<![CDATA[The production of biodiesel from microalgae presents a sustainable solution to global energy challenges, particularly through the utilization of Botryococcus braunii, known for its high lipid yield. However, conventional cultivation methods remain constrained by manual monitoring and limited process optimization, resulting in suboptimal lipid productivity. This study introduces Algaboost, an intelligent photobioreactor that integrates UV-B induced stress with Artificial Neural Network (ANN)-based environmental control to enhance lipid accumulation in B. braunii. The system was designed with real-time sensor feedback, automated fluid control, and a graphical user interface (GUI) to facilitate dynamic cultivation management. The ANN model, trained on a dataset of 119 entries, successfully predicted optimal cultivation set points (pH 6.0; salinity 30.1 ppt) and demonstrated reliable performance as a software sensor. Under these conditions, a lipid yield of 41.49% was achieved, with 20.83% TAG content, suitable for biodiesel synthesis. The findings validate the feasibility of combining machine learning and photobiological stress in a semi-autonomous platform, offering a scalable approach to renewable fuel production. Algaboost not only improves operational efficiency but also marks a step toward adaptive, data-driven bioprocessing for sustainable energy systems. ]]>
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