In recent years, our world has been grappling with the challenges of global warming, prompting a search for alternative energy sources that can provide clean and sustainable power without adversely impacting the environment. Biomass has emerged as a viable option due to its potential as a renewable and environmentally friendly energy source. Among various processes available, hydrothermal liquefaction (HTL) has gained significant attention for its capability of converting wet biomass into biocrude oil in a relatively short timeframe. Previous studies have explored integration of machine learning with HTL to forecast the yields of biocrude oil. This work aims to develop a machine learning model specifically tailored to predicting the biocrude oil yields from the HTL process. Furthermore, experiments were done using Napier 4190 (Giant Juncao Grass) as a biomass source to verify the accuracy of this machine learning model. The findings revealed that the machine learning model achieved an R2 value of approximately 0.8. Model predictions and experimental results show a discrepancy of approximately 6-14%.

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