Due to the complex dynamics of the growth of microalgae a sophisticated control method is needed, to achieve the maximum yield out of a microalgae plant.
Model-predictive control (MPC) is a powerful tool gaining a lot of attention in the field of modern control systems.
With the help of an underlying model predicting the system behavior, an optimal working mode is determined for maximizing a productivty criterion.
Temperature and light are two variables having a significant influence on the growth of microalgae. In various systems sunlight is the only light source. Temperature however, has mostly to be controlled, since microalgae optimally grow in a specific range of temperature and the yield varies along this range. Above a specific temperature, they die.
A temperature model evolution is crucial to predict the temperature in a plant. Data on the air temperature, light inflow from weather forecasts, can be used to predict the temperature of the microalgae medium. In existing literature heat transfer models achieve high accuracy. However, these models are complex, have many parameters and accurate knowledge about the plant. Due to their highly computational effort, they are also not usable for MPC.
The initial step is to create an adaptive temperature model, that has less parameters and has higher computational efficiency than the existing heat transfer models in literature, so it can be used for MPC. Afterwards this model has to be validated with data from different systems, to ensure an accurate temperature (prediction) submodel as part of the microalgae growth model, that will be used to achieve the optimal yield.
Under the supervision of Prof. Olivier Bernard and Dr. Walid Djema, I am developing a model-predictive control with auto-adaptation, which takes benefit of future meteorology to track both the culture density and the temperature along the day.
Afterwards the work of the project provides the implementation and testing of the control strategy on a pilot plant as well as the benefits assessments in terms of productivity, water use, energy use and manpower requirement.
by Ali Gharib