The aim would be to discover the impact of conflicting factors in the application of algae in CO2 sequestration for the benefit of sustainable biofuels. Based on the LCA approach, the model and quantitative AI assessment approach were established by coupling the upstream CO2 source and the downstream algal product at the uniform level of Nannochloropsis oceanica algae, benefiting the selection of algal biofuel suppliers would come. The AI model examined the impact of interacting factors on energy consumption, including transport distances coupled with cleaning modes coupled with flue gas CO2 concentration, lipid content coupled with specific productivity with the supply of nutrients, and the refining process with the end products. The computational framework of the AI model is divided into three sub-models, including the CO2 capture and purification model, the algae cultivation and harvesting model, the refining process, and the biofuel product model. Consistent with the uncertainty analysis of the KI model, positive energy gains were realized over a wide range of lipid levels despite the use of biofuel or biodiesel that couples solar energy and nutrient byproduct effects. Wet biodiesel and HTLHRJ jet biofuel had the energy consumption priorities in three jet biofuel tracks and three biodiesel tracks. The allocation analysis confirmed that algal biofuel has promise in the direction of cultivating algae suitable for the target requirements of biofuel products and improving by-product recovery. The results would increase interest in both LCA and CO1 sequestration for the benefit of sustainable biofuels.
