4.7 Article

Mechanisms behind overshoots in mean cluster size profiles in aggregation-breakup processes

期刊

JOURNAL OF COLLOID AND INTERFACE SCIENCE
卷 528, 期 -, 页码 336-348

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jcis.2018.05.064

关键词

Population balance modeling; Colloidal aggregation; Shear aggregation; Aggregate breakup; Restructuring; Flocculation; Fractal aggregates; Turbulence

资金

  1. Swedish Research Council VR [2012-6216]
  2. Swedish Gasification Centre consortium
  3. Center for Dynamic Systems (CDS) - Federal State Saxony-Anhalt, Germany

向作者/读者索取更多资源

Aggregation and breakup of small particles in stirred suspensions often shows an overshoot in the time evolution of the mean cluster size: Starting from a suspension of primary particles the mean cluster size first increases before going through a maximum beyond which a slow relaxation sets in. Such behavior was observed in various systems, including polymeric latices, inorganic colloids, asphaltenes, proteins, and, as shown by independent experiments in this work, in the flocculation of microalgae. This work aims at investigating possible mechanism to explain this phenomenon using detailed population balance modeling that incorporates refined rate models for aggregation and breakup of small particles in turbulence. Four mechanisms are considered: (1) restructuring, (2) decay of aggregate strength, (3) deposition of large clusters, and (4) primary particle aggregation where only aggregation events between clusters and primary particles are permitted. We show that all four mechanisms can lead to an overshoot in the mean size profile, while in contrast, aggregation and breakup alone lead to a monotonic, S shaped size evolution profile. In order to distinguish between the different mechanisms simple protocols based on variations of the shear rate during the aggregation-breakup process are proposed. (C) 2018 Elsevier Inc. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据