Water level in boiling water reactors — Measurement, modelling, diagnostic

Hampel, Rainer; Traichel, Anke; Fleischer, S.; Kastner, Robert

doi:10.1016/s0149-1970(03)00018-0

Cited by 1 publication

(1 citation statement)

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“…[3][4][5] Calibration of capacitance level sensor (CLS) is discussed in Terzic et al 6 using a back propagation algorithm; compensation for effect of temperature is achieved using one redundant sensor. Calibration by fast Fourier transform for level measurement using acoustic wave and radiation sensor is discussed in Jianjun and Xiaobin, 7 Zhi et al, 8 Chen et al, 9 Gerding et al, 10 and Jeffries et al 11 A radiation method of level measurement is discussed in Hampel et al 12 and calibrated using artificial neural network (ANN) algorithms. Optical method of level measurement is discussed in Musyev and Karlik 13 and calibration is done using polynomial equation.…”

Section: Introductionmentioning

confidence: 99%

A Practically Validated Adaptive Calibration Technique using Optimized Artificial Neural Network for Level Measurement by Capacitance Level Sensor

Venkata

Roy

2015

Measurement and Control

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Design of an adaptive calibration technique using an optimized artificial neural network for liquid-level measurement is discussed in this paper. The objective of the present work is to design and validate an adaptive calibration technique so as (1) to extend the linearity range of measurement to 100% of full-scale input range and (2) to make the measurement technique adaptive of variations in tank diameter, permittivity of liquid, liquid temperature, and to achieve objectives (1) and (2) using an optimized neural network. An optimized artificial neural network is a network having least possible number of hidden layers to achieve minimum mean square error between outputs and targets by comparing various algorithms, schemes, and transfer functions of neuron. The output of capacitance level sensor is capacitance. A data conversion unit is used to convert it to voltage. A suitable optimized artificial neural network is designed and used in place of conventional calibration circuit. The proposed technique is tested with simulated data and validated with practical data. Results show that proposed technique has fulfilled the set objectives.

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