Tie-line phase-fraction uncertainty

ID: tie-line-phase-fraction-uncertainty

Tie-line phase-fraction uncertainty describes the uncertainty of the phase amount inferred from a tie-line phase-fraction fit. It depends on the uncertainties of the overall composition, the endpoint compositions and the model used to fit them. A conditional estimate treating endpoints as exact omits their contribution. All composition vectors below use two independent coordinates of a ternary liquid-liquid tie line; the third mole fraction is fixed by closure.
For fixed endpoints , let and let the overall composition measurement have covariance matrix . With and an interior unconstrained fit,
Here denotes standard uncertainty. The variance formula is exact for this linear estimator with fixed endpoints and known covariance; Gaussian errors are needed for a Gaussian sampling distribution, not for the variance identity itself. This is an instance of covariance-based uncertainty propagation.[ref]
Endpoint errors enter through tie-line phase-fraction sensitivity. Near an exactly balanced composition , with the weight matrix held fixed, differentiating the fit gives
If overall and endpoint measurements are mutually independent, their combined first-order contribution is
Shared instruments or common reference data can correlate these measurements; then cross-covariances, including tie-line endpoint covariance, must also be propagated rather than discarded.[ref]
For an original example, use endpoints and with overall composition , so . Let the first two overall mole fractions have independent standard uncertainties and . Then and , giving with exact endpoints. If each endpoint instead has independent coordinate uncertainties , with all three measurements independent, the endpoint contribution is
The combined value becomes .
When the endpoints approach one another, their difference becomes less informative about phase fraction; at coincident endpoints this inference is undefined. Near the physical bounds, clipping an unconstrained fit to the interval from zero to one changes its sampling distribution, potentially creating a point mass at an endpoint. A symmetric Gaussian error summary can then be misleading. Propagating a joint input model through the actual bounded fit with a Monte Carlo method is one possible analysis; it must retain composition closure and the specified correlations. Neither a small uncertainty nor a small ternary tie-line consistency residual independently verifies chemical potential equality or phase stability.

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