Dynamic modeling with experimental calibration for the syngas production from biomass fixed‐bed gasification

Abstract

AbstractIn this article, a dynamic biomass gasification model was developed based on the hybrid peripheral fragmentation and shrinking‐core model. To improve the accuracy of syngas generation transient prediction, the chemical kinetic model was trained using global surrogate optimization techniques. The pre‐exponential corrections of kinetic reaction rates are calibrated under noncatalytic conditions, employing experimental transient data of syngas generation rate and compositions under different temperatures and gasifying agents. The Dynamic Coordinate search using Response surface model and the Gap Optimized Multi‐objective Optimization using Response Surfaces framework were employed as the numerical solvers for finding the global optimum solution of the pre‐exponential factors. The calibrated particle‐level kinetic models based on both single‐objective and multi‐objective approaches have been validated by experimental data in four different biomass gasification scenarios. The calibrated model shows an over 95% decrease in terms of integrated squared error (ISE)‐based model mismatch when compared with the original model.