XU Xiao-yi,GE Dan-dan,YU Lan,et al.Analysis of Fine Grit Separation Characteristics in a Vortex-type Grit Chamber Based on Regression Orthogonal Design[J].China Water & Wastewater,2021,37(11):63-69.
基于回归正交设计的沉砂池细砂分离特性分析
- Title:
- Analysis of Fine Grit Separation Characteristics in a Vortex-type Grit Chamber Based on Regression Orthogonal Design
- 摘要:
- 悬浮颗粒粒度特性是影响旋流沉砂池设计与运行效能的重要因素之一，我国城镇污水中无机物粒径细微，不宜直接采用常规参数取值进行设计。针对城镇污水中无机悬浮颗粒的粒度分布特征，构建了比氏旋流沉砂池模型，以回归正交试验设计为手段，开展了数值模拟与实际物理模型相结合的研究。基于悬浮颗粒不同粒径范围（0~100、100~200、>200 μm），建立了分离效率与影响因素之间的多元回归模型，明确了各因素对不同粒径颗粒去除率的影响规律及优化运行参数。不同粒度范围悬浮颗粒去除的各最优工况中，搅拌桨转速和水力停留时间各不相同，适当增加水力停留时间和转速，可以在一定程度上提高对细微无机颗粒的分离去除效能，但对粒径<100 μm的颗粒去除率提升空间较低。在优化工况条件下，物理模型试验结果与回归模型分析的误差为1.86%~4.20%，具有良好可信度。
- Abstract:
- Particle size characteristic of suspended particles is one of the important factors affecting the design and operation efficiency of a vortex-type grit chamber. The particle size of inorganic substances in urban sewage in China is very small, so it is inappropriate to use the conventional parameters to design or operate a grit chamber directly. According to the distribution characteristics of inorganic suspended solid (ISS) in urban sewage, a Pista vortex-type grit chamber model was established, and separation characteristics of fine grit in the vortex-type grit chamber were simulated by means of regression orthogonal design. Based on different particle size range of the suspended particles (0-100 μm, 100-200 μm and >200 μm), a multivariate regression model between ISS removal efficiency and influencing factors was developed, and the influence rule of each factor on particle removal efficiency and the optimal conditions were determined. Rotation speed and hydraulic residence time (HRT) were different in the optimal conditions for ISS removal in various particle size ranges. Although the increase of HRT and rotation speed could improve the separation and removal efficiency of the fine inorganic particles to a certain extent, it had a poor effect on the removal efficiency of particles with particle size less than 100 μm. Under the optimal operation conditions, the error between the actual physical model results and the regression prediction was 1.86%-4.20%, which had good reliability.
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