Synthesis, characterization and optimization of gasification products from Aegle Marmelos Correa shell in a downdraft gasifier using ANN and RSM

Abstract

Biomass has demonstrated how it is likely to be an excellent energy source equipped to meet the rising global need for clean and unceasing power sources to better our society. The gasification process is among the most viable thermochemical routes of bioenergy production. Gasification of Aegle Marmelos Correa shell was conducted in a portable downdraft fixed-bed reactor to assess the impact of moisture content and particle size on syngas composition. The artificial neural network (ANN) technique predicted gasification performance and compared it with response surface methodology (RSM) for forecasting their abilities. Syngas composition at optimum conditions of 10% (wt.) moisture and 2.6 mm particle size was 19.2 vol.% H2, 15.3 vol.% CO, 18 vol.% CO2, and 6.4 vol.% CH4. The results reveal that moisture content plays a more significant role than particle size in gasification reaction rate. It can be concluded that ANN has been more accurate and superior to the RSM model for forecasting gasification performance.