ANN model of waste tyre gasification in bubbling fluidized bed: Gasification strategy and contribution ratios analysis

Fluidized bed gasification presents a viable approach for transforming waste tyre into energy. The suitable gasification strategy and important factors conducted by experiment were difficult, time and labor cost. The simulation methods of CFD and Aspen could not easily handle with complex hydrodynamics behavior in conjunction with chemical reactions for waste tire gasification. To solve these problems, an artificial neural network (ANN) model with different types and algorithm was established, taking into account the influence of various gasification agents and analyzing the contribution ratio of factors. Generalized regression neural network (GRNN) demonstrated the best performance among the used six models. The maximum values for hydrogen, LHV, EC and Y were 50 %, 15.8 MJ/m3, 0.57 and 6.2 m3/kg, respectively. For air with steam gasification strategy, the most important parameters was the ratio of steam to air ratio (S/A). The proposed ANN model offers a valuable resource for conversion of waste tyre into energy, aiding researchers in identifying optimal gasification strategies and operational parameters.