废水处理的机器学习方法:高斯过程回归和蒙特卡洛分析
Nimra Nadeem1,2, Zubair Khaliq2,3, Abdulaziz Bentalib4
1Department of Textile Engineering, National Textile University Faisalabad 37610 Pakistan.
Nanoscale advances
|June 12, 2025
概括
高斯过程回归 (GPR) 准确预测废水污染物的降解. 与环境处理应用的多项式回归相比,GPR提供了更好的洞察力和可靠性.
科学领域:
- 环境科学 环境科学
- 化学工程是化学工程的重要组成部分.
- 数据科学数据科学数据科学
背景情况:
- 废水处理效率依赖于对污染物降解的准确预测.
- 传统的回归模型可能缺乏对复杂环境相互作用的精度.
研究的目的:
- 评估高斯过程回归 (GPR) 以提高废水处理中的降解反应预测.
- 为了将GPR性能与多项式回归进行比较,用于模拟污染物降解.
主要方法:
- 用高斯过程回归 (GPR) 来建模退化.
- 分析的主要因素:催化剂 (CFA-ZnF),氧化剂 (H2O2) 和污染物 (MB) 度.
- 通过比较RPAE值和相关性分析来验证模型性能.
主要成果:
- 在多项式回归 (RPAE 2.2947) 上,GPR显示出更高的预测准确性 (RPAE 0.92689).
- CFA-ZnF和H2O2与降解呈现强烈的正相关性,而MB呈现弱负相关性.
- 与多项式回归不同的是,GPR可以同时解释多个预测效应,而不是多项式回归.
结论:
- 高斯过程回归 (GPR) 是一种更有效,更有洞察力的方法,用于废水处理中的污染物降解建模.
- GPR提供可靠的预测和对治疗过程动态的更深入的理解.
- 该研究强调了GPR在可持续和高效的废水管理方面的潜力.
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