Integrating cross-correlation techniques and neural networks for feedwater flow measurement

D. Ruan; D. Roverso; P. F. Fantoni; J. I. Sanabrias; J. A. Carrasco; L. Fernandez

doi:10.1016/S0149-1970(03)00036-2

Integrating cross-correlation techniques and neural networks for feedwater flow measurement

D. Ruan, D. Roverso, P. F. Fantoni, J. I. Sanabrias, J. A. Carrasco, L. Fernandez

Research output › peer-review

Abstract

This paper reports an early progress of a feasibility study of a computational intelligence approach to the enhancement of the accuracy of flow measurements in the framework of an ongoing cooperation between Tecnatom s.a. in Madrid and the OECD Halden Reactor Project (HRP) in Halden. The aim of this research project is to contribute to the development and validation of a flow sensor in a nuclear power plant (NPP). The basic idea is to combine the use of applied computational intelligence approaches (noise analysis, neural networks, fuzzy systems, wavelets etc.) with existing traditional flow measurements, and in particular with cross-correlation flowmeter concepts.

Original language	English
Pages (from-to)	267-274
Number of pages	8
Journal	Progress in Nuclear Energy
Volume	43
Issue number	1-4 SPEC
DOIs	https://doi.org/10.1016/S0149-1970(03)00036-2
State	Published - 2003

ASJC Scopus subject areas

Nuclear Energy and Engineering
Safety, Risk, Reliability and Quality
Energy Engineering and Power Technology
Waste Management and Disposal

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Cite this

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title = "Integrating cross-correlation techniques and neural networks for feedwater flow measurement",

abstract = "This paper reports an early progress of a feasibility study of a computational intelligence approach to the enhancement of the accuracy of flow measurements in the framework of an ongoing cooperation between Tecnatom s.a. in Madrid and the OECD Halden Reactor Project (HRP) in Halden. The aim of this research project is to contribute to the development and validation of a flow sensor in a nuclear power plant (NPP). The basic idea is to combine the use of applied computational intelligence approaches (noise analysis, neural networks, fuzzy systems, wavelets etc.) with existing traditional flow measurements, and in particular with cross-correlation flowmeter concepts.",

keywords = "Cross-correlation techniques, Feedwater flow measurement, Inferential sensor techniques, Neural networks, Noise analysis",

author = "D. Ruan and D. Roverso and Fantoni, {P. F.} and Sanabrias, {J. I.} and Carrasco, {J. A.} and L. Fernandez",

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T1 - Integrating cross-correlation techniques and neural networks for feedwater flow measurement

AU - Ruan, D.

AU - Roverso, D.

AU - Fantoni, P. F.

AU - Sanabrias, J. I.

AU - Carrasco, J. A.

AU - Fernandez, L.

PY - 2003

Y1 - 2003

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AB - This paper reports an early progress of a feasibility study of a computational intelligence approach to the enhancement of the accuracy of flow measurements in the framework of an ongoing cooperation between Tecnatom s.a. in Madrid and the OECD Halden Reactor Project (HRP) in Halden. The aim of this research project is to contribute to the development and validation of a flow sensor in a nuclear power plant (NPP). The basic idea is to combine the use of applied computational intelligence approaches (noise analysis, neural networks, fuzzy systems, wavelets etc.) with existing traditional flow measurements, and in particular with cross-correlation flowmeter concepts.

KW - Cross-correlation techniques

KW - Feedwater flow measurement

KW - Inferential sensor techniques

KW - Neural networks

KW - Noise analysis

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SP - 267

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JO - Progress in Nuclear Energy

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