Department of Global Smart City, Sungkyunkwan University (SKKU), 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, 16419, Republic of Korea.
Department of Environmental Engineering, Gyeongsang National University, 33 Dongjin-ro, Jinju, 52725, Republic of Korea.
Chemosphere. 2024 Aug;361:142577. doi: 10.1016/j.chemosphere.2024.142577. Epub 2024 Jun 8.
Water distribution networks play a crucial role in ensuring a reliable water supply, yet they encounter challenges such as corrosion, scale formation, and biofilm growth due to interactions with environmental elements. Biofilms and corrosion layers are significant contaminants in water pipes, formed by complex interactions with pipe materials. As the structure of these contamination layers varies depending on the pipe material, it is essential to investigate the contamination layer for each material individually. Specifically, biofilm growth is typically investigated concerning organic sources, while the growth of humus layers is examined in relation to inorganic elements such as manganese (Mn), iron (Fe), and aluminum (Al), which are major elements and organic substances found in water pipes. Real-time imaging of recently contaminated layers can provide important insights to improve system performance by optimizing operations and cleaning processes. In this study, cast iron (7.10 ± 0.78 nm) exhibits greater surface roughness compared to PVC (5.60 ± 0.14 nm) and provides favorable conditions for biofilm formation due to its positive charge. Over a period of 425 h, the fouling layer on cast iron and PVC surfaces gradually increased in fouling thickness, porosity, roughness, and density, reaching maximum value of 29.72 ± 3.6 μm, 11.44 ± 1.1%, 41673 ± 1025.6 pixels, and 0.80 ± 0.3 fouling layer pixel/layer pixel for cast iron, and 8.15 ± 0.4 μm, 20.64 ± 0.9%, 35916.6 ± 755.7 pixels, and 0.58 ± 0.1 fouling layer pixel/layer pixel, respectively. Within the scope of the current research, CNN model demonstrates high correlation coefficients (0.98 and 0.91) in predicting biofilm thickness for cast iron and PVC. The model also presented high accuracy in predicting porosity for both materials (over 0.91 for cast iron and 0.96 for PVC). While the model accurately predicted biofilm roughness and density for cast iron (correlation coefficients 0.98 and 0.94, respectively), it had lower accuracy for PVC (correlation coefficients 0.92 for both parameters).
供水管网在确保可靠供水方面发挥着关键作用，但由于与环境因素的相互作用，它们会遇到腐蚀、结垢和生物膜生长等挑战。生物膜和腐蚀层是水管中的重要污染物，它们是通过与管道材料的复杂相互作用形成的。由于这些污染层的结构因管道材料而异，因此必须分别研究每种材料的污染层。具体而言，生物膜生长通常与有机来源有关，而腐殖质层的生长则与锰 (Mn)、铁 (Fe) 和铝 (Al) 等无机元素有关，这些元素是水管中主要的元素和有机物质。最近污染层的实时成像可以通过优化操作和清洁过程提供重要的见解，以提高系统性能。在这项研究中，与 PVC（5.60±0.14nm）相比，铸铁（7.10±0.78nm）具有更大的表面粗糙度，由于其带正电荷，因此为生物膜的形成提供了有利条件。在 425 小时的时间内，铸铁和 PVC 表面的污垢层的污垢厚度、孔隙率、粗糙度和密度逐渐增加，达到铸铁最大值 29.72±3.6μm、11.44±1.1%、41673±1025.6 像素和 0.80±0.3 污垢层像素/层像素，以及 PVC 的最大值 8.15±0.4μm、20.64±0.9%、35916.6±755.7 像素和 0.58±0.1 污垢层像素/层像素。在当前研究范围内，CNN 模型在预测铸铁和 PVC 的生物膜厚度方面表现出较高的相关系数（分别为 0.98 和 0.91）。该模型在预测两种材料的孔隙率方面也表现出很高的准确性（铸铁超过 0.91，PVC 超过 0.96）。虽然该模型可以准确预测铸铁的生物膜粗糙度和密度（相关系数分别为 0.98 和 0.94），但对 PVC 的预测准确性较低（两个参数的相关系数均为 0.92）。
Huan Jing Ke Xue. 2009-2-15
J Environ Sci (China). 2014-4-1
Zotero 插件