Abstract:
An important aspect of water treatment is removing fine-grain materials from water. Due to the properties of fine-grain materials, they are difficult to remove from water. During the sedimentation process, which takes place in settling tanks, such materials are removed. The sedimentation process is often accompanied by coagulation and flocculation processes, which form aggregates of particles (flocs) from the fine-grained material particles in a suspension (non-grainy suspension). This kind of suspension (consisting of aggregates of particles or flocs) shows a different behaviour when falling compared with classic grainy suspensions. The main goal and novelty of this article are to propose (and test) a modification of the often used Stokes\' formula with the addition of fractal geometry into the calculation of the terminal velocity of free-falling particles in order to overcome Stokes\' formula\'s limitation, thus obtaining the sedimentation process efficiency. Because of this fractal modification, it is possible to use the simple and elegant Stokes\' formula in order to calculate better the terminal velocity of non-grainy particles-aggregates or flocs-and thus obtain the sedimentation efficiency for the whole range of suspensions, both non-grainy and grainy. The results obtained in this article show that the sedimentation process efficiency calculated by using the modified formula based on the fractal geometry morphology of particles (the proposed fractal method) describes and agrees more with the data from the experiment than the sedimentation efficiency calculated only based on particle size (classic method).
摘要:
水处理的一个重要方面是从水中去除细粒材料。由于细粒材料的特性,它们很难从水中去除。在沉降过程中,发生在沉淀池中,这些材料被移除。沉淀过程往往伴随着混凝和絮凝过程,其在悬浮液(非粒状悬浮液)中从细粒材料颗粒形成颗粒(絮凝物)的聚集体。与经典的粒状悬浮液相比,这种悬浮液(由颗粒或絮凝物的聚集体组成)在落下时表现出不同的行为。本文的主要目标和新颖性是提出(和测试)对常用的斯托克斯公式的修改,在计算自由落体粒子的终端速度时加入分形几何,以克服斯托克斯公式的局限性,从而获得沉降过程的效率。由于这种分形修改,可以使用简单而优雅的斯托克斯公式,以便更好地计算非粒状颗粒-聚集体或絮凝物的最终速度,从而获得整个悬浮液范围的沉降效率。非粒状和粒状。本文获得的结果表明,使用基于颗粒分形几何形态的修正公式(提出的分形方法)计算的沉降过程效率比仅基于颗粒尺寸计算的沉降效率(经典方法)更多地描述和符合实验数据。