Air gasification of high-ash sewage sludge for hydrogen production: Experimental, sensitivity and predictive analysis

Abstract

In this work, air gasification of sewage sludge was conducted in a lab-scale bubbling fluidized bed gasifier. Further, the gasification process was modeled using artificial neural networks for the product gas composition with varying temperatures and equivalence ratios. Neural network-based prediction will help to predict the hydrogen production from product gas composition at various temperatures and equivalence ratios. The gasification efficiency and lower heating values were also established as a function of temperatures and equivalence ratios. The maximum H2 and CO was recorded as 16.26 vol% and 33.55 vol %. Intraileally at ER 0.2 gas composition H2, CO, and CH4 show high concentrations of 20.56 vol%, 45.91 vol%, and 13.32 vol%, respectively. At the same time, CO2 was lower as 20.20 vol% at ER 0.2. Therefore, optimum values are suggested for maximum H2 and CO yield and lower concentration of CO2 at ER 0.25 and temperature of 850 degrees C. A predictive model based on an Artificial Neural network is also developed to predict the hydrogen production from product gas composition at various temperatures and equivalence ratios.