ScholarWorks@동국대학교

초록

Microalgae are recognized for their rapid growth rate and serve as an efficient biofuel production feedstock to address critical sustainability and energy security challenges. However, the primary challenge for stable biorefinery operations lies in the cultivation phase, as microalgae growth is highly dependent on time-varying climatic conditions. This work presents an optimal biorefinery process pathway to address this issue by integrating economic and environmental considerations with harvest scheduling under fluctuating climate conditions. In the first stage, a kinetic growth model was developed for eight microalgae species, incorporating temperature and solar radiation data. Harvest scheduling was then optimized using a genetic algorithm to find the optimal harvest interval for each microalgae species, maximizing the biomass yield under time-varying climate conditions. Subsequently, a multi-objective superstructure optimization approach was employed to select the optimal biorefinery process pathway, balancing the net operational margin and carbon dioxide emissions. The performance of the selected biorefinery process was evaluated through techno-economic analysis and life cycle assessment across four regions in South Korea: Jeju, Yeosu, Ulsan, and Daesan. Among the eight microalgae species studied, Asterionella formosa achieved the highest biomass yield, producing 2,300 tons annually with an optimal harvest interval of 17.5 days under average temperature conditions. The optimized process improved the net operational margin by 2.49 M$ per year and reduced carbon dioxide emissions by 41.4 % compared to the base case. The proposed method provides an integrated framework for large-scale microalgae-based biorefinery processes from microalgae species selection to optimal harvest strategy and process pathway under time-varying climatic conditions, providing a levelized cost of biofuels and global warming protention to achieve sustainable development goal. © 2025 Elsevier Ltd

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