Thermodynamic models and experimental data exhibit systematic and random
errors. The severity of
their errors depends on their use, such as for process calculations in
a process simulator. Similarly, the
value of better thermodynamic models and/or data should be measured
with reference to such use.
Strategies for quantification of such thermodynamics-induced
process uncertainties via Monte Carlo
simulation, regression analysis, and analogies to optimization are
described, with simple examples. Such
approaches can be used for safety-factor/risk analysis, guidelines for
process simulator use, experimental
design, and model comparisons.
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