Figure 10: The interpolated error information based on training
points.
The interpolated error information will be used to revise the flowmeter
output as follow:
\(v_{\text{Rev}}=\frac{v_{\text{TTUF}}}{Z_{i}}\) … (21)
By applying the correction equation (21), the mean flow coefficient\(\overset{\overline{}}{C_{\text{\ Rev}}}\) has improved from 0.917 to
1.012. The machine learning method such as 2D interpolation is
inherently non-linear, so it can detect almost any kind of non-linear
behaviors and produce a better estimate for the output and response. It
gives a better accuracy than linear regression approach such as LLS
method.
As summarized in Table 3, the mean relative error\(\overset{\overline{}}{\epsilon_{\text{Rev}}}\) was revised from
-0.0830 (-8.30%) to +0.0124 (+1.24%). However, the error was reduced
to less than 1.5% but at the cost of increased standard deviation,\(\sigma_{\text{Rev}}\) from ±0.2030 (20.30%) to ±0.2702 (27.02%).