A novel data-driven model for prediction and adaptive control of pH in raceway reactor for microalgae cultivation.

This work proposes a new data-driven model to estimate and predict pH dynamics in freshwater raceway photobioreactors. The resulting model is based purely on data measured from the reactor and divides the pH dynamics into two different behaviors. One behavior is described by the variation of pH due to the photosynthesis phenomena made by microalgae; and the other comes from the effect of CO 2 injections into the medium for control purposes. Moreover, it was observed that the model parameters vary throughout the day depending on the weather conditions and reactor status. Thus, a decision tree algorithm is also developed to capture the parameter variation based on measured variables of the system, such as solar radiation, medium temperature, and medium level. The proposed model has been validated for a data set of more than 100 days during 10 months in a semi-industrial raceway reactor, covering a wide range of weather and system scenarios. Additionally, the proposed model was used to design an adaptive control algorithm which was also experimentally tested and compared with a classical fixed parameter control approach. • Simple regression tree model able to adapt to a wide variety of conditions. • The model is capable of successfully predicting pH under a different system conditions. • The simple structure of the model facilitates the adaptive tuning of control algorithms. • More than 100 days for 10 months for calibration and validation purposes. [ABSTRACT FROM AUTHOR]