用机器学习评估植物性凝固剂以消除度和预测凝固剂剂量
Poloko Ivy Namane1,2, Moatlhodi Wise Letshwenyo1,2, Abid Yahya1,3
1Botswana International University of Science and Technology, Palapye, Botswana.
Environmental technology
|December 11, 2024
概括
六种植物性凝固剂有效地消除了废水的度,在性条件下具有最佳性能. 机器学习准确地预测了凝血效率,建议天然凝血剂作为可持续的替代品.
科学领域:
- 环境科学 环境科学
- 水处理工程水处理工程
- 材料科学 材料科学 材料科学
背景情况:
- 传统的化学凝固剂对环境和健康构成风险.
- 越来越需要可持续和环保的水处理解决方案.
- 植物性凝固剂由于其可用性和生物降解性,提供了一个有前途的替代品.
研究的目的:
- 调查六种植物性凝固剂在废水度去除方面的有效性.
- 描述这些天然凝固剂的物理和化学特性.
- 以优化凝血条件 (剂量和pH值) 并使用机器学习预测性能.
主要方法:
- 使用扫描电子显微镜 (SEM),X射线光 (XRF) 和X射线衍射的凝固剂的表征.
- 通过使用不同剂量和pH值的瓶式试验评估凝血性能.
- 机器学习算法的应用用于性能预测.
主要成果:
- SEM检测到不规则的,粗的表面;XRF检测到基本元素 (Fe,Ca,S,K).
- 在性条件下,度的最佳去除发生在性条件下,效率在59%到92.24%之间.
- 机器学习模型准确地预测了最佳凝固剂剂量和pH值,以有效降低度.
结论:
- 植物性凝固剂显示出在废水度去除方面具有显著的潜力.
- 性pH条件提高了大多数测试植物材料的凝固效率.
- 机器学习集成为优化基于天然凝固剂的水处理过程提供了强大的工具.
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