Application of a new HMW framework derived ANN model for optimization of aquatic dissolved organic matter removal by coagulation

凝结 溶解有机碳 偏最小二乘回归 基质(化学分析) 人工神经网络 化学 有机质 水处理 生物系统 数学 环境工程 环境化学 环境科学 色谱法 人工智能 统计 计算机科学 精神科 有机化学 生物 心理学
作者
Guocheng Zhu,Nana Xiong,Chuang Wang,Zhongwu Li,Andrew Hursthouse
出处
期刊:Chemosphere [Elsevier BV]
卷期号:262: 127723-127723 被引量:11
标识
DOI:10.1016/j.chemosphere.2020.127723
摘要

Removing dissolved organic matter (DOM) with polyaluminium chloride is one of the primary goals of drinking water treatment. In this study, a new HMW framework was proposed, which divided the factors affecting coagulation into three parts consisting of hydraulic condition (H), metal salt (M), and background water matrix (W). In this framework, H, M and W were assumed to be interacted with each other and combined to determine coagulation efficiency. We investigated the feasibility of the framework to determine the treatment efficiency through mathematical models. Results showed that non-linear artificial neural network (ANN) model was a better fit to the experimental data than the linear partial least squares (PLS) model: the ANN model could explain 76% of the total variations while the PLS could only explain 71%. The PLS did not follow the variations of observed values adequately. These experiments showed that the interaction between the HMW framework components were not simple linear relationships. The ANN model was able to optimize the composition of the HMW framework improving the efficiency of DOM removal through the components of HMW such as velocity gradient (G value), coagulant dosage, solution pH, and background water matrix. Overall, HMW framework is a new classification of factors affecting coagulation, leading to a better understanding of the coagulation process and sensitivity to influencing variables.
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