On A Novel Estimation Method of Stickiness Parameter of Suspended Particles from Tank Experiments

Stickiness is a key factor for characterizing the dynamics of coagulation (aggregation and flocculation). Previous studies have developed methods for estimating constant stickiness parameters. These methods estimate the stickiness parameter based on the reduction rate of the number of particles typically observed in a simple stirred tank. However, their applicability is limited because they rely on monodisperse (or sufficiently narrow) particle size distributions (PSDs). In this study, we developed a novel estimation method that poses no assumption on PSD shapes through theoretical analysis of the coagulation equation and numerical experiments that imitated tank experiments. First, we demonstrated that the reduction rate decreases under polydisperse PSDs and that this effect can be quantitatively represented by a correction factor explicitly calculated from the PSD shape. Second, we developed a novel estimation method for the stickiness parameter by incorporating correction factor, and tested our method on two polydisperse PSDs. In this study, coagulation experiments were numerically conducted to evaluate the performance of our estimation method using a sufficiently validated particle model. Our results show that the correction factor evaluated from a single PSD measurement can improve the stickiness estimation compared with that of previous methods. Upon using a time-averaged correction factor evaluated from multiple PSD measurements during tank experiments, the estimation accuracy improved further.