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Prediction of gas composition obtained from steam-gasification of residual oil using an Artificial Neural Network (ANN) model

Abstract Steam-gasification is a promising technology for hydrogen-rich syngas production with low tar content. In this work, an Artificial Neural Network (ANN) modeling of steam-gasification of residual oil was developed. The aim of this paper was to study the effect of steam flow rate (SFR) and reaction temperature on gas yield (GSY) and hydrogen yield (HDY). Results showed that GSY and HDY increased from 27.3 Nm3/kg to 37.1 Nm3/kg and 0.27 mol/kg to 0.39 mol/kg as the reaction temperature increased from 850 °C to 1000 °C. It was also found that with increase of SFR from 0.08 to 0.32, HDY increased from 7.5 mol/kg to 27.3 mol/kg.