使用无监督学习来分类入口水,以便在工业园区中重新利用水的设计更为稳定
Kan Chen1, Xiaofei Shi2, Zhihao Zhang2
1School of Environment, Harbin Institute of Technology, 73 Huanghe Road, Harbin, Heilongjiang, China; Suzhou Sujing Environmental Engineering Co., Ltd, 2 Weixin Road, Suzhou, Jiangsu, China.
概括
无监督学习方法有效地分类工业废水,减少对专家工程师的依赖. AGNES算法在水再利用站表现出卓越的性能,提高了管理效率.
科学领域:
- 环境工程 环境工程
- 数据科学数据科学数据科学
- 水处理技术水处理技术
背景情况:
- 工业园区面临的挑战是,在水再利用设施中管理各种废水源.
- 目前的进水分类严重依赖于经验丰富的工程师,限制了可扩展性和一致性.
- 自动化这种分类对于高效可靠的水再利用操作至关重要.
研究的目的:
- 应用无监督学习方法来对中国水回收站的进水进行分类.
- 减少在废水源管理中对人类专业知识的依赖.
- 评估不同聚类算法在水质分类中的有效性.
主要方法:
- 将"水质距离"纳入K-means,DBSCAN和AGNES集群算法.
- 使用来自中国水再利用站的六个案例研究验证了算法.
- 将无监督学习集群与人工和基于ChatGPT的集群方法进行了比较.
主要成果:
- 与人工和基于ChatGPT的方法相比,无监督学习集群显示出更高的稳定性和性能.
- AGNES算法显示,在水的再利用中,实际应用的潜力最高.
- 对于K-means,DBSCAN和AGNES的平均纯度分数分别为0.947,0.852和0.955.
结论:
- 无监督学习为分类工业废水源提供了强大而可靠的方法.
- AGNES算法对提高水再利用管理的效率和准确性特别有希望.
- 这项研究提供了一个数据驱动的框架,用于优化水再利用设施的运行.
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