Integrated sustainable valorization of seaweed (Ulva ohnoi) biomass into food-grade ingredients with year-round cultivated feedstock
摘要
Via efficient, scalable, and climate resilient processes, seaweed biorefineries can advance cleaner production, delivering nutrient-rich ingredients with relatively lower land and freshwater requirements. Here, a land-based Ulva ohnoi platform that integrates optimized outdoor tank cultivation, pH-shift protein extraction with dual-product valorization, scale-aware techno-economic analysis (TEA), and a climate-scenario emulator was developed and evaluated. Year-round cultivation yielded a pooled feedstock with carbohydrate and protein contents at 46.6% and 26.8%, respectively, and under optimal conditions, protein yield was 10.30% DW. Further, the protein content of cultivated Ulva protein (CUP) was 47.64%, with bound amino acids showing predominance (96%), implying food-grade utility. pH-shift extraction also improved in-vitro pepsin digestibility to 68.08%. The total dietary fiber content of CUP residue (CUPR) was 40.59%, with insoluble dietary fiber (31.59%) showing predominance, supporting the applicability of CUPR in improving gastrointestinal tract function. TEA revealed strong economies of scale for the platform. The 10,000-kg production model outperformed the 100- and 1,000-kg production models, with gross margin, Return On Investment (ROI), and Internal Rate of Return (IRR) at 78.56%, 110%, and 66.02%, respectively, and a payback time of only 0.91 years. Based on site-specific regression analysis (R2 = 0.884), protein yield increased with temperature but decreased with rainfall, and further warming increased mean protein yield, while higher-emission pathways introduced rainfall-driven volatility, necessitating strategies for sustaining performance under climate variability. Overall, the use of the Ulva platform showed a broad growth temperature window as well as rapid acclimation for U. ohnoi, implying resilience even under global warming conditions and increasing weather variability.
Graphical abstract