Learning, fast and slow: a two-fold data-based model adaptation algorithm for processes under varying operating conditions

This article proposes a novel two-fold learning framework to maintain the accuracy of data-based process models, typically affected by two types of uncertainty. Out-of-domain uncertainty arises when the modelled process operates under conditions not represented in the training dataset, in-domain unc