Development of real-time state estimators for reaction–separation processes: A continuous flash fermentation as a study case

The development of reliable on-line state estimators applicable to reaction–separation processes is addressed in this work. Artificial Neural Network-based software sensors (ANN-SS) are proposed to allow on-line measurement of key variables, with an estimation algorithm that uses secondary variables as inputs. A continuous laboratory-scale flash fermentation for bioethanol production is considered as a case study. The process consists of three interconnected units: fermentor, filter (tangential microfiltration for cell recycling) and vacuum flash vessel (for the continuous separation of ethanol from the broth). The concentrations of ethanol in the fermentor and of ethanol condensed from the flash are successfully monitored on-line using ANN-SS. The proposed model contributes to improve the understanding of the complex relationships between process variables in the reaction and separation units, which is of major importance to allow the operation of the ethanol production process near its optimum performance.