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In this work, we generalize a previously developed multi-model, data-based modeling approach for batch processes to account for time-varying dynamics by incorporating online learning ability into the model. The application of the standard recursive least squares (RLS) algorithm with a forgetting factor for the model form leads to unnecessary updates for some of the models. We address this issue by...
This work addresses the problem of driving a batch process to a specified product quality using model predictive control (MPC) with data-driven models. To address the problem of unavailability of online quality measurements, an inferential quality model, which relates the process conditions over the entire batch duration to the final quality, is required for this problem. At a given sampling instant,...
This work presents a data-based multi-model approach for modeling batch systems in which multiple local linear models are identified using partial least squares (PLS) regression and then combined with an appropriate weighting function that arises from fuzzy c-means clustering. The resulting data-based model is used to generate estimates of empirical reverse-time reachability regions (RTRRs) (defined...
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