Numerical modelling of a fast pyrolysis process in a bubbling fluidized bed reactor

In this study, the Eulerian-Granular approach is applied to simulate a fast pyrolysis bubbling fluidized bed reactor. Fast pyrolysis converts biomass to bio-products through thermochemical conversion in absence of oxygen. The aim of this study is to employ a numerical framework for simulation of the fast pyrolysis process and extend this to more complex reactor geometries. The framework first needs to be validated and this was accomplished by modelling a lab-scale pyrolysis fluidized bed reactor in 2-D and comparing with published data. A multi-phase CFD model has been employed to obtain clearer insights into the physical phenomena associated with flow dynamics and heat transfer, and by extension the impact on reaction rates. Biomass thermally decomposes to solid, condensable and non-condensable and therefore a multi-fluid model is used. A simplified reaction model is sued where the many components are grouped into a solid reacting phase, condensable/non-condensable phase, and non-reacting solid phase (the heat carrier). The biomass decomposition is simplified to four reaction mechanisms based on the thermal decomposition of cellulose. A time-splitting method is used for coupling of multi-fluid model and reaction rates. A good agreement is witnessed in the products yield between the CFD simulation and the experiment

Multiphase phenomena in Diesel fuel injection systems

Fuel Injection Equipment (FIE) are an integral component of modern Internal Combustion Engines (ICE), since they play a crucial role in the fuel atomization process and in the formation of a fuel/air combustible mixture, consequently affecting efficiency and pollutant formation. Advancements and improvements of FIE systems are determined by the complexity of the physical mechanisms taking place; the spatial scales are in the order of millimetres, flow may become locally highly supersonic, leading to very small temporal scales of microseconds or less. The operation of these devices is highly unsteady, involving moving geometries such as needle valves. Additionally, extreme pressure changes imply that many assumptions of traditional fluid mechanics, such as incompressibility, are no longer valid. Furthermore, the description of the fuel properties becomes an issue, since fuel databases are scarce or limited to pure components, whereas actual fuels are commonly hydrocarbon mixtures. Last but not least, complicated phenomena such as phase change or transition from subcritical to transcritical/supercritical state of matter further pose complications in the understanding of the operation of these devices

A hybrid SVR-PSO model to predict a CFD-based optimised bubbling fluidised bed pyrolysis reactor

Comprehensive scrutiny is necessary to achieve an optimised set of operating conditions for a pyrolysis reactor to attain the maximum amount of the desired product. To reach this goal, a computational fluid dynamic (CFD) model is developed for biomass fast pyrolysis process and then validated using the experiment of a standard lab-scale bubbling fluidised bed reactor. This is followed by a detailed CFD parametric study. Key influencing parameters investigated are operating temperature, biomass flow rate, biomass and sand particle sizes, carrier gas velocity, biomass injector location, and pre-treatment temperature. Machine learning algorithms (MLAs) are then employed to predict the optimised conditions that lead to the maximum bio-oil yield. For this purpose, support vector regression with particle swarm optimisation algorithm (SVR-PSO) is developed and applied to the CFD datasets to predict the optimum values of parameters. The maximum bio-oil yield is then computed using the optimum values of the parameters. The CFD simulation is also performed using the optimum parameters obtained by the SVR-PSO. The CFD results and the values predicted by the MLA for the product yields are finally compared where a good agreement is achieved