(428f) A Surrogate-Based Topological Compartment Model for Counter-Current Spray Dryers.
AIChE Annual Meeting
Wednesday, November 10, 2021 - 9:03am to 9:24am
The surrogate-based compartment model has been generated following a step-by-step validation procedure with a decoupling of the momentum, heat, and mass transfer mechanisms as presented in . In the first stage, the distribution and size of each of the zones have been identified based on the volume fraction of the particles. These zones presented in Figure 1 have been made flexible using surrogate models. In the second stage, the fluxes of particles and air between the compartments and the residence time of the particles have been modeled. Unlike previous works, a new numerical procedure has been developed for surrogates based on dimensionless groups. The methodology is based on a three-step algorithm that first selects the necessary dimensionless groups based on minimizing the Bayesian Information Criteria. The second step models the mass fluxes based on these dimensionless groups in the form of Eq. (1) and evaluates the quality of the model; an alternative statistical model is proposed if the model is not good enough. In the same way, the third step of the process generates the surrogate models for the energy fluxes, which have been evaluated under different operating conditions including the heat and mass transfer model defined as in . These surrogate models have been implemented into the final model where the discrete phase is defined by means of a multi-dimensional population balance. The single droplet drying model for the particles includes diffusion and boiling mechanisms as in  and it is implemented employing finite differences. The model is finally solved by defining a two-way iterative scheme in Python.
The model has been validated by decoupling the physics and evaluating it step-by-step. The validation against the momentum of the particles is carried out with the mean residence time of monodispersed injections. The prediction of the mean residence time shows a relative root mean square error of 7%. The validation including the heat and mass transfer is carried out with the temperature at the entrance and exit of every compartment showing an average error of 5 K and a maximum of 11 K in all the compartments evaluated. Even though the differences in predicting the mean residence time and the temperature are low, the errors are still too high to implement the agglomeration and breakage kernels as characterized in the CFD-DPM model. The relative time at which the discrete phase has a wet surface only represents around 1.6% of the total mean residence time. This high level of accuracy required results in too small feasible region for maintaining the surrogate models of the kernels. Thus, the implementation of these kernels into process or compartment models requires at least the use of a data-driven model as an intermediate step and may not yield a significant improvement in the reduction of computational cost.
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