Department of Environment, Land and Infrastructure Engineering (DIATI), Politecnico di Torino, Corso Duca degli Abruzzi, Torino 10129, Italy E-mail:
School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Lindstedtsvägen 3, Stockholm 10044, Sweden and Science for Life Laboratory, Tomtebodavägen 23A, Solna 17165, Sweden.
Water Sci Technol. 2020 Apr;81(8):1541-1551. doi: 10.2166/wst.2020.220.
This paper outlines a hybrid modeling approach to facilitate weather-based operation and energy optimization for the largest Italian wastewater treatment plant (WWTP). Two clustering methods, K-means algorithm and Gaussian mixture model (GMM) based on the expectation-maximization (EM) algorithm, were applied to an extensive dataset of historical and meteorological records. This study addresses the problem of determining the intrinsic structure of clustered data when no information other than the observed values is available. Two quantitative indexes, namely the Bayesian information criterion (BIC) and the Silhouette coefficient using Euclidean distance, as well as two general criteria, were implemented to assess the clustering quality. Furthermore, seven weather-based influent scenarios were introduced to the process simulation model, and sets of aeration strategies are proposed. The results indicate that incorporating weather-based aeration strategies in the operation of the WWTP improves plant energy efficiency.
本文概述了一种混合建模方法，以促进意大利最大的污水处理厂（WWTP）的基于天气的运行和能源优化。两种聚类方法，K-均值算法和基于期望最大化（EM）算法的高斯混合模型（GMM），应用于历史和气象记录的广泛数据集。本研究解决了当除了观测值之外没有其他信息可用时确定聚类数据内在结构的问题。采用两个定量指标，即贝叶斯信息准则（BIC）和基于欧几里得距离的轮廓系数，以及两个一般准则，来评估聚类质量。此外，还向过程模拟模型引入了七种基于天气的进水情景，并提出了一系列曝气策略。结果表明，在 WWTP 的运行中采用基于天气的曝气策略可以提高工厂的能源效率。
