Summary

The proposal focusses on the process chain of spray drying. A spray drying process atomizes a carrier liquid, which contains solids in the form of dissolved or dispersed particles. The liquid evaporates during the residence time in the dryer and solidification occurs, either by crystal growth (solutions) or by concentration increase (suspensions). The property function of the so-generated granules determines the application properties. Here, porosity and pore structure play a central role, e.g. for adsorptive or catalytic behavior and handling. Therefore, product design works on tailoring the inner morphology of granules. The latter depends on both, the properties of the feed, e.g. the particles, their interactions, the solid concentration, and the process conditions, e.g. temperature field, drop size, drying conditions. To control the entire process all these parameters have to be considered leading to a multiparametric problem. The process data available from in-line measurements is limited concerning the desired final product specification. It is necessary to employ off-line high-resolution methods like computer tomography (CT) to acquire the full information on the pore structure of the granules. This is time consuming and undergoes several steps from sampling, sample preparation to image segmentation and data analysis. To be able to use CT-data in the control loop it has to be connected to in-line data, which is done using proxies. A proxy is a single or combined parameter deduced from image data. Two strategies for proxies will be used: interpretable proxies (e.g. pore property distributions) and data-driven proxies (e.g. compressed image data of particle systems obtained by automated encoding). The forward and backward connection of process parameters and feed properties with interpretable and data-driven proxies is achieved by methods of machine learning, e.g., feed-forward networks. In the 1st funding period (FP) the connected steps of spray generation and particle structuring during droplet drying are addressed. This will be extended in the 2nd FP by defined preconditioning steps, e.g., tailoring of particle-particle-interactions and de-agglomeration (mechanical shear / ultrasound) within the feed stream. Then the control concept obtains more degrees of freedom, since a pre-structuring of the feed becomes possible. The model-based control scheme developed in the 1st FP uses transfer functions by performing statistical learning between proxies derived either from distributed properties of particle systems or abstractly integrated image data (in-line dynamic imaging / off-line CT-images) applying the concept of automated encoding. It has to be extended in the 2nd FP considering similar data from additional process steps. In this way, the project generates for the example spray drying scientific insights on the performance and applicability of a data-driven black-box control scheme, using both in-line and off-line information.