The development of renewable energy systems requires integrated processes that enable efficient conversion of biological resources into multiple energy carriers. Microalgae are promising bioenergy feedstocks due to their rapid growth, biochemical versatility, and potential for sequential conversion into different renewable fuels. However, most studies have focused on individual biofuel pathways rather than on maximizing energy recovery from the same biomass through integrated processing. This study evaluated alternative technological routes for converting Tetraselmis subcordiformis biomass into hydrogen, methane, and bio-oil, with emphasis on biomass utilization and gross energy recovery. Microalgae were cultivated in a photobioreactor, subjected to hydrogen biosynthesis, bio-oil extraction, and/or anaerobic digestion in different process sequences, and the energy outputs of the resulting carriers were compared. The final biomass concentration reached 2610 ± 170 mgVS/L. Hydrogen production was 77.2 ± 11.7 mL H2/gTS. Direct anaerobic digestion yielded 193 ± 23.4 mL CH₄/gTS, while biomass remaining after hydrogen production yielded 181 ± 21.4 mL CH4/gTS, retaining 93.8% of the methane-production potential of untreated biomass. After bio-oil extraction, methane yield decreased to 129 ± 19.5 mL CH₄/gTS, corresponding to 66.8% of the direct-digestion value. Bio-oil content was 8.41 ± 1.1%TS, with a calorific value of 40.2 ± 4.2 MJ/kg, but contributed only 1.3 kWh/MgTS to the gross energy output. The highest gross energy recovery was obtained for biomass production followed by hydrogen biosynthesis and anaerobic digestion, reaching 1891.4 ± 199.4 kWh/MgTS, compared with 1769.8 ± 214.6 kWh/MgTS for direct methane fermentation and 1415.9 kWh/MgTS for the hydrogen–bio-oil–methane sequence. The results show that process optimization should focus primarily on the selection and order of unit operations. Omitting the low-yield bio-oil extraction step and directing hydrogen-depleted biomass to anaerobic digestion maximized gross energy recovery while limiting losses in methanogenic potential.
Physics-Informed AI Framework for Waste Heat Recovery and Thermal Energy Optimization in Sustainable Laser Additive Manufacturing of Aerospace Components