Targeting δ-A2O3, we constructed a multi-response machine-learning model for predicting the phase output of PAAD, and trained this model on a set of consistent experimental campaigns. Feature analysis (FA) was employed on input as well as on output parameters in order to select the most predictive parameters from a range of initially seven input and five output features. The model itself uses Gaussian process regression with multivariate CoKriging (mGPR, ). It enables training on correlated output observations by expanding the covariance functions of a singleresponse Gaussian processing model (sGPR, ) to a matrix that includes the output covariance (Figure ). In this way, PAAD optimization and reliable predictions of the phase composition of alumina heteroaggregates are achieved. Furthermore, the model enables process resilience predictions in terms product stability under fluctuating experimental conditions.