Microalgae, such as Chlorella vulgaris and Scenedesmus obliquus, are highly efficient at capturing carbon dioxide through photosynthesis, converting it into valuable biomass. This biomass can be further processed into carbon materials with applications in various fields, including water treatment. The reinforcement learning (RL) method was used to dynamically optimize environmental conditions for microalgae growth, improving the efficiency of biodiesel production. The contributions of this study include demonstrating the effectiveness of RL in optimizing biological systems, highlighting the potential of microalgae-derived materials in various industrial applications, and showcasing the integration of renewable energy technologies to enhance sustainability. The study demonstrated that Chlorella vulgaris and Scenedesmus obliquus, cultivated under controlled conditions, significantly improved absorption rates by 50% and 80%, respectively, showcasing their potential in residential heating systems. Post-cultivation, the extracted lipids were effectively utilized for biodiesel production. The RL models achieved high predictive accuracy, with R2 values of 0.98 for temperature and 0.95 for oxygen levels, confirming their effectiveness in system regulation. The development of activated carbon from microalgae biomass also highlighted its utility in removing heavy metals and dyes from water, proving its efficacy and stability, thus enhancing the sustainability of environmental management. This study underscores the successful integration of advanced machine learning with biological processes to optimize microalgae cultivation and develop practical byproducts for ecological applications.

Carbon aerogels including graphite and graphene have unique properties such as lightweight, strong, and insulative to roofing applications. Carbon aerogels offer innovative solutions in building management by enhancing thermal and acoustic insulation while reducing structural weight, aligning with the focus on economic and business analysis driven by machine learning. Traditional building materials often fail to meet contemporary energy efficiency and sustainability demands, underscoring the necessity for more advanced solutions. This project is dedicated to integrating carbon aerogels into roofing systems and employs Deep Neural Networks (DNNs) to optimize their performance and integration. The novelty of this study lies in its application of carbon aerogel technology—a cutting-edge, lightweight, and highly insulative material—specifically within roofing to analyze the practical evaluation of carbon aerogels’ thermal properties and economic viability in the construction industry. This study aims to rigorously assess carbon aerogels’ performance and financial impact on roofing applications. By conducting the thermal guard test and economic lifecycle evaluation, the study seeks to validate carbon aerogels’ enhanced energy efficiency and cost-effectiveness compared to traditional roofing materials. The study demonstrates that carbon aerogels offer superior thermal insulation in roofing applications, with a thermal conductivity of 0.02 W/m·K, significantly outperforming traditional materials. Economically, the high initial cost of carbon aerogels is effectively offset by substantial energy savings, estimated at $300 annually per square meter, resulting in a payback period of approximately 1.05 years. These findings are supported by rigorous testing and optimization through DNN, highlighting the material’s potential to enhance energy efficiency and sustainability in building practices.