Numerical approaches and comprehensive models for gasification process: A review

Nowadays, gasification is seen as a worldwide settled technique for sustainably generating energy from residues. Its final product is a synthetic gas which may be used for several purposes such as fueling engines, industry and the transport sector. This technique involves several complex reactions, different thermodynamic regimes and environments, and may be held under a myriad of operational conditions. Thus, simulation tools have become very useful to predict the optimal parameters to be applied and the heating content of the final product, therefore producing the most suitable syngas for the desired utilizations. Computational fluid dynamics (CFD) reduces the time needed to design and implement each gasification experiment, using mathematical approaches and developing specific codes regarding the type of feedstock, reactor utilized and expected atmosphere. This paper reviewed the major works on numerical methods for gasification and co-gasification of feedstocks, such as biomass, waste and fossil fuels, published in the last two decades. A thorough explanation of the solid and gaseous phases, their interconnections and behavior was presented, while relating the characteristics of the main methods to the final product obtained. Major trends were identified as well as some suggestions regarding future perspectives were done. Kinetic, thermodynamic and computational fluid dynamic models were seen to be preferred concerning the study of syngas production from distinct types of feedstocks. Also, biomass and coal were the most frequently used feedstocks, with 37% and 24% of the total share, respectively. Relative to the gasifier type, fluidized beds were used in almost 50% of the assessments, followed by entrained beds, mostly used for coal gasification. Only major trends could be identified for some of the experimental conditions applied, due to the high variances seen for different feedstocks or reactor features. This review supports the need to continue developing new models and adapting the existing ones in order to reach broader conclusions, since distinct parameters afford completely different outcomes.