Multi-scale Modeling and Experimental Analysis of Sewage Sludge Gasification: Thermochemical Insights for Hydrogen Production

The management of sewage sludge presents a pressing environmental and economic challenge due to its increasing global production and complex, hazardous composition. Gasification offers a viable method for converting this waste into valuable energy resources. This study investigates whether integrating experimental and computational techniques can enhance the understanding and optimization of sludge gasification. Two types of sewage sludge, SSG from Rethymno and SSD from Dubai were evaluated using an entrained flow gasifier under controlled thermal and flow conditions. The methodology combines equilibrium modeling, computational fluid dynamics (CFD), drop tube reactor (DTR) experiments, and artificial neural network (ANN) modeling. The ANN was combined with Kissinger analysis to obtain kinetics from the ANN outputs and derive thermodynamic parameters used to enhance CFD fidelity. Gas composition analysis and scanning electron microscopy (SEM) revealed that SSD decomposes more easily, with a lower activation energy (42.29–138.31 kJ/mol) and a lower Gibbs free energy. In contrast, SSG demonstrated greater thermal stability and reactivity. SSG achieved consistently higher cold gas efficiency (CGE), reaching 53.66 % in equilibrium modeling, 45.50 % in CFD, and 38.90 % in experiments, compared to SSD’s 48.86 %, 37.81 %, and 31.19 %, respectively. SEM imaging confirmed an increase in porosity and surface area for SSG after gasification. These results indicate that the type of sludge has a significant impact on energy recovery, and that ANN-calibrated thermokinetics and CFD enhance process predictability. This integrated method scales hydrogen generation and promotes sustainable waste-toenergy technology.