Performance Analysis of Waste Biomass Gasification and Renewable Hydrogen Production by Neural Network Algorithm

Abstract

Abstract This study assesses renewable hydrogen production via gasification of residual biomass, using artificial neural networks (ANNs) for predictive modeling. The process uses residues from sugarcane and orange harvests, sewage sludge, corn byproducts, coffee remnants, eucalyptus remains, and urban waste. Simulation data from aspen plus® software predict hydrogen conversion from each biomass type, with a three-layer feedforward neural network algorithm used for model construction. The model showed high accuracy, with R2 values exceeding 0.9941 and 0.9931 in training and testing datasets, respectively. Performance metrics revealed a maximum higher heating value (HHV) of 18.1 MJ/kg for sewage sludge, the highest cold gas efficiency for urban and orange waste (82.2% and 80.6%), and the highest carbon conversion efficiency for sugarcane bagasse and orange residue (92.8% and 91.2%). Corn waste and sewage sludge yielded the highest hydrogen mole fractions (0.55 and 0.52). The system can reach relative exergy efficiencies from 24.4% for sugarcane straw residues to 42.6% for sugarcane bagasse. Rational exergy efficiencies reached from 23.7% (coffee waste) to 39.0% (sugarcane bagasse). This research highlights the potential of ANNs in forecasting hydrogen conversion and assessing the performance of gasification-based renewable hydrogen procedures using biomass wastes.