Non-ideal flow modeling: Which is the classic single-parameter model proposed to describe deviations from plug flow and perfect mixing?

Chemical Engineering Chemical Reaction Engineering Difficulty: Easy
Choose an option
  • A
    Tanks-in-series model
  • B
    Dispersion model
  • C
    Both (a) and (b) are strictly single-parameter
  • D
    Neither (a) nor (b)
  • E
    Axial mixing with dead zone two-parameter model

Answer

Correct Answer: Dispersion model

Explanation

Introduction / Context:Real reactors rarely exhibit ideal plug flow or ideal mixing. Non-ideal behavior is captured using RTD-based models. Two widely used conceptual models are the dispersion model and the tanks-in-series model.

Given Data / Assumptions:

  • Goal: represent axial mixing and spreading around plug flow.
  • Prefer minimal parameterization for tractable fitting.

Concept / Approach:The dispersion model augments the convective plug-flow equation with a single axial dispersion coefficient. In nondimensional form, the behavior depends on one parameter, the dispersion number (Dax / uL), which quantifies deviation from plug flow. The tanks-in-series model uses an integer or real-valued number of perfectly mixed CSTRs in series; while often treated as a single parameter N, it is conceptually different and sometimes regarded as having a discrete nature.

Step-by-Step Solution:Identify single-parameter formulation → dispersion number controls E(t).Fit RTD → obtain Dax/uL from experimental E(t) or F(t).Use parameter to predict conversion and scale-up impacts.

Verification / Alternative check:Limiting cases: dispersion number → 0 gives ideal plug flow; very large values approach mixed-flow behavior, matching expected trends.

Why Other Options Are Wrong:

  • Tanks-in-series: commonly used but often treated as semi-empirical with N; the historically termed single-parameter "proposed" model is the dispersion model.
  • Neither / two-parameter models: contradict common practice in RTD modeling.

Common Pitfalls:Assuming a unique mapping between the two models; while they can be approximately related (N ≈ 1/dispersion number for some ranges), they are not identical.

Final Answer:Dispersion model

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