持平。

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.

对回收钐钴（SmCo）磁体的条件分析显示，第二轮实验在纯度和标称产率（基于假设起始量计算的回收率）之间存在权衡——钕铁硼第一轮的路径在富集度上存在差异。针对石油和天然气开采产生的采出水的排名，取决于需要进一步确认的相态和稀释假设。

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.

我们建议根据批次在下游贝叶斯风险中的预期降低程度来选择批次：即在当前认知下，所有可用工艺决策中的最小预期损失。在探索性模拟中，一种过滤候选方案的混合方法比现有的跨路径和条件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。

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.

我们概述了一项在统一损失和记录标准下的预注册前瞻性测试，该测试要求明确测量和记录、定义工艺决策及相关产出、提供可信的经济输入，并在预期的规模下进行验证。