Multiscale reaction engineering

Lerou J, Ng K. Chemical reaction engineering a multiscale approach to a multiobjective task. Chem. Eng. Sci. 1996 51 1595-1614. 

The first objective is represented by the need to increase the productivity and selectivity of both existing and new processes through intelligent operations and multiscale control of processes. This objective is sustained by the important results obtained thanks to the synthesis of a new class of engineered porous supports and catalysts. So the catalytic reactions and separation processes that use these materials can be efficiently controlled. 

In general, previous experimental values and computational data can be used to estimate the kinetic parameters needed for a KMC-based simulation. These parameters may be improved and adjusted after KMC simulation, if an initially identified reaction mechanism is shown to be insufficient to capture the experimental behavior. Most importantly, the DFT+KMC multiscale simulation approach establishes a well-defined pathway for taking atomistic-level details and reaching lab-level experimental results, which can be used to accelerate the discovery process and enhance engineering design. 

Saldccioli, M., M. Stamatakis, S. Caratzoulas D. G. Vlachos (2011) A review of multiscale modeling of metal-catalyzed reactions Mechanism development for complexity and emergent behavior. Chemical Engineering Science, 66, 4319-4355,1SSN 00092509. 

The development of global reaction kinetics is a stage wise scale-up approach. The various stages of this multiscale approach are summarized in Fig. 13.6 and include (a) microreactor experiments over powdered catalyst for the determination of the intrinsic reaction kinetics, (b) synthetic gas bench experiments over small monolith samples to account for intraporous diffusion of species, and (c) validation on steady-state and transient engine tests. 

Two important challenges exist for multiscale systems. The first is multiple time scales, a problem that is familiar in chemical engineering where it is called stiffness, and we have good solutions to it. In the stochastic world there doesn t seem to be much knowledge of this phenomenon, but I believe that we recently have found a solution to this problem. The second challenge—one that is even more difficult—arises when an exceedingly large number of molecules must be accounted for in stochastic simulation. I think the solution will be multiscale simulation. We will need to treat some reactions at a deterministic scale, maybe even with differential equations, and treat other reactions by a discrete stochastic method. This is not an easy task in a simulation. 

The configuration of the tissue engineering bioreactor and the culturing conditions can vary widely. One example is the well-stirred bioreactor where several scaffolds seeded with cells are fixed on needles and cultured in continuously stirred media. This is the so-called dynamic tissue culture method that has been shown to promote both cell proliferation and ECM component deposition in bioartificial tissues [137-139]. However, the aforementioned multiscale model can handle other reactor configurations by appropriately changing the boundary condition [2] of the diffusion-reaction problem. 

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