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

2014 ◽  
Vol 14 (4) ◽  
pp. 219-226 ◽  
Author(s):  
Dongzhi Zhang ◽  
Bokai Xia
Keyword(s):  
Water Content ◽  
Measurement Model ◽  
Support Vector ◽  
Svm Classifier ◽  
Model Parameters ◽  
Water Mixture ◽  
Measuring Range ◽  
Two Phase ◽  
Soft Measurement ◽  
Oil Water

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

scholarly journals A novel method for total chlorine detection using machine learning with electrode arrays

RSC Advances ◽  
2019 ◽  
Vol 9 (59) ◽  
pp. 34196-34206
Author(s):  
Zhe Li ◽  
Shunhao Huang ◽  
Juan Chen
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cyclic Voltammetry ◽  
Support Vector Regression ◽  
Principal Component ◽  
Measurement Model ◽  
Support Vector ◽  
Electrode Arrays ◽  
Soft Measurement ◽  
Novel Method

Establish soft measurement model of total chlorine: cyclic voltammetry curves, principal component analysis and support vector regression.

scholarly journals Pattern transitions of oil-water two-phase flow with low water content in rectangular horizontal pipes probed by terahertz spectrum

2015 ◽  
Vol 23 (24) ◽  
pp. A1693 ◽  
Author(s):  
Xin Feng ◽  
Shi-Xiang Wu ◽  
Kun Zhao ◽  
Wei Wang ◽  
Hong-Lei Zhan ◽  
...  
Keyword(s):  
Water Content ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Two Phase ◽  
Horizontal Pipes ◽  
Terahertz Spectrum ◽  
Oil Water

scholarly journals Volume Fraction Measurement in Crude Oil-Water Two-Phase Mixture using a Neutron Beam

2017 ◽  
Vol 2 (2) ◽  
pp. 57-63
Author(s):  
Abdullah A. Kendoush ◽  
Hameed B. Mahood ◽  
Ibrahim G. Fiadh
Keyword(s):  
Crude Oil ◽  
Neutron Source ◽  
Neutron Beam ◽  
Volume Fraction ◽  
Water Mixture ◽  
Two Phase ◽  
Oil In Water ◽  
Oil Water ◽  
Phase Mixture ◽  
Fraction Measurement

A neutron beam has been used to measure the volume fraction of crude oil in water of non- flow two-phase mixture experimentally.241Am-Be neutron source were used with an activity of 3.7x104 MBq. The volume fraction was simulated by using small plastic tubes filled with oil and immersed in non-flow water tube. The results show that it is feasible to measure the volume fraction of crude oil in a crude oil-water mixture.

Volume Water Content Prediction of Variable Bulk Density Soil Based on Spectral Characteristics

2013 ◽  
Vol 321-324 ◽  
pp. 309-313
Author(s):  
Zhen Chen ◽  
Lan Cai
Keyword(s):  
Support Vector Machine ◽  
Water Content ◽  
Bulk Density ◽  
Near Infrared ◽  
Spectral Characteristics ◽  
Characteristic Vector ◽  
Support Vector ◽  
Model Parameters ◽  
Undisturbed Soil ◽  
Soil Volume

In order to eliminate effects of soil bulk density variance and soil properties difference on soil water content forecast precision, a method was proposed to forecast soil volume water content based on near-infrared spectroscopy. This paper investigated relationship between volume water content and near infrared spectral reflectance characteristics in 900-2500nm band, used spectral parameters and support vector machine to built quantitative prediction model for three type variable bulk density soil volume water content, normalized signal characteristics by relative dry soil characteristic vector, putted forward three further processing methods. It was used support vector machine (SVM) method to establish spectral characteristics inverting soil volume water content model of undisturbed soil, and model parameters were optimized by genetic algorithm, through predict error comparison, the final determination was that relative characteristics variation of first-order derivative signal as model input characteristic vector, GA-SVM model prediction had best effect and its forecast error was 1.7866.

Determination of the Pressure Drop Optimum Pipe Size for a Two-Phase Slug Flow in an Inclined Pipe

1970 ◽  
Vol 92 (4) ◽  
pp. 717-726 ◽  
Author(s):  
G. Singh ◽  
P. Griffith
Keyword(s):  
Pressure Drop ◽  
Pressure Gradient ◽  
Slug Flow ◽  
Total Pressure ◽  
Model Parameters ◽  
Shear Stresses ◽  
Water Mixture ◽  
Two Phase ◽  
Inclined Pipe ◽  
Pipe Size

A simple model of two-phase slug flow in inclined pipes is proposed. The model parameters are determined experimentally using five different size copper pipes at 5, 10, 15 deg inclinations on an air-water mixture at one atmosphere with up flow. The model predicts the total pressure gradient due to the sum of gravity and wall shear stresses. An investigation of the relationship between pressure gradient and pipe size results in an optimum pipe size at which the pressure gradient is minimum. A comparison between the simplified model predictions and experimental measurements shows a good agreement in the total pressure drop.

scholarly journals Recognition for Human Gestures Based on Convolutional Neural Network Using the Off-the-Shelf Wi-Fi Routers

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Haixia Yang ◽  
Zhaohui Ji ◽  
Jun Sun ◽  
Fanan Xing ◽  
Yixian Shen ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
High Speed ◽  
Recognition Rate ◽  
Recognition System ◽  
Support Vector ◽  
Svm Classifier ◽  
Model Parameters ◽  
Noise Interference ◽  
Human Gestures

Human gestures have been considered as one of the important human-computer interaction modes. With the fast development of wireless technology in urban Internet of Things (IoT) environment, Wi-Fi can not only provide the function of high-speed network communication but also has great development potential in the field of environmental perception. This paper proposes a gesture recognition system based on the channel state information (CSI) within the physical layer of Wi-Fi transmission. To solve the problems of noise interference and phase offset in the CSI, we adopt a model based on CSI quotient. Then, the amplitude and phase curves of CSI are smoothed using Savitzky-Golay filter, and the one-dimensional convolutional neural network (1D-CNN) is used to extract the gesture features. Then, the support vector machine (SVM) classifier is adopted to recognize the gestures. The experimental results have shown that our system can achieve a recognition rate of about 90% for three common gestures, including pushing forward, left stroke, and waving. Meanwhile, the effects of different human orientation and model parameters on the recognition results are analyzed as well.

scholarly journals Experimental Investigation of Seepage Mechanism on Oil-Water Two-Phase Displacement in Fractured Tight Reservoir

Geofluids ◽  
2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Xuyang Zhang ◽  
Jianming Zhang ◽  
Cong Xiao
Keyword(s):  
Hydraulic Fracturing ◽  
Water Content ◽  
Production Rate ◽  
Oil Recovery ◽  
Hydraulic Fractures ◽  
Fracture System ◽  
Two Phase ◽  
Phase Displacement ◽  
Before And After ◽  
Oil Water

As a type of unconventional oil and gas resources, tight sandstone reservoir has low permeability and porosity properties and thus is commonly necessary to develop through hydraulic fracturing treatment. Due to the coexistence of natural fractures and induced hydraulic fractures, the heterogeneity of reservoir permeability becomes severe and therefore results in complicated fluid seepage mechanism. It is of significance to investigate the oil-water two-phase seepage mechanics before and after the hydraulic fracturing stimulation with the aim of supporting the actual production and development of oilfield. This paper experimentally investigated the influences of fracture system on seepage characteristics of two-phase displacement in sample cores of fractured tight sandstones. In details, the changes of injection rate, cumulative production rate, recovery ratio, and water content were analyzed before and after the hydraulic fracturing treatments. To further analyze the displacement characteristics of the sample core, the displacement indices of four rock samples in different displacement stages were investigated. The sensitivity of sample core displacement indices to many key factors, including injection time, oil production rate, oil recovery factor and injection multiple factor, and moisture (i.e., water content was 95%, 98%, and 99.5%, respectively), before and after the hydraulic fracturing treatments were obtained synthetically. Besides, the relationship between recovery difference and contribution of fracture to permeability was explored at different water contents. The experimental results reveal that the fracture system shortens the water-free production period and hence reduces the recovery rate. The greater the contribution of fractures to permeability, the lower the recovery of water during this period.

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