Abstract
Microalgae biomass is an up-and-coming option for supplying clean and reliable bio-products and energy for future years. However, its industrial production is not yet accomplished due to its economic unfeasibility. To date, several strategies have been used to improve microalgae productivity. Nonetheless, in most of these strategies, the objective function is minimized by nested procedures that have shown limitations dealing with discontinuities, which is very common in microalgae models. This paper describes the application of simultaneous optimization procedures in GEKKO Python package through the exploration of optimal biomass productivity under continuous operation of the strains Dunaliella tertiolecta and Chlorella vulgaris. The results show the successful implementation with fast convergence times: 7.43s to solve 10800 equations with 900 degrees of freedom in Chlorella vulgaris and 11.45s to solve 6600 equations with 600 degrees of freedom in Dunaliella tertiolecta. Furthermore, in biological terms, simulations show that once an optimal pH>8 level is reached in the Chlorella model, the sensitivity of other variables such as Iin decreases dramatically. Therefore, it is possible to achieve high productivity even without increasing the required light intensity. In addition, in Dunaliella case, the results also infer that larger biomass productivity requires larger input substrate concentration.
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