Optimal design of novel honeycomb photocatalytic reactors for numerical analysis of formaldehyde degradation by CFD modeling

Abstract

As an alternative to the investigation of photocatalysts, it is a potential approach to enhance the photocatalytic performance of the novel photocatalytic reactor by optimizing its geometric structure and reaction conditions. In this work, ve different honeycomb photocatalytic reactors with a de ector and a porous air ow distribution plate were designed and a numerical simulation was performed based on computational uid dynamics (CFD). The simulation results showed that a huge vortex appeared near the entrance of the original model and the velocity distribution inside the reactor was non-uniform, whereas these shortcomings could be effectively overcome when using the 45° de ector model (S-4) compared to the other models. Compared to S-1, the photocatalytic conversion rate of formaldehyde for S-4 was boosted by 7.29% at a ow velocity of 0.04 m s −1 . In addition, it was found that the photocatalytic conversion rate of formaldehyde increased from 55.45-94.73% when the velocity decreased from 0.04 to 0.01 m s −1 , and the photocatalytic removal rate of formaldehyde decreased from 94.73-70.05% as the relative humidity varied from 20-70%. Furthermore, when the irradiance increased from 45 to 265 mW cm −2 , the photocatalytic conversion rate of formaldehyde improved by 10.78%. Overall, this work contributes to the design of the novel honeycomb reactor to acquire the optimized construction of the photocatalytic reactor.

A novel numerical method coupling CFD with PSO vs. a mathematical approach in the modeling of photocatalytic degradation of NO

“…where r(x) is the reaction rate (mol m −2 s −1 ); C s (x) is the NO concentration on the catalyst surface (mol m −3 ), which is different from the NO concentration in the gas bulk phase, C(x), due to mass transfer limitations; k and K are the L-H reaction rate constant (mol m −2 s −1 ) and adsorption equilibrium constant (m 3 mol −1 ), respectively. 26 These two constants are independent of concentration field, fluid dynamics, and reactor geometry. 27 Meanwhile, the kinetic model can be simplified to a pseudo-first-order reaction rate equation: 28…”

“…The algorithm initializes a set of random particles and updates the velocities and locations of particles according to individual extreme value (pbest) and global extreme value (gbest). During the process of evolution, the velocity and location of the ith particle (v and x) are updated according to eqn (25) and (26). 41…”

“…Then, the gas–solid two-phase reaction kinetic expression for NO single-molecule removal is described using eqn (1):where r ( x ) is the reaction rate (mol m −2 s −1 ); C s ( x ) is the NO concentration on the catalyst surface (mol m −3 ), which is different from the NO concentration in the gas bulk phase, C ( x ), due to mass transfer limitations; k and K are the L–H reaction rate constant (mol m −2 s −1 ) and adsorption equilibrium constant (m 3 mol −1 ), respectively. 26 These two constants are independent of concentration field, fluid dynamics, and reactor geometry. 27…”