(87g) Characterizing Mixing Processes Using Computational Fluid Dynamics and z-Transform

Yin, D. W., The Dow Chemical Company
Deshpande, S., The Dow Chemical Company
Kuchibhatla, S. C., The Dow Chemical Company
Computational fluid dynamics (CFD) has now become widely used in studying the performance of mixing unit operations. It complements experimental studies by making accessible to the investigator quantitative measurements and insights that are difficult or impossible to obtain physically. Motivated by the desire to reduce the need to run repeated CFD simulations, we have in recent years demonstrated our successful use of CFD with z-transform, the discrete-time counterpart of the Laplace transform, to provide a simple, convenient, and robust method for simulating and analyzing the dynamics of mixing processes. Examples that we had illustrated included the open-loop response of a lumped-parameter mixing process (Yin and Yu, 2014 AIChE Annual Meeting Paper 354913), the open-loop response of a distributed-parameter mixing process (Yin, 2015 AIChE Annual Meeting Paper 404589), and the closed-loop response of an unstable mixing process (Yin, 2016 AIChE Annual Meeting Paper 449915).

While we may normally strive to design and implement mixing processes as ideal continuous stirred-tank reactors, the behavior of real mixing processes often does not conform to this paradigm. In order to run mixing processes more effectively and efficiently or to troubleshoot problematic ones, it is helpful to be able to characterize the internal mixing dynamic behavior of such nonideal processes in a quantitative and deterministic way. In our current study we extend our previously work by opening up the blackbox nonideal mixing process and demonstrate how we can analyze its internal mixing dynamics using transient CFD simulations and z-transform. The result allows us to identify and construct models to capture local phenomena such as compartmentalization and short-circuiting, and determine under which conditions the overall mixing process can be deconstructed into a network of smaller classical mixing and transport processes.