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: 7.43 s to solve 10800 equations with 900 degrees of freedom in Chlorella vulgaris and 11.45 s 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 I_in 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.

微藻生物质是未来几年提供清洁可靠的生物产品和能源的新兴选择。然而，由于其经济上的不可行性，其工业化生产尚未完成。迄今为止，已使用多种策略来提高微藻生产力。尽管如此，在大多数这些策略中，目标函数通过嵌套程序最小化，这些程序显示出处理不连续性的局限性，这在微藻模型中非常常见。本文描述了同时优化程序在 GEKKO Python 包中的应用，通过探索在连续运行的菌株Dunaliella tertiolecta和Chlorella vulgaris下的最佳生物质生产力. 结果表明，快速收敛时间的成功实现：7.43  s 求解 10800 个普通小球藻的900 自由度方程，11.45  s 求解 600 自由度杜氏盐藻6600 个方程。此外，在生物学方面，模拟表明，一旦在小球藻模型中达到最佳pH  >  8 水平，其他变量（如I _in）的灵敏度就会急剧下降。因此，即使不增加所需的光强度，也可以实现高生产率。此外，在杜氏藻 在这种情况下，结果还推断，更大的生物质生产力需要更大的输入底物浓度。