通过机器学习技术在连续流中探索以热石为稀土元素的吸附剂的优化
Óscar Barros1,2, Pier Parpot1,2, Isabel C Neves1,2
1CEB-Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal.
Molecules (Basel, Switzerland)
|December 23, 2023
概括
无监督的机器学习 (ML) 有效地描述了热带石对稀土元素 (REE) 的吸附. 这种方法优化了吸附周期,实现了超过70%的REE去除和80%的回收.
科学领域:
- 环境科学 环境科学
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
背景情况:
- 稀土元素 (REEs) 对现代技术至关重要.
- 从水溶液中有效地回收废弃能源在环境和经济上都很重要.
- 焦石是对REE去除有前途的吸附剂.
研究的目的:
- 应用无监督机器学习 (ML) 来表征热带石的REE吸附.
- 为了优化吸附-脱附周期,以提高REE回收.
- 开发一个预测模型用于REE回收使用ML.
主要方法:
- 无监督的机器学习技术,包括主要组件分析 (PCA) 和K-Means集群.
- 连续流吸附-脱附实验. 连续流吸附-脱附实验.
- 开发用于REE回收估计的回归模型.
主要成果:
- ML成功评估了吸附结果,使得可以评估不同的吸附周期阶段.
- 超过70%的REEs被从水溶液中去除.
- 超过80%的被吸附的REEs被利用优化的脱吸周期回收.
结论:
- 无监督的ML提供了一个强大的工具,用于表征和优化基于热带石的REE吸附.
- 开发的回归模型可以预测REE回收.
- 这项研究证明了一种可行的方法,可以有效地去除和回收REE.
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