Unlocking Global Bioenergy Potential: Multi-Modal AI Framework for Microalgae Cultivation on Marginal Lands with Intelligent Data Mining

The sustainable development of microalgae bioenergy systems faces dual challenges: identifying suitable cultivation locations and optimizing production parameters across diverse environmental conditions. Building upon our previous research on global marginal land assessment and machine learning applications in microalgae cultivation, this study presents a novel multi-modal artificial intelligence framework that combines deep learning, machine learning, and large language models (LLMs) to address these challenges comprehensively. Our approach integrates three key components: (1) a hybrid deep learning network with attention mechanisms for biomass productivity prediction across different geographical and climatic conditions, (2) LLM-powered intelligent analysis of historical experimental data (1980-2024) for parameter optimization and pattern discovery, and (3) advanced machine learning algorithms for identifying and assessing marginal land suitability. Initial spatial analysis has identified approximately 7.37 million square kilometers of marginal lands suitable for microalgae cultivation, particularly in equatorial and low-latitude regions, with Australia, Kazakhstan, Sudan, Brazil, the United States, and China showing significant potential. Our previous machine learning models demonstrated that Photobioreactors (PBRs) achieved a global average daily biomass productivity of 142.81mgL−1d−1, while Open Ponds reached 122.57mgL−1d−1. Building on these findings, our new deep learning framework shows a 35% improvement in productivity prediction accuracy compared to traditional methods, achieving a test R² of 0.94. The LLM-based data mining approach reveals novel correlations between cultivation parameters and system performance across different geographical contexts, while accounting for various cultivation methods. The framework suggests that optimal cultivation strategies could potentially increase biomass yields by 40% while minimizing resource inputs, with projected annual production reaching 99.54 gigatons of microalgae biomass when utilizing suitable marginal lands. This biomass could be transformed into 64.70 gigatons of biodiesel, equivalent to 58.68 gigatons of traditional diesel, while sequestering 182.16 gigatons of CO₂. The integration of LLMs for experimental data analysis represents a significant advancement in understanding complex parameter interactions and optimization opportunities. This integrated approach not only advances our understanding of microalgae cultivation optimization but also provides practical insights for sustainable land management and renewable energy development, while addressing critical challenges in climate change mitigation through bioenergy production and carbon sequestration.