机器学习引导的性能预测前向透聚合物膜用于回收
Meng Wang1, Zhanlin Ji2, Yingchao Dong1
1School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, PR China.
Water research
|April 30, 2025
概括
预测水中的回收是具有挑战性的. 先进的机器学习准确地预测了去除效率,提高了水净化和资源回收.
科学领域:
- 环境科学 环境科学
- 水处理技术水处理技术
- 材料科学 材料科学 材料科学
背景情况:
- 从水中可持续地回收对于资源管理至关重要.
- 预测从海水等非传统来源的回收仍然是一个挑战.
- 向前透 (FO) 是水处理和资源回收的一个有前途的技术.
研究的目的:
- 开发一个有效的策略,预测回收性能在前向透.
- 通过机器学习探索影响回收的关键因素.
- 为了提高回收效率在向前透系统.
主要方法:
- 先进机器学习回归模型的比较.
- 使用XGBoost模型与粒子群优化算法相结合.
- 分析包括pH值,度和膜方向在内的因素.
主要成果:
- 确定pH值,度和膜方向作为影响回收的关键因素.
- 使用XGBoost-PSO混合模型实现了高预测准确度 (95.4%的R平方).
- 预测的排斥率达到了98.28%,超过了现有的方法.
结论:
- 先进的机器学习,特别是XGBoost-PSO模型,可以准确预测回收.
- 开发的模型显著提高了除前透系统的性能.
- 这种方法有可能改善水质,并使资源回收应用成为可能.
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