2.3 Error Reduction Algorithm for Self-Calibration of Ultrasonic
Flow Meters
In recent years, the use of artificial intelligence in flow metering
have attracted researchers’ attention. For example, neural networks and
support vector regression algorithms have been applied to the data from
temporal and spatial ultrasonic level measurements of the drilling fluid
in the open channel to estimate the flow rate (Chhantyal et al. 2017).
The Least Square Error Reduction technique and neural networks method
have been used for self-calibration of ultrasonic water flow meter
(Yazdanshenashad et al. 2018; Catak and Ergan 2019). However, none of
these self-calibration exercises involves the use of transit-time
ultrasonic flow meter in multiphase flow such as water-bentonite mixture
flow.
Catak and Ergan (2019) reported using the least square error method for
the self-calibration of ultrasonic water flow meter. Three common least
square errors calibration methods have been employed to the data
obtained from DN-20 type ultrasonic flow meter, namely, Linear Least
Squares (LLS), Weighted Least Squares (WLS), and Piecewise Linear Least
Squares (PLR). The results presented found PLR gave the best results in
all cases, while WLS was the best for higher flow rates. Both WLS and
LLS were especially not adequate for low level of flowrate. For example,
the flowmeter accuracy at 10 L/h (0.167 L/min) was around 5-8%, but
after calibration, the improvement was only about 0.8-1.3%. (Catak and
Ergan 2019).
In a recent report, Yazdanshenashad et al. (2018) use the Multi-Layer
Perceptron Neural Network (MLPNN) model to calibrate an ultrasonic flow
meter to achieve an error smaller than 1.5%. The measured flow range
was from 0.2 to 4 m3 per hour. However, it was only
aimed to reduce systematic errors. The authors did not report on the
improvement in random errors which could be revealed by the change in
standard deviation value of the errors. The authors have instead
suggested reducing random errors by averaging large number of data. But
the main drawback of this approach is the loss in data resolution. If it
has to average 1000 data to significantly reduce the random error, it
also means the resolution of data would be compromised by 1000 times.