NUMERICAL SIMULATION OF PARTICLE AGGREGATION IN GRID FLOCCULATION TANK BASED ON CFD-PBM
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摘要: 为探究絮凝池内部颗粒聚并行为的影响因素,采用群体平衡模型(PBM)模拟颗粒聚并行为,通过CFD-PBM耦合方法,研究了絮凝颗粒初始平均粒径和絮凝颗粒体积分数对栅条絮凝池内部颗粒聚并行为的影响,提出了颗粒平均粒径增长率指标来表征CFD-PBM耦合方法下絮凝池内部絮凝效果。结果表明:1)当进口颗粒体积分数为0.1时,随着絮凝颗粒初始平均粒径由10 μm增长到76 μm,出口处粒径由117.54 μm增长到154.82 μm,但颗粒平均粒径增长率由1075.4%迅速降低至103.7%;2)在进口颗粒初始粒径为40 μm时,随着颗粒体积分数由0.05增长至0.2,出口位置颗粒粒径由127.16 μm增加至155.74 μm,而平均粒径增长率也随之由208.7%增加至289.4%;3)从整体上来看,当颗粒体积分数从0.05增加到0.1时,平均粒径增长率的变化最快,絮凝效果的提升最为显著。研究结果对于栅条絮凝池的结构设计指导和絮凝效果评价具有重要意义。Abstract: In order to explore the influencing factors of particle aggregation behavior in the flocculation tank, the population balance model (PBM) was used to simulate the particle aggregation behavior in this paper. By means of CFD-PBM coupling method, the effects of initial average particle size and volume fraction of flocculated particles on particle aggregation behavior in a grid flocculation tank were studied. An index of average particle size growth rate was proposed to characterize the flocculation effect inside the flocculation tank under the CFD-PBM coupling method. The results showed that: 1) when the inlet particle volume fraction was 0.1, as the initial average particle size of flocculated particles increased from 10 μm to 76 μm, the particle size at the outlet increased from 117.54 μm to 154.82 μm, but the average particle size growth rate decreased from 1075.4% to 103.7%; 2) when the initial particle size of the inlet particles was 40 μm, as the particle volume fraction increased from 0.05 to 0.2, the particle size at the outlet position increased from 127.16 μm to 155.74 μm, and the average particle size growth rate also increased from 208.7% to 289.4%; 3) overall, within the particle volume fraction range of 0.05 to 0.1, the average particle size growth rate was the highest, and the flocculation effect improved most significantly.
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[1] 伏雨,龙云,肖波,等. 栅条絮凝池内部流场及颗粒运动状态模拟分析[J]. 环境工程, 2021, 39(4): 25-29. [2] 朱昭福. 微絮凝直接过滤工艺在自来水厂扩建工程中的应用[J]. 工程建设与设计, 2020(11): 237-238. [3] KHELIFA A, HILL P S. Models for effective density and settling velocity of flocs[J]. Journal of Hydraulic Research, 2006, 44(3): 390-401. [4] BIGGS C A, LANT P A. Activated sludge flocculation: on-line determination of floc size and the effect of shear[J]. Water Research, 2000, 34(9): 2542-2550. [5] COUFORT C, BOUYER D, LINÉ A, et al. Modelling of flocculation using a population balance equation[J]. Chemical Engineering & Processing Process Intensification, 2007, 46(12): 1264-1273. [6] COUFORT C, DUMAS C, BOUYER D, et al. Analysis of floc size distributions in a mixing tank[J]. Chemical Engineering & Processing Process Intensification, 2008, 47(3): 287-29410. [7] HULBURT H M, KATZ S. Some problems in particle technology: a statistical mechanical formulation[J]. Chemical Engineering Science, 1964, 19(8): 555-574. [8] MAHONEY A W, RAMKRISHNA D. Efficient solution of population balance equations with discontinuities by finite elements[J]. Chemical Engineering Science, 2002,57(7):1107-1119. [9] GERSTLAUER A, MITROVIĆ A, MOTZ S, et al. A population model for crystallization processes using two independent particle properties[J]. Chemical Engineering Science, 2001, 56(7): 2553-2565. [10] IMMANUEL C D, CORDEIRO C F, SUNDARAM S S, et al. Modeling of particle size distribution in emulsion co-polymerization: comparison with experimental data and parametric sensitivity studies[J]. Computers & Chemical Engineering, 2002, 26(7/8): 1133-1152. [11] MILLIES M, MEWES D. Interfacial area density in bubbly flow[J]. Chemical Engineering and Processing-Processing Intensification, 1999, 38(4/5/6): 307-319. [12] BIGGS C A, LANT P A. Modelling activated sludge flocculation using population balances[J]. Powder Technology, 2002, 124(3):201-211. [13] 李振亮. 基于群体平衡的活性污泥絮凝动力学[D]. 重庆:重庆大学, 2014. [14] 宋峻林,唐荣联,王洪. 絮凝过程CFD数值模拟研究[J]. 现代化工, 2018, 38(8): 231-235. [15] 张世豪,艾恒雨,崔婉莹,等. 基于CFD模拟的絮凝效果评价指标研究[J]. 中国给水排水, 2022, 38(5): 45-53. [16] 刘存,王庆涛,陈翔宇,等. 网格絮凝池结构参数对流场影响的数值模拟[J]. 水资源与水工程学报, 2018, 29(4): 162-167. [17] 姚萌,冉治霖,相会强,等. 搅拌桨叶类型对絮凝池内流场特性的仿真模拟[C]//环境工程2019年全国学术年会,北京,2019. [18] GOLZARIJALAL M, ZOKAEE ASHTIANI F, DABIR B. Modeling of microalgal shear-induced flocculation and sedimentation using a coupled CFD-population balance approach[J]. Biotechnol Prog, 2018,34(1):160-174. [19] YANG N, WEN Y. Numerical simulation of secondary sedimentation tank based on population balance model[J]. IOP Conference Series Earth and Environmental Science, 2019, 358: 32052. [20] LI Z L, LU P L, ZHANG D J, et al. Simulation of floc size distribution in flocculation of activated sludge using population balance model with modified expressions for the aggregation and breakage[J]. Mathematical Problems in Engineering, 2019, 6:5243860. [21] ABRAHAMSON J. Collision rates of small particles in a vigorously turbulent fluid[J]. Chemical Engineering Science, 1975, 30(11): 1371-1379. [22] SAFFMAN P G, TURNER J S. On the collision of drops in turbulent clouds[J]. Journal of Fluid Mechanics, 1956, 1(1): 16-30. [23] HOUNSLOW M J, RYALL R L, MARSHALL V R. A discretized population balance for nucleation, growth, and aggregation[J]. Aiche Journal, 1988, 34(11):1821-1832. [24] ELDUAYEN-ECHAVE B, LIZARRALDE I, LARRAONA G S, et al. A new mass-based discretized population balance model for precipitation processes: application to struvite precipitation[J]. Water Research, 2019,155:26-41. [25] BIGGS C A, LANT P A. Modelling the effect of shear history on activated sludge flocculation[J]. Water Science and Technology, 2003, 47(11): 251-257.
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