Modeling and adaptive control of pH in raceway reactors for microalgae production

This work proposes the obtaining and use of regression tree models for the prediction of pH and its control in clean water raceway photobioreactors, taking into account the different operating conditions to which the culture is subjected. These operating conditions are dictated, among other measurable variables, by solar radiation, the temperature and level of the medium in which the culture grows. The aim is to evaluate the validity of this approach, which produces simple and fast-running models, in the modelling of complex biological processes such as the one discussed in this paper. Its performance has been validated in simulation, using a non-linear model based on artificial neural networks as a virtual plant and a pH PI control algorithm whose parameters are adapted according to the model chosen in the regression tree.