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The 107 trees in the second data set were used to carry out model validation on independent data from a population that may be different from the one used in model fitting.
This narrow data base is used for model validation on both a monthly and an hourly basis.
The remaining 238 trees were used for model validation on data from the same population as the model fitting data set.
With the purpose of testing the methodology here presented, a numerical model validation on the basis of experimental results from a scale model test [34] is conducted.
After a preliminary model validation on the grounds of some experiments carried out ad hoc, the mathematical model is used for the analysis of the flashback phenomenon, so as to point out the key parameters that affect burner operation stability.
Such discrepancies could (at least partly) be attributable to the higher experimental error inherent to NPQ800 measurements (see in Table 4 and Figure 8b the large standard deviations among the 3 experimental replicates of the random combinations tested for model validation: on average, RSD = 19.7% for NPQ800 and ≤9.0% for the other responses).
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A traditional approach is to split data and perform model development (training) on a sample, and model validation (test) on the remainder.
Moreover, assisting points coded +α and −α were considered for model validation based on the models.
Next, model analysis, model identification and model validation based on available reactor operational data are performed.
Model validation based on closed-loop step responses also used as verification of the results.
One challenge in industrial applications is model validation based on experimental data.
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