Estimation of micromixing parameters from tracer concentration fluctuation measurements

Abstract

A method of micromixing parameter estimation is proposed which is convenient and applicable to a wide variety of models. One conducts a typical tracer response test, except that in addition to the (timeaveraged) mean tracer response, the variance of the tracer response is also recorded as a function of time. Micromixing parameters for a given model are determined by equating the predicted and measured values of the time response of the tracer concentration variance. The predicted tracer concentration fluctuation response has been determined for both step and pulse tracer tests, for the IEM model, the coalescenceredispersion model, and the two-, three-and four-environment models. Parameter estimates were calculated by minimizing the mismatch between predicted and measured concentration fluctuation responses.Preliminary numerical results indicate that the method provides satisfactory parameter estimates, even from moderately noisy measurements. These results also show that statistical analysis of the fit to measured response data can provide discrimination between competing mixing models.

Detention and mixing in free water wetlands

“…There are dozens of published methods for constructing such flow models (see the review of Call (1989), for example). Many of these consist of series and parallel combinations of the two ideal extremes of mixing: totally mixed zones (corresponding to a continuous stirred tank reactor or CSTR in the reaction literature) and totally unmixed zones (corresponding to a plug flow reactor or PFR in the reaction literature).…”

Modeling an annular flow tubular reactor

“…A stochastic micro-mixing model was presented by Too et al (1986) to describe axial dispersion in tubular reactors. Call and Kadlec (1989) developed a stochastic micro-mixing model to describe flow systems. The authors estimated a micro-mixing parameter from observed fluctuations of pulse and step inputs, and the interchange with the mean (IEM) model and coalescence and dispersion model tracking tracer concentration at a single point, as may be seen in the turbulent velocity fluctuations; a very interesting stochastic model was thus developed.…”

Modeling of imperfect mixing and its effects on polymer properties

“…They are proposed to correct drawbacks of the original approaches, but introduce some additional complexity (Goto and Matsubara, 1975;Ritchie and Tobgy, 1978;Call and Kadlec, 1989).…”