Hydrothermal liquefaction of woody biomass with catalysts was commonly applied in bio-energy research, but the effects of catalyst and solvent on yield and properties of bio-energy are not clear. In this work, the influences of catalyst and solvent on bio-energy production were studied, during which four solvents and three catalysts were used, and the liquefaction parameters were optimized by experimental and Machine learning (ML) method. Results show that the maximum yields of bio-oil and biochar are 65.0% and 32.0%, respectively, and the caloric values of bio-oil and biochar are 31.2 MJ/kg and 26.5 MJ/kg, respectively. Alkaline catalysts and 1,4-butanediol-triethanolamine mix solvent can benefit the bio-energy generation. In addition, a Random Forest (RF) was developed to forecast the yields, and the method performed well with experimental results.
Scopus Subject Areas
- Environmental Engineering
- Renewable Energy, Sustainability and the Environment
- Waste Management and Disposal
- Hydrothermal liquefaction mechanism
- Machine learning
- Woody biomass