Comparison of Different Optimization and Control Schemes in an Industrial Scale Microalgae Fermentation

Optimization and control of an industrial microalgae fermentation has been studied in this work. A mathematical model has been developed based on the data collected from pilot plant test runs at different operating conditions at Coors BioTech Company. Several optimization techniques, such as, Pontryagin´s Maximum Principle, Feedback Sub-Optimal Policy and Direct Search Method, were used for maximizing the cellular productivity. Adaptive and Globally Linearizing Control techniques were applied for controlling the multivariable nonlinear continuous fermentation. An output sensitivity analysis was used to obtain information about the sensitivity of the closed-loop fermentation operation due to parameter changes. Comparison of the theoretical results of the different optimization schemes reveals, that the direct search algorithm is preferable because of its simplicity and better performance. The results of real time application of the feedback suboptimal optimization algorithm resembles the theoretical analysis. The linear state variable feedback control law showed unsatisfactory performance in closed-loop behavior. The multivariable adaptive controller was found to perform better than the Globally Linearizing Control algorithm.