PAAD using aqueous alumina solutions is affected by a variety of trade-off relations in terms of the phase composition of the obtained product, overall product yield, or energy input. We demonstrated a workflow which enables prediction of the multivariate PAAD output from input processing data. For this, we initially provided a unique, highly consistent dataset of real-world PAAD processing campaigns with corresponding phase evaluation of the obtained hetero-aggregate powders. Feature analysis was conducted on this dataset, taking into account input-input, output-output as well as input-output