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

Guocheng Zhu*, Nana Xiong, Chuang Wang, Zhongwu Li, Andrew S. Hursthouse

*Corresponding author for this work

Research output: Contribution to journalArticle


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.
Original languageEnglish
Article number127723
Early online date29 Jul 2020
Publication statusE-pub ahead of print - 29 Jul 2020


  • water treatment
  • coagulation
  • dissolved organic matter
  • polyaluminium chloride
  • artificial neural network (ANN)

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  • Prizes

    Liancheng Friendship Award of Xiangtan City, Hunan.

    Hursthouse, Andrew (Recipient), 12 Dec 2017

    Prize: Prize (including medals and awards)

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