Modelling approaches to biomass gasification: A review with emphasis on the stoichiometric method

Abstract

Worldwide escalating energy consumption of recent years, due to the earth population growth and the spreading of industrialization, has resulted in an increased concern about the environmental impact of energy conversion systems. Heavy exploitation and extensive use of fossil fuels have indeed also led to envisage their foreseeable depletion, thus opening the way to the use of alternative fuels as biomass. Among thermo-chemical treatments of biomass, gasification is particularly attractive for its release of syngas (or producer gas), suitable of being used in various combustion systems, including internal combustion engines. In principle, biomass contaminants and heavy hydrocarbons can be removed during syngas cleaning, before the actual combustion process, thus leading to an overall cleaner conversion process. At present, demonstrating operational feasibility and effectiveness of gasification technologies and proving long term sustainability, also through the enhancement of fuel flexibility, are recognized as key elements for the development and market diffusion of biomass energy systems. In fact, although gasification has been known for a long time, its control has long requested serious efforts by researchers and manufacturers. Nowadays, new perspectives are imaginable thanks to the use of simulation tools that may reveal particularly useful to improve gasification efficiency and increase the quality of the producer gas. In recent years, several numerical models have been indeed proposed to characterise and predict such a complex process, where drying, pyrolysis, gasification and combustion take place simultaneously. This article presents a general overview of gasification models available, with emphasis on those based on the stoichiometric method. Although this last may seem too restrictive under some circumstances, equilibrium models are useful to predict the maximum yield attainable by a reagent system, since they reproduce an ideal gasification performance. Due to their simplicity and the reduced computational time, these models are suitable of being employed in a first stage of an analysis or within optimization procedures, where the influence of a number of parameters has to be investigated or a choice of the optimal biomass to be treated for a certain scope is to be made.