Effect of equivalence ratio on gas distribution and performance parameters in air-gasification of asphaltene: A model based on Artificial Neural Network (ANN)

Abstract

Air-gasification of asphaltene was carried out in a bench scale fluidized bed. A neural network model was also developed to study the influence of equivalence ratio (ER) on gas distribution, lower caloric value (LHV) of producer gas, and performance indicators (char conversion and cold gas efficiencies). The contents of CO and H2 were initially increased from 53.2 to 55.4 vol% and 35.4 to 37.6 vol% as ER increased to 0.35, and then decreased to 43.3 vol% and 27.7 vol% at ER of 0.5, respectively. The results also indicated that the LHV of the produced syngas was significantly decreased with ER increasing because of more oxidation reactions at higher ERs.