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用机器学习实现可持续资源回收的-溶液聚合物膜度

Zhanlin Ji¹, Hao Guan², Meng Wang²

¹College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, PR China.

|August 23, 2025

概括

机器学习优化了纳米过膜从盐水中恢复资源. 受压力和粗度的影响,是高水透率和离子排斥的关键.

科学领域:

材料科学
化学工程
环境科学

背景情况:

从海水和盐水中有效地回收宝贵资源对于全球可持续性至关重要.
传统的膜方法存在性能限制和不清楚的机制.

研究的目的:

为优化纳米过膜性能开发机器学习框架.
阐明管理资源回收的结构-流程-性能关系.

主要方法:

使用机器学习模型系统优化纳米过膜.
综合沙普利增量解释 (SHAP) 和部分依赖性分析以了解影响因素.
使用粒子群优化来提高性能.

主要成果:

在分离性能中确定了固体障碍为主导因素.
通过膜压力 (5-10 bar) 和表面粗度 (3.4-3.5 nm) 来确定高性能.
实现了高透率 (15.12 L·m−2·h−1),具有优异的 Li+ 和 Mg2+ 排斥.

结论:

机器学习为优化膜过程和理解机制提供了强大的范式.
这种方法为推进基于膜的盐水资源回收提供了理论指导.

关键词:

全球优化算法资源回收机器学习纳米过业绩预测

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