Modeling and simulation of biomass pyrolysis processes - Maldonado, Pedro (2022)
Pyrolysis is a thermochemical process where organic matter is decomposed into gaseous products, oils constituted by tars, and non-volatilized residual char, through the elevation of the system temperature (400-800°C), in the absence of oxygen. This process can be modeled and simulated for deeper analysis and optimization. However, since the process is clearly influenced by a high number of operational parameters such as temperature, pressure and dozens of simultaneous parallel reactions, its simulation becomes significantly complex. Thus, the aim of this work is the modeling of a more robust pyrolysis process, considering more components present in tar composition, as well as the evaluation of pyrolysis products distribution under different pyrolysis temperatures: 400, 500 and 600°C. Hence, a model was developed based on second-order equations [1], using pyrolysis temperature as the main variable, achieving as result the yield of three macro components: gases, tar and residual char. The gas fraction is composed by: carbon monoxide (CO), carbon dioxide (CO2), methane (CH4) and hydrogen (H2); tar fraction is constituted by: benzene (C6H6), toluene (C7H8) and naphthalene (C10H8), and the residual char is accompanied by ash in its composition. Simulation was implemented using biomass data based on the composition of olive residues applying the chemical process simulation software UniSim Design. The modeling first step is biomass decomposition in a conversion reactor, applying the yields obtained using the previous equations, while the second step is the decomposition of residual char in a yield reactor, resulting in the elemental constituents: carbon (C(s)), hydrogen gas (H2), oxygen gas (O2), nitrogen gas (N2), solid sulfur (S(s)), and ash. It is possible to note that the pyrolysis model results (see Table 1), implemented with the Software UniSim Design, show, in general, compatibility with the results available in the literature [2, 3]. The model reveals low sensitivity for the yield results, when using different sources of biomass with similar compositions, possibly due to the use of the temperature as the main variable