<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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</p></abstract>

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

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

College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, China

Measurement Science Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Measurement of water content in oil-water mixing flow was restricted by special problems such as narrow measuring range and low accuracy. A simulated...

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

Pubblicato online: 23 ago 2014

Pagine: 219 - 226

Ricevuto: 03 ott 2013

Accettato: 14 ago 2014

DOI: https://doi.org/10.2478/msr-2014-0030

Parole chiave
Oil-water mixture, water content measurement, soft model, multi-sensor, model prediction.

© by Dongzhi Zhang

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Pubblicato online: 23 ago 2014

Pagine: 219 - 226

Ricevuto: 03 ott 2013

Accettato: 14 ago 2014

DOI: https://doi.org/10.2478/msr-2014-0030

Parole chiaveOil-water mixture, water content measurement, soft model, multi-sensor, model prediction.

© by Dongzhi Zhang

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
Oil-water mixture, water content measurement, soft model, multi-sensor, model prediction.