文献类型：期刊文献

英文题名：Bed filtration pressure drop prediction and accuracy evaluation using the Ergun equation with optimized dynamic parameters in industrial wastewater treatment

作者：Huang, Haitao[1];Ma, Hongpeng[2];Liu, Bing[1];Yang, Suwen[1];Wei, Qi[1];Zhang, Yanhong[1];Lv, Wenjie[1]

机构：[1]East China Univ Sci & Technol, Natl Engn Res Ctr Ind Wastewater Detoxicat & Resou, 130 Meilong Rd, Shanghai 200237, Peoples R China;[2]Northwestern Polytech Univ, Key Lab Special Funct & Smart Polymer Mat, Minist Ind & Informat Technol, Xian 710072, Shaanxi, Peoples R China

年份：2023

卷号：53

外文期刊名：JOURNAL OF WATER PROCESS ENGINEERING

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001011459300001)】；

基金：Acknowledgements This work was supported by the sponsorship of the National Natural Science Foundation of China (52030001) .

语种：英文

外文关键词：Industrial wastewater; SS particles; Bed filtration; Pressure drop model

摘要：Industrial wastewater frequently contains suspended solid (SS) particles, which may be extremely harmful to human health and the environment. The bed filtration method offers clear benefits for treating wastewater contaminated with SS particles. An important technical indicator of the method is the bed pressure drop, but modeling of its development during bed filtration has been poorly studied. In this study, the Ergun equation is selected as the basis of the pressure drop model, and its parameters are optimized. To evaluate the model's accuracy at various flow rates and total bed depths, filtration experiments were carried out. The findings demonstrate that the model has a high prediction accuracy, with prediction errors for the bed pressure drop in the deep stage being less than 5 %. By the end of the deep stage, the accumulated masses of SS particles in the 5cm layer below the bed surface in 15-cm and 25-cm depth beds accounted for about 85.1 % and 80.4 %, and were responsible for 51 % and 36 % of the pressure drop, respectively. And after the deep stage, less SS particles being retained as filter cake will benefit long-term operation. The optimization model provided in this study and the discovery of the pressure drop distribution within the bed can provide a theoretical basis for the design or improvement of the bed filtration process.