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Model improvement for estimating of water turbidity removal efficiency in helical tubular flocculators

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ABSTRACT Scientific studies have been demonstrating that helical tubular flocculators (HTFs) have high efficiency in floc formation and low hydraulic retention time when compared to flocculators commonly used in water and wastewater treatment. However, its practical application is still limited because there is still a significant demand for advances in the understanding of the relationship between the hydrodynamics of the unit and the flocculation process, as well as for criteria and methodologies in support to the rational design of HTF. In this context, the objective of this study was to propose an improvement in the model of turbidity removal efficiency developed by Oliveira (2014), which takes into account a set of geometric, hydraulic and hydrodynamic parameters relevant to the flocculation process in this type of reactor, by incorporating one of the most representative parameters of flocculation processes, the normal pressure gradient, as one of its independent variables. The development of the work employs computational fluid dynamics (CFD) in the study of 84 HTFs configurations, considering laminar and turbulent flow regimes. As a result, a new model version for estimating water turbidity removal’s efficiency in helical tubular flocculators was obtained, which, in relation to the original version, has a smaller number of independent variables, presents better fit to the experimental data and is simpler from the operational point of view.

摘要 已有科学研究表明,相较于当前给水与污水处理中常用的絮凝装置,螺旋管式絮凝器(helical tubular flocculators,HTFs)在絮凝体形成效率上更高,且水力停留时间更短。但该装置的实际应用仍受限于两点:一是对该单元流体动力学与絮凝过程间关联机制的认知仍需大幅深化,二是缺乏支撑螺旋管式絮凝器合理设计的相关标准与方法。本研究旨在对奥利维拉(2014)提出的浊度去除效率模型进行改进:该原始模型已纳入了与该类反应器絮凝过程相关的一系列几何、水力及流体动力学参数,本次改进则将絮凝过程中最具代表性的参数之一——法向压力梯度,作为新增自变量纳入其中。本研究采用计算流体动力学(computational fluid dynamics,CFD)方法,针对84种螺旋管式絮凝器构型开展研究,涵盖层流与湍流两种流态。最终得到了用于估算螺旋管式絮凝器浊度去除效率的新版模型;相较于原始模型,该新版模型的自变量数量更少,对实验数据的拟合效果更佳,且从操作层面而言更为简便。
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SciELO journals
创建时间:
2019-10-09
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