Using unsupervised learning to classify inlet water for more stable design of water reuse in industrial parks

Abstract

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. HIGHLIGHTS Validation of three unsupervised learning clustering algorithms in six real cases.; L2 distance improved based on water quality in the clustering algorithms.; Practical engineering applications consideration.; Machine clustering compared with human and ChatGPT's clustering.;

Keywords

• agnes
• dbscan
• inlet water classification
• k-means
• unsupervised learning
• water reuse