Adaptive temperature model for microalgae cultivation systems

Abstract

Microalgae cultivation for energy production is a promising avenue for converting solar light into sustainable biofuel. Solar processes are however subjected to the permanent fluctuations of light and medium temperature. Accurate temperature prediction of the culture medium turns out to be critical for optimising growth conditions. In this study, we introduce a reduced-model approach derived from existing models, turning the complex heat transfer modelling problem into an identification problem. The resulting generic model, called the Simplified Auto Tuning Heat Exchange (SATHE) model, has a clear and simple structure, offering a balance between accuracy and computational complexity. The SATHE model is versatile and contains the necessary terms to catch a large variety of heat transfer problems, while the parameters can be identified from experimental data. We first prove the parameter identifiability and then propose an identification strategy, based on the gradient computation, to identify the model’s underlying parameters. We further validate the SATHE model performance in two distinct reactors across various seasons. Finally, we discuss the potential of online applications with a continuous self-tuning strategy to keep optimal predictive performances. This work lays the foundation for enhanced control strategies in large-scale cultivation systems.