Model predictive control coupled with economic and environmental constraints for optimum algal production

Algae production process is a key cost center in production of biofuels/bioproducts from microalgae. Decline in the growth of algae in outdoor ponds during non-optimal conditions is one of the hurdles for achieving consistently high algal production rates. An optimal controller can be used to overcome this limitation and provide reliable growth in outdoor conditions. A model predictive controller (MPC) was developed to optimize the algal growth, predicted by flux balance analysis, under natural disturbances, embedding within the cost function, the economic and environmental constraints associated with the process. The model, developed in MATLAB, was validated on a 30-L continuous algal culture under light, temperature and a combination of light and temperature disturbances. The MPC proved effective in minimization of a decrease in growth under these natural disturbances. The growth rates with MPC were observed to be 79-116% higher as compared to the non-MPC growth.