Cloud-Model-Based Feature Engineering to Analyze the Energy–Water Nexus of a Full-Scale Wastewater Treatment Plant

Abstract

Wastewater treatment plants (WWTPs) are important and energy-intensive municipal infrastructures. High energy consumption and relatively low operating performance are major challenges from the perspective of carbon neutrality. However, water–energy nexus analysis and models for WWTPs have rarely been reported to date. In this study, a cloud-model-based energy consumption analysis (CMECA) of a WWTP was conducted to explore the relationship between influent and energy consumption by clustering its influent’s parameters. The principal component analysis (PCA) and K-means clustering were applied to classify the influent condition using water quality and volume data. The energy consumption of the WWTP is divided into five standard evaluation levels, and its cloud digital characteristics (CDCs) were extracted according to bilateral constraints and golden ratio methods. Our results showed that the energy consumption distribution gradually dispersed and deviated from the Gaussian distribution with decreased water concentration and quantity. The days with high energy efficiency were extracted via the clustering method from the influent category of excessive energy consumption, represented by a compact-type energy consumption distribution curve to identify the influent conditions that affect the steady distribution of energy consumption. The local WWTP has high energy consumption with 0.3613 kW·h·m−3 despite low influent concentration and volumes, across four consumption levels from low (I) to relatively high (IV), showing an unsatisfactory operation and management level. The average oxygenation capacity, internal reflux ratio, and external reflux ratio during high energy efficiency days recognized by further clustering were obtained (0.2924–0.3703 kg O2·m−3, 1.9576–2.4787, and 0.6603–0.8361, respectively), which could be used as a guide for the days with low energy efficiency. Consequently, this study offers a water–energy nexus analysis method to identify influent conditions with operational management anomalies and can be used as an empirical reference for the optimized operation of WWTPs.

Keywords

• Wastewater treatment plants
• Cloud-model theory
• Data mining
• Principal component analysis
• K-means clustering
• Cloud-model-based energy consumption analysis