Parameter Identification For Continuous Fluidized Bed Spray Agglomeration

Abstract

Agglomeration represents an important particle formation process used in many industries. One particularly attractive process setup is continuous fluidized bed spray agglomeration, which features good mixing as well as high heat and mass transfer on the one hand and constant product throughput with constant quality as well as high flow rates compared to batch mode on the other hand. Particle properties such as agglomerate size or porosity significantly affect overall product properties such as re-hydration behavior and dissolubility. These can be influenced by different operating parameters. In this manuscript, a population balance model for a continuous fluidized bed spray agglomeration is presented and adapted to experimental data. Focus is on the description of the dynamic behavior in continuous operation mode in a certain neighborhood around steady-state. Different kernel candidates are evaluated and it is shown that none of the kernels are able to match the first six minutes with time independent parameters. Afterwards, a good fit can be obtained, where the Brownian and the volume independent kernel models match best with the experimental data. Model fit is improved for identification on a shifted time domain neglecting the initial start-up phase. Here, model identifiability is shown and parameter confidence intervals are computed via parametric bootstrap.

Special Issue on “Recent Advances in Population Balance Modeling”

“…They demonstrated that the combination of DYS and shear-cell simulations is beneficial, using data from their experimental system for validation. Golovin et al [7] proposed a population balance model for a continuous fluidized bed spray agglomeration and validated it using experimental data. The main focus was on the description of the dynamic behavior in continuous operation mode in a certain neighborhood around steady-state.…”

Approximate Moment Methods for Population Balance Equations in Particulate and Bioengineering Processes

“…(I) In the top-down-modeling approach, only a few particle properties are taken into account (often only one). The unknown fluxes and source/sink mechanics of the corresponding PBEs are either described by semi-empirical approaches or adapted to experimental data with suitable methods (see, e.g., [61][62][63][64][65][66]). (II) In contrast, the bottom-up approach is based on detailed first-principle modeling of the individual particle behavior, e.g., ordinary or stochastic differential equations, which is adapted to single-particle experimental data.…”

Effect of Adding Surfactants to a Solution of Fertilizer on the Granulation Process

“…The most widely used and successful method for the analysis and quantitative prediction of the granulation processes is the approach based on population balance modeling [19][20][21][22][23]. The population balance equation (PBE), which is based on the law of conservation of mass, is a mathematical description of how the particle size distribution evolves [24].…”