School of Environment, Harbin Institute of Technology, 73 Huanghe Road, Harbin, Heilongjiang, China; Suzhou Sujing Environmental Engineering Co., Ltd, 2 Weixin Road, Suzhou, Jiangsu, China.
School of Environment, Harbin Institute of Technology, 73 Huanghe Road, Harbin, Heilongjiang, China.
Water Sci Technol. 2024 Apr;89(7):1757-1770. doi: 10.2166/wst.2024.087. Epub 2024 Mar 19.
The water reuse facilities of industrial parks face the challenge of managing a growing variety of wastewater sources as their inlet water. Typically, this clustering outcome is designed by engineers with extensive expertise. This paper presents an innovative application of unsupervised learning methods to classify inlet water in Chinese water reuse stations, aiming to reduce reliance on engineer experience. The concept of 'water quality distance' was incorporated into three unsupervised learning clustering algorithms (K-means, DBSCAN, and AGNES), which were validated through six case studies. Of the six cases, three were employed to illustrate the feasibility of the unsupervised learning clustering algorithm. The results indicated that the clustering algorithm exhibited greater stability and excellence compared to both artificial clustering and ChatGPT-based clustering. The remaining three cases were utilized to showcase the reliability of the three clustering algorithms. The findings revealed that the AGNES algorithm demonstrated superior potential application ability. The average purity in six cases of K-means, DBSCAN, and AGNES were 0.947, 0.852, and 0.955, respectively.
工业园区的水资源再利用设施面临着管理不断增加的各种废水来源的挑战，这些废水是其进水。通常，这种聚类结果是由具有丰富专业知识的工程师设计的。本文提出了一种将无监督学习方法应用于中国水资源再利用站进水分类的创新应用，旨在减少对工程师经验的依赖。“水质距离”的概念被纳入三种无监督学习聚类算法（K-means、DBSCAN 和 AGNES）中，并通过六个案例研究进行了验证。在这六个案例中，有三个案例说明了无监督学习聚类算法的可行性。结果表明，与人工聚类和基于 ChatGPT 的聚类相比，聚类算法表现出更高的稳定性和优越性。其余三个案例用于展示三种聚类算法的可靠性。研究结果表明，AGNES 算法具有更好的潜在应用能力。在 K-means、DBSCAN 和 AGNES 的六个案例中，平均纯度分别为 0.947、0.852 和 0.955。
