LEQUIA, Institute of the Environment, Universitat de Girona. C/Maria Aurèlia Capmany, 69, E-17003 Girona, Catalonia, Spain.
LEQUIA, Institute of the Environment, Universitat de Girona. C/Maria Aurèlia Capmany, 69, E-17003 Girona, Catalonia, Spain.
Sci Total Environ. 2021 Dec 10;799:149398. doi: 10.1016/j.scitotenv.2021.149398. Epub 2021 Aug 2.
Coagulation is the main process for removing natural organic matter (NOM), considered to be the major disinfection by-products (DBPs) precursor in drinking water production. In this work, k-means clusters analysis were used to classify influent waters from two different surface drinking water treatment plants (DWTPs) located in the Mediterranean region. From this, enhanced coagulation models based on response surface methodology (RSM) were then developed to optimise coagulation at two water catchments (river and reservoir). The cluster analysis classified the water quality of the raw waters into two groups related to baseline and peak organic loads. The developed enhanced coagulation models were based on the turbidity, total organic carbon (TOC) and UV removals. Sensitivity analysis applied to the models (after predictors selection) determined the factors relative individual contributions for each DWTP scenario. Then, profile plots for enhanced coagulation were studied to identify the optimal levels for each case. Models mean R were 0.85 and 0.86 in baseline and 0.85 and 0.84 in peak scenario for river and reservoir catchments, respectively. Results of this study indicate that the surface water quality variation in river DWTP is seasonal and is expressed by an increase of turbidity, while in the reservoir DWTP is related to extreme weather events showing high levels of dissolved organic load (TOC and UV). During baseline cases, where raw waters present low levels of organics, the three factors optimal adjustment should be ensured to optimise coagulation. Then, during peak scenarios, where influent waters present high organics, the optimal for enhanced coagulation relies on the correct adjustment of C. The presented work provides models for drinking water production aimed to propose the optimum conditions for enhanced coagulation, considering the influent water characteristics under different weather conditions.
混凝是去除天然有机物（NOM）的主要过程，被认为是饮用水生产中主要消毒副产物（DBPs）的前体。在这项工作中，使用 K 均值聚类分析对来自位于地中海地区的两个不同地表水饮用水处理厂（DWTP）的进水进行分类。在此基础上，然后基于响应面法（RSM）开发了强化混凝模型，以优化两个集水区（河流和水库）的混凝。聚类分析将原水水质分为与基线和峰值有机负荷相关的两组。开发的强化混凝模型基于浊度、总有机碳（TOC）和 UV 去除率。对模型（在预测因子选择后）进行的敏感性分析确定了每个 DWTP 场景的相对个别贡献因素。然后，研究了强化混凝的剖面图，以确定每个案例的最佳水平。河流和水库集水区的基线和峰值情景下模型的平均 R 分别为 0.85 和 0.86，以及 0.85 和 0.84。这项研究的结果表明，河流 DWTP 的地表水水质变化具有季节性，表现为浊度增加，而水库 DWTP 与极端天气事件有关，表现为溶解有机负荷（TOC 和 UV）较高。在基线情况下，原水有机物含量较低，应确保调整三个因素以优化混凝。然后，在峰值情况下，进水有机物含量较高，强化混凝的最佳条件取决于 C 的正确调整。本研究提供了饮用水生产模型，旨在根据不同天气条件下的进水特性，提出强化混凝的最佳条件。
