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

Abstract Stickiness is a key factor in characterizing coagulation dynamics (aggregation or 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 observed in a simple stirred tank. However, their applicability is limited to dense particles with monodisperse (or sufficiently narrow) particle size distributions (PSDs). In this study, we developed a novel estimation method that poses fewer assumptions on fractal structures and PSD shapes through theoretical analysis of the coagulation equation and numerical experiments that imitated tank experiments. First, we demonstrated that the reduction rate depends on the fractal dimension and the polydispersity of suspended particles, and both effects can be quantitatively integrated into a single correction factor. Second, we developed a novel estimation method for the stickiness parameter by incorporating the correction factor. This novel method is applicable to polydisperse particles with a single fractal dimension. We tested the consistency and the performance of our method using two polydisperse PSDs. Coagulation experiments were numerically conducted here to evaluate the performance of the proposed estimation method using a validated particle model. Our results show that the correction factor reduces the estimation error of the stickiness parameter from hundreds of percent (obtained by previous methods) to a few percent. Using the averaged correction factor evaluated from multiple PSD measurements during tank experiments, the estimation accuracy improved further to less than 1%.