Soft Measurement of Water Content in Oil-Water Two-Phase Flow Based on RS-SVM Classifier and GA-NN Predictor

Measurement of water content in oil-water mixing flow was restricted by special problems such as narrow measuring range and low accuracy. A simulated multi-sensor measurement system in the laboratory was established, and the influence of multi-factor such as temperature, and salinity content on the...

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Main Authors: Zhang Dongzhi, Xia Bokai
Format: Article
Language:English
Published: Sciendo 2014-08-01
Series:Measurement Science Review
Subjects:
Online Access:https://doi.org/10.2478/msr-2014-0030
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spelling doaj-735bbccccd52442f86515a51916014332021-09-06T19:22:37ZengSciendoMeasurement Science Review1335-88712014-08-0114421922610.2478/msr-2014-0030msr-2014-0030Soft Measurement of Water Content in Oil-Water Two-Phase Flow Based on RS-SVM Classifier and GA-NN PredictorZhang Dongzhi0Xia Bokai1College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, ChinaCollege of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, ChinaMeasurement of water content in oil-water mixing flow was restricted by special problems such as narrow measuring range and low accuracy. A simulated multi-sensor measurement system in the laboratory was established, and the influence of multi-factor such as temperature, and salinity content on the measurement was investigated by numerical simulation combined with experimental test. A soft measurement model based on rough set-support vector machine (RS-SVM) classifier and genetic algorithm-neural network (GA-NN) predictors was reported in this paper. Investigation results indicate that RS-SVM classifier effectively realized the pattern identification for water holdup states via fuzzy reasoning and self-learning, and GA-NN predictors are capable of subsection forecasting water content in the different water holdup patterns, as well as adjusting the model parameters adaptively in terms of online measuring range. Compared with the actual laboratory analyzed results, the soft model proposed can be effectively used for estimating the water content in oil-water mixture in all-round measuring rangehttps://doi.org/10.2478/msr-2014-0030oil-water mixturewater content measurementsoft modelmulti-sensormodel prediction.
collection DOAJ
language English
format Article
sources DOAJ
author Zhang Dongzhi
Xia Bokai
spellingShingle Zhang Dongzhi
Xia Bokai
Soft Measurement of Water Content in Oil-Water Two-Phase Flow Based on RS-SVM Classifier and GA-NN Predictor
Measurement Science Review
oil-water mixture
water content measurement
soft model
multi-sensor
model prediction.
author_facet Zhang Dongzhi
Xia Bokai
author_sort Zhang Dongzhi
title Soft Measurement of Water Content in Oil-Water Two-Phase Flow Based on RS-SVM Classifier and GA-NN Predictor
title_short Soft Measurement of Water Content in Oil-Water Two-Phase Flow Based on RS-SVM Classifier and GA-NN Predictor
title_full Soft Measurement of Water Content in Oil-Water Two-Phase Flow Based on RS-SVM Classifier and GA-NN Predictor
title_fullStr Soft Measurement of Water Content in Oil-Water Two-Phase Flow Based on RS-SVM Classifier and GA-NN Predictor
title_full_unstemmed Soft Measurement of Water Content in Oil-Water Two-Phase Flow Based on RS-SVM Classifier and GA-NN Predictor
title_sort soft measurement of water content in oil-water two-phase flow based on rs-svm classifier and ga-nn predictor
publisher Sciendo
series Measurement Science Review
issn 1335-8871
publishDate 2014-08-01
description Measurement of water content in oil-water mixing flow was restricted by special problems such as narrow measuring range and low accuracy. A simulated multi-sensor measurement system in the laboratory was established, and the influence of multi-factor such as temperature, and salinity content on the measurement was investigated by numerical simulation combined with experimental test. A soft measurement model based on rough set-support vector machine (RS-SVM) classifier and genetic algorithm-neural network (GA-NN) predictors was reported in this paper. Investigation results indicate that RS-SVM classifier effectively realized the pattern identification for water holdup states via fuzzy reasoning and self-learning, and GA-NN predictors are capable of subsection forecasting water content in the different water holdup patterns, as well as adjusting the model parameters adaptively in terms of online measuring range. Compared with the actual laboratory analyzed results, the soft model proposed can be effectively used for estimating the water content in oil-water mixture in all-round measuring range
topic oil-water mixture
water content measurement
soft model
multi-sensor
model prediction.
url https://doi.org/10.2478/msr-2014-0030
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AT xiabokai softmeasurementofwatercontentinoilwatertwophaseflowbasedonrssvmclassifierandgannpredictor
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