Abstract
This work presents a hybrid modelling approach for a downdraft sewage sludge gasifier within the Shit2Power (S2P) process. The gasifier is represented in CHEMCAD NXT by a series of four standard reactors that combine stoichiometric and equilibrium models with a data-driven correction step to account for deviations from ideal Gibbs equilibrium. Reaction conversions in the correction reactor are fitted to experimental synthesis gas compositions reported by Werle (2014) [7] for 30 operating points with varying equivalence ratios and reactor inlet air temperatures. The calibrated hybrid reactor model is evaluated against these data and shows conservative agreement for the combustible gas components of the synthesis gas. To overcome the limitations of linear interpolation between fitted operating points, several machine learning approaches are evaluated to predict the reaction conversions, and boosted neural networks are selected as a compromise between prediction accuracy and smooth behaviour in the operating window. After implementation in the CHEMCAD flowsheet, sensitivity studies with respect to equivalence ratio and reactor inlet air temperature show good agreement between predicted and fitted gas compositions, considering the limited data available. This demonstrates that the combination of flowsheet simulation and machine learning provides a promising framework for modelling sewage sludge gasification in the S2P process, but that significantly more experimental and training data are needed for practical application.