LAUSR

In the industrial sector, foaming remains an inevitable side effect of mixing, shearing, powder incorporation, and the metabolic activities of microorganisms in a bioprocess. Excessive foaming can interfere with the mixing of reactants and lead to problems such as decreased effective reactor volume, microbial contamination, product loss, and increased reaction time. Physical modeling of foaming is an arduous process as it requires estimation of foam height, which is dynamic in nature and varies for different processes. This work demonstrates a novel application of ensemble-based machine learning methods for prediction of different fermenter types in a fermentation process (to allow for successful data integration) and of the onset of foaming. Ensemble-based methods are robust nonlinear modeling techniques that aggregate a set of learners to obtain better predictive performance than a single learner. We apply two ensemble frameworks, extreme gradient boosting (XGBoost) and random forest (RF), to build clas...