Methane decomposition over a Ni/Cu/Al 2 O 3 catalyst is studied in a two-stage fluidized bed reactor. Low temperature is adopted in the lower stage and high temperature in the upper stage. This allows the fluidized catalysts to decompose methane with high activity in the high temperature condition; then the carbon produced will diffuse effectively to form carbon nanotubes (CNTs) in both low and high temperature regions. Thus the catalytic cycle of carbon production and carbon diffusion in micro scale can be tailored by a macroscopic method, which permits the catalyst to have high activity and high thermal stability even at 1123 K for hydrogen production for long times. Such controlled temperature condition also provides an increased thermal driving force for the nucleation of CNTs and hence favors the graphitization of CNTs, characterized by high resolution transmission electron microscopy (HRTEM), Raman spectroscopy and XRD. Multistage operation with different temperatures in a fluidized bed reactor is an effective way to meet the both requirements of hydrogen production and preparation of CNTs with relatively perfect microstructures.

Production of hydrogen and carbon nanotubes from methane decomposition in a two-stage fluidized bed reactor

In this paper we study stochastic models for the transport of particles in a fluidized bed reactor and compute the associated residence time distribution (RTD). Our main model is basically a diffusion process in [0,A] with reflecting/absorbing boundary conditions, modified by allowing jumps to the origin as a result of transport of particles in the wake of rising fluidization bubbles. We study discrete time birth-death Markov chains as approximations to our diffusion model. For these we can compute the particle distribution inside the reactor as well as the RTD by simple and fast matrix calculations. It turns out that discretization of the reactor into a moderate number of segments already gives excellent numerical approximations to the continuous model. From the forward equation for the particle distribution in the discrete model we obtain in the diffusion limit a partial differential equation for the particle density p(t,x) \[ \frac{\partial}{\partial t} p(t,x) =\frac{1}{2} \frac{\partial^2}{\partial x^...

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Stochastic models for transport in a fluidized bed

Hydrodynamic and solids residence time distribution in a binary bubbling fluidized bed: 3D computational study coupled with the structure-based drag model

Experimental investigation of RTD (residence time distribution) of solids is carried out in a single-stage fluidized bed provided with an internal, using uniformly sized particles and a binary solid mixture, varying gas flow rate, solids rate, bed height, dilution and the bed geometry. The effect of these variables on first and second moments as well as on F-curves has been determined. Using a binary solid mixture or an internal inside the bed is found to reduce backmixing of solids. The data is fitted to FTEM and the values of N obtained were compared for different variables.

Y. Pydisetty

Papers

Residence time distribution of solids in multistage fluidisation