Lu, L. G., Xi; Gel, Aytekin; Wiggins, Gavin M.; Crowley, Meagan; Pecha, Brennan; Shahnam, Mehrdad; Rogers, William A.; Parks, James; Ciesielski, Peter N. "Investigating biomass composition and size effects on fast pyrolysis using global sensitivity analysis and CFD simulations," Chemical Engineering Journal Vol. 421, 2021, p. 127789. https://doi.org/10.1016/j.cej.2020.127789. (https://www.sciencedirect.com/science/article/pii/S1385894720339097)
Abstract: It is notoriously difficult to build an accurate universal model for biomass pyrolysis due to its sensitivity to a wide number of critical material attributes such as chemical species and physical sizes. In this work, a biomass pyrolysis kinetics with 32 heterogeneous reactions and 59 species was implemented in an open-source multiphase computational fluid dynamics (CFD) software MFiX and validated against two different experimental pyrolysis data sets that provided detailed data describing chemical component yields. The reaction scheme was then used to build a surrogate model and assess the sensitivity of pyrolysis yields to feedstock compositions. The sensitivity analysis determined that the yield of bio-char showed a strong positive sensitivity to the carbon-rich lignin and tannin pseudo-species in the reaction scheme while the bio-oil and bio-gas were correlated to oxygen-rich lignin pseudo-species. The reaction scheme was then integrated into a coarse-grained discrete element model to simulate fast pyrolysis in a bubbling fluidized bed over a range of feedstock particle sizes. The reactor simulations showed further sensitivity to particle size and hydrodynamics. Notably, particles under 0.5 mm have small heat transfer limitations but left the reactor before completely converting and thus reduced the bio-oil yield. Results from this study can be used to guide future development of highly accurate models for fast pyrolysis reactors with a variety of feedstock properties and operating conditions.
Keywords: Biomass; Pyrolysis; Fluidized bed; Kinetics; Surrogate model; Sensitivity study