Biomass Gasification Product Estimation Tool
This web application uses the power of Machine Learning (ML) to predict the concentrations of hydrogen (Hâ‚‚) and carbon dioxide (COâ‚‚) produced during the process of biomass gasification.
​
Our goal is to empower researchers, engineers, and anyone interested in biomass conversion with a user-friendly tool to estimate syngas composition. By providing key input parameters about the biomass feedstock and gasification process, you can gain valuable insights into the expected Hâ‚‚ and COâ‚‚ yields.
​
The predictions generated by this application are based on a robust ML model trained on a comprehensive dataset of real-world gasification experiments. This model allows us to identify complex relationships between various factors influencing gas composition and translate them into actionable predictions.
​
It's important to note that the models were trained on a specific range of biomass properties, including:
-
particle size: 0.25–70.00 mm
-
carbon content: 40.38–69.35 %daf
-
hydrogen content: 3.79–10.13 %daf
-
ash content: 0.27–44.00 %db
-
moisture content: 0.00–29.13 %wb
-
temperature: 553.00–1,050.00 °C
-
steam/biomass ratio: 0.00–4.04 wt/wt
-
​equivalence ratio (non-steam): 0.00–0.87
To ensure the most accurate predictions, please input values within these ranges.
​
Read more →​
* db: dry basis, wb: wet basis, daf: dry ash-free basis