Fermentation monitoring by Bayesian states estimators. Application to red wines elaboration

State Estimation in Alcoholic Fermentation Models: A Case-Study in Wine-Making Conditions

“…However, since the approach relies on a linearization of the system, the estimator can diverge if the initial estimate of the state is too far from the real state. Other algorithms that do not rely on mechanistic models of the fermentation process, such as non-parametric Gaussian trackers [8], have proved their efficacy, but can fail to capture the constraints and nonlinearities of the real system.…”

Nonlinear Control for Bioprocesses with Model Uncertainties and External Disturbances

“…To improve the results of nominal optimization, a methodology called "run to run" optimization appears, which uses previous runs information to optimize the operation of subsequent ones [31][32][33][34][35][36][37]. Another strategy is the online optimization of the model parameters [38][39][40][41][42][43][44][45]. is kind of optimization is difficult to perform since the available models might only be locally valid and thus inappropriate for predicting final concentrations [46].…”