CN1538168A - Oil-gas two-phase flow measuring method based on copacitance chromatorgraphy imaging system and its device - Google Patents

Abstract

A method based on capacitance chromatographic imaging system for measuring the two-phase oil-gas stream features that a reverse projection algorithm is used to reconfigure the oil-gas stream's medium distribution image to real-time show the stream type in pipeline, a fuzzy pattern recognization is used for in-line automatic recognization of stream type, a combined image rebuilding algorithm is used to rebuild the image for showing oil and gas distribution, and the image is processed to obtain the porosity of the oil-gas stream. Its equipment is composed of array-type capacitance sensor, data acquisition unit and computer. Its advantages are high speed and high precision.

Description

Oil-gas two-phase flow measuring method and device thereof based on capacitance tomography system

Technical field

The present invention relates to oil-gas two-phase flow and measure, relate in particular to a kind of oil-gas two-phase flow measuring method and device thereof based on capacitance tomography system.

Background technology

Two-phase flow system has very at numerous industrial circles such as oil, chemical industry to be used widely.The measurement of diphasic stream parameter such as flow pattern, voidage etc. has great importance to commercial production.Because the complicacy of diphasic flow system, the on-line measurement of these parameters is very difficult, and at present, the two-phase flow on-line measurement instrument that can be applied in the actual industrial is to count seldom.

In the actual industrial two-phase flow system, the difference of two phase flow pattern not only influences Two-phase flow characteristic, heat transfer and mass-transfer performance, and influences reliability of system operation and efficient.The online demonstration of two phase flow pattern and identification are all significant to design and running of monitoring, fault diagnosis and the pipe system of production run etc.Simultaneously, the measurement to other parameter of two-phase flow also is very helpful.Therefore, flow pattern is the basic parameter that has important engineering significance in the two-phase flow system, and the automatic identification of flow pattern has important industrial application value and learning value.Regrettably diphasic flow is the system of a complexity, and the phase interface that each alternate existence is variable at random causes the kind of flow pattern varied, and causes the variation of flow pattern in process fluid flow to have randomness.Two phase flow pattern not only is subjected to the influence of each phase medium self-characteristic, and be subjected to the influence of industrial system operating mode (geometric configuration, wall characteristic and the mounting means etc. that comprise pressure, each separate phase flow rate, each minute phase content, pipeline), so the on-line automatic identification of flow pattern is very difficult.At present, the flow pattern discrimination method has ocular estimate, high-speed photography method, ray attenuation method, and based on the methods such as signal analysis technology of differential pressure/pressure, voidage fluctuation signal.But existing these methods are owing to the tube section phase distributed intelligence that is difficult to obtain real reflection flow pattern, and flow pattern identification accuracy rate is often not high enough, and practical application is also very limited.

Gas phase content in the biphase gas and liquid flow is called voidage again, characterize the gas cross section and contain rate, technological design, the operation conditions obtained industrial system of this parameter are monitored, and the measurement of the automatic control of two-phase flow system and metering and further two-phase flow etc. all has great importance.The method of present measurement voidage mainly contains three major types: the first kind is to adopt the single-phase flow measurement instrument.These class methods need be passed through a large amount of theoretical research and experimental verifications, obtain the two-phase flow measurement model, operating mode of living in when its range of application often is confined to obtain measurement model.Second class is to adopt partition method, with respectively being separated of two-phase fluid, measures each phase content.These class methods can be measured the two-phase flow voidage, but owing to will separate two-phase fluid, system complex may influence the continuity of industrial process, so has limited its range of application.The 3rd class methods are to adopt various new technologies, develop specific instrument and directly measure the two-phase flow potpourri.This class technology comprises radiant rays technology, nuclear magnetic resonance technique, electromagnetic technique, ultrasonic technology, spectral technique, laser doppler technique and process tomographic imaging technology etc., but still is in the experimental study stage at present mostly based on the measuring two-phase flow parameter of this class technology.

Capacitance chromatography imaging (Electrical Capacitance Tomography, brief note ECT) technology is the nearly 20 years a kind of novel detection techniques that grow up, its know-why comes from medicine CT, be applicable to the parameter measurement of the two-phase flow system that non-conductive medium constitutes, be a kind of process imaging technique of present broad research, have simple in structure, non-invasi, characteristics such as speed is fast, cost is low and security performance is good.Because the ECT technology can record distribute mutually in the reflection tube section local and real-time information microcosmic on non-invasi ground under the interference flowing field situation not, so its problems such as the identification of solution two phase flow pattern, voidage measurement that appear as provide an effective way.But the measuring two-phase flow parameter instrument based on this technology still is in the laboratory study stage, and using apart from industry spot still has certain distance.

Oil-gas two-phase flow is that a kind of common gas-liquid two-phase flows, extensively be present in fields such as oil (exploitation of oil gas field and oil gas are carried), chemical industry, oil refining, power, the measurement of important parameters such as the flow pattern of oil-gas two-phase flow, voidage is adjusted energy reserves, pipeline transportation, process control and metering and is all had great significance, but at present good measuring method is not arranged as yet.Along with production run metering, energy-conservation and raising that control requires, it is more and more urgent that the measurement of these parameters just becomes.

Summary of the invention

The purpose of this invention is to provide a kind of oil-gas two-phase flow measuring method and device thereof based on capacitance tomography system.

Method is: 1) adopt backprojection algorithm to carry out image reconstruction, realize the online demonstration of flow pattern; 2) adopt Fuzzy Pattern Recognition Method to carry out the on-line automatic identification of flow pattern, can discern homogeneous flow, laminar flow, wave flow, annular flow and slug flow equal flow type, and can calculate the time span of slug according to the clock in the computing machine; 3) employing reconstructs the oil-gas two-phase flow cross-sectional image, and calculates the voidage of oil-gas two-phase flow according to the gray-scale value of image based on the combined novel image reconstruction algorithm of Tikhonov regularization theory and algebraic reconstruction technique.

Device has capacitive array sensor successively, capacitance data collecting unit and the computing machine that is used for image reconstruction, data processing and demonstration, said capacitive array sensor has the insulation tube of joint flange as the sensing pipeline section with two ends, sensing pipeline section outboard shafts is to evenly being pasted with the copper foil electrode array, the sensing pipeline section outside is provided with fixed support, radome, radial electrode, is provided with electric capacity/voltage transformation module on radial electrode:

The present invention can realize the real-time demonstration of flow pattern in the pipeline, the on-line automatic identification and the voidage measurement of flow pattern.Adopt backprojection algorithm to carry out the image reconstruction that flow pattern shows, flow pattern display speed 50 frames/more than second.The two-phase flow dielectric distribution image that reconstructs according to backprojection algorithm, adopt Fuzzy Pattern Recognition Method to carry out the on-line automatic identification of flow pattern, for typical flow patterns such as homogeneous flow, laminar flow, wave flow and annular flows, the accuracy rate of its identification is higher than 95%, differentiate the used time of flow pattern less than 0.3 second, be higher than 90% for the identification accuracy rate of slug flow.Employing reconstructs the oil-gas two-phase flow cross-sectional image based on the combined novel image reconstruction algorithm of Tikhonov regularization theory and algebraic reconstruction technique, and calculates the voidage of oil-gas two-phase flow according to the gray-scale value of image, and the voidage error is less than 5%.

Description of drawings

Fig. 1 is that oil-gas two-phase flow is measured block diagram;

Fig. 2 is the typical flow regime map of horizontal tube biphase gas and liquid flow;

Fig. 3 a～e is one group of typical flow regime map;

Fig. 4 a～b is two kinds of image reconstruction field domain subdivision graphs;

Fig. 5 is a flow pattern identification block diagram;

Fig. 6 is combined image reconstruction algorithm block diagram;

Fig. 7 is voidage measurement result figure.

Fig. 8 is based on capacitance tomography system oil-gas two-phase flow measurement mechanism synoptic diagram;

Fig. 9 is the capacitive array sensor structural drawing;

Figure 10 is capacitance measurement circuit and sequential chart;

Figure 11 is a capacitance data acquisition module block scheme.

Embodiment

Utilize electrical capacitance tomography can measure the characteristic parameter of the two-phase fluid of forming by medium with differing dielectric constant.For two-phase fluid with differing dielectric constant, when the space distribution of each phase component or concentration (containing rate) when changing, will cause that the specific inductive capacity of two-phase fluid on tube section distributes to change, thereby make potential electrode to electric capacity change.Adopt capacitive array sensor,, measure these capacitance changes, just can reflect the concentration of two-phase fluid and the phase distribution situation on tube section by combination mutually between each electrode.Measured value with these capacitance changes is a data for projection, adopts suitable image reconstruction algorithm, just can reconstruct flow through phase distributed image on the cross-section of pipeline of a certain moment two-phase fluid, realizes the visual of flow pattern.By technology such as Flame Image Process reconstructed image is further analyzed, can be drawn the differentiation result of flow pattern and the voidage of two-phase fluid.

Image reconstruction is the core link of capacitance tomography system, its essence is to find the solution following image reconstruction model:

P=WF wherein, P=[p ₁, p ₂..., p _N] ^TFor measuring electric capacity data for projection vector, its element p _iMeasurement capacitance and the sensitivity profile thereof of value after by normalized determined.

$p_i=(\underset{D}{\∫}\∫S_i(x,y)\mathrm{dxdy})\·C{r}_i$

In the formula, Cr _iNormalized capacitance, computing method are:

${\mathrm{Cr}}_i=\frac{C_i-C_{\mathrm{oi}}}{C_m-C_{\mathrm{oi}}}$

C _iBe fluid-mixing measurement capacitance, C _OiMeasurement capacitance when being full of gas phase in the pipe, C _MiMeasurement capacitance when being full of oil phase in the pipe.S _i(x y) is i the sensitivity profile function that potential electrode is right, and the value of this function on each pixel obtains by the finite element analysis of electromagnetic field.W is the weight coefficient matrix, its element w _Ij, i=1,2 ..., N, j=1,2 ..., M is determined by the sensitivity on j pixel of i measurement electric capacity and the area of this pixel.F=[f ₁, f ₂..., f _M] ^TFor waiting to ask the gradation of image vector, element f wherein _i∈ [0,1], the medium on 0 this pixel of expression is a gas phase, the medium on 1 this pixel of expression is an oil phase.N represents the measurement data number, and the M presentation video is rebuild the pixel subdivision number of field domain.

Image reconstruction algorithm choose real-time and picture quality important influence to image reconstruction, according to the different requirements to image reconstruction speed and quality of various application scenarios, can adopt different image reconstruction algorithms.

As shown in Figure 1, at the characteristics and the application requirements of flow pattern, flow pattern shows the monitoring that is mainly used in the two-phase flow industrial system, and the image that is used for the flow pattern demonstration thus need adopt back projection's image reconstruction algorithm (LBP) of simple and fast.Flow pattern is recognized as a qualitative parameter measurement, therefore still adopts back projection's image reconstruction algorithm, through Flame Image Process, utilizes the Fuzzy Pattern Recognition technology to carry out the flow pattern identification.And voidage is a quantitative parameter, reconstructed image quality is high more, be of value to the raising measuring accuracy more, so adopt combined novel image reconstruction algorithm to reconstruct the higher dielectric distribution image of quality based on Tikhonov regularization and algebraic reconstruction technique (ART), utilize image processing techniques computed image gray scale again, thereby obtain the voidage of oil-gas two-phase flow.

Shown in Fig. 4 a, in the image reconstruction process of flow pattern demonstration and flow pattern identification, adopt the finite element triangulation that the image reconstruction field domain is split into 54 pixels, adopt backprojection algorithm to carry out image reconstruction.After reconstruction is finished, corresponding for making the dielectric distribution image with digital picture, through interpolation, technical finesse such as level and smooth, image transitions become 32 * 32 grid plan image distribute with the two-phase fluid that characterizes on the tube section.Simultaneously, choose one group of pixel on vertical diameter of all cross-sectional images in 3 seconds, show successively, can be characterized in the dielectric distribution image on the longshore current body flow direction in this time period.

The characteristics of backprojection algorithm are that calculated amount is less, and speed is fast, and the image that reconstructs also can satisfy the application requirements of online demonstration of flow pattern and qualitative flow pattern identification, and its computing formula is shown below:

$f_i=\frac{\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{Cr}}_i\·W_{i,j}}{\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{W}_{i,j}}$

In the formula, Cr _iNormalized capacitance.

According to the two-phase flow dielectric distribution image of rebuilding, adopt fuzzy pattern recognition method to carry out the flow pattern identification, its way of thinking is described below:

1. define domain U={A ₁, A ₂, A ₃, A wherein ₁={ homogeneous flow }, A ₂={ laminar flow }, A ₃={ annular flow }.As shown in Figure 2, typical flow pattern comprises in the moving system of oil-gas two-phase flow: be full of in laminar flow, annular flow, wave flow, slug flow, bubble flow and the pipe and be full of gas phase (being defined as blank pipe) in oil phase (being defined as full packages) and the pipe.Bubble flow, full packages and blank pipe can be summed up as homogeneous flow; Wave flow can be thought to be made up of the laminar flow of different floor heights; Slug flow can be thought to be made up of homogeneous flow and laminar flow.Therefore set three mode standard A ₁={ homogeneous flow }, A ₂={ laminar flow }, A ₃={ annular flow }.

2. define the characteristic quantity X={x} relevant with U, randomness and fuzzy behaviour according to flow pattern, and consider the characteristic distributions of medium on pipeline section under the typical flow patterns such as homogeneous flow, laminar flow, annular flow, following three statistical natures of the dielectric distribution image of rebuilding are differentiated variable as Fuzzy: the average gray value x of (1) entire image ₁(2) be positioned at the average gray of 27 pixels of first pipeline and the absolute value x of the difference of the average gray that is positioned at 27 pixels of second pipeline ₂(3) near the average gray of 30 pixels of tube wall one circle and the absolute value x of the difference of the average gray that is positioned at 24 pixels of tube hub ₃

3. choosing normal state Fuzzy distribution function is the version of membership function, and then flow pattern is differentiated multifactor Fuzzy Pattern Recognition problem of the question resolves itself into.At this moment, each mode standard and three corresponding membership function collection of Fuzzy characteristic quantity are:

$A_1:{\mathrm{\μ}}_1=({\mathrm{\μ}}_1^1,{\mathrm{\μ}}_1^2,{\mathrm{\μ}}_1^3)=(e^{-{\left(\frac{x_1-{\mathrm{\β}}_{11}}{{\mathrm{\σ}}_{11}}\right)}^2},e^{-{\left(\frac{x_2-{\mathrm{\β}}_{12}}{{\mathrm{\σ}}_{12}}\right)}^2},e^{-{\left(\frac{x_3-{\mathrm{\β}}_{13}}{{\mathrm{\σ}}_{13}}\right)}^2})$

$A_2:{\mathrm{\μ}}_2=({\mathrm{\μ}}_2^1,{\mathrm{\μ}}_2^2,{\mathrm{\μ}}_2^3)=(e^{-{\left(\frac{x_1-{\mathrm{\β}}_{21}}{{\mathrm{\σ}}_{21}}\right)}^2},e^{-{\left(\frac{x_2-{\mathrm{\β}}_{22}}{{\mathrm{\σ}}_{22}}\right)}^2},e^{-{\left(\frac{x_3-{\mathrm{\β}}_{23}}{{\mathrm{\σ}}_{23}}\right)}^2})$

$A_3:{\mathrm{\μ}}_3=({\mathrm{\μ}}_3^1,{\mathrm{\μ}}_3^2,{\mathrm{\μ}}_3^3)=(e^{-{\left(\frac{x_1-{\mathrm{\β}}_{31}}{{\mathrm{\σ}}_{31}}\right)}^2},e^{-{\left(\frac{x_2-{\mathrm{\β}}_{32}}{{{\mathrm{\σ}}_{32}}_{}}\right)}^2},e^{-{\left(\frac{x_3-{\mathrm{\β}}_{33}}{{\mathrm{\σ}}_{33}}\right)}^2})$

Parameter beta wherein _Ij, σ _IjSet by artificial experience; I=1,2,3; J=1,2,3.Obtain to be used for the parameter beta of flow pattern identification by a large amount of experimental studies _Ij, σ _IjAs follows:

$\mathrm{\β}=\left[\begin{array}{ccc}1.59& 0.0& 0.0\\ 0.0& 0.96& 0.0\\ 0.0& 0.0& 0.95\end{array}\right],\mathrm{\σ}=\left[\begin{array}{ccc}0.48& 0.08& 0.08\\ 0.08& 1.0& 0.08\\ 0.08& 0.08& 1.0\end{array}\right]$

Pattern μ to be identified is with respect to mode standard A ₁, A ₂, A ₃Membership function be:

${\mathrm{\&mu;}}_{{\mathrm{<figure-callout\; id="1"\; label="A"\; filenames="A20031010814200172.png,A20031010814200181.png"\; state="\{\{state\}\}">A</figure-callout>}}_1}=M_m\&lsqb;{\mathrm{\&mu;}}_i^1\left(x_1\right),{\mathrm{\&mu;}}_i^2\left(x_2\right),{\mathrm{\&mu;}}_i^3\left(x_3\right)\&rsqb;\&CenterDot;\&CenterDot;\&CenterDot;i=\mathrm{1,2,3}$

M wherein _mBe called comprehensive function, its implication is that comprehensive three Fuzzy features provide an overall evaluation.Get comprehensive function M _mBe the form of mapping ∑, pattern then to be identified is with respect to A ₁, A ₂, A ₃Degree of membership be respectively:

${\mathrm{\&mu;}}_{{\mathrm{<figure-callout\; id="1"\; label="A"\; filenames="A20031010814200172.png,A20031010814200181.png"\; state="\{\{state\}\}">A</figure-callout>}}_1}=\frac{1}{3}(e^{-{\left(\frac{x_1-{\mathrm{\&beta;}}_{11}}{{\mathrm{\&sigma;}}_{11}}\right)}^2}+e^{-{\left(\frac{x_2-{\mathrm{\&beta;}}_{12}}{{\mathrm{\&sigma;}}_{12}}\right)}^2}+e^{-{\left(\frac{x_3-{\mathrm{\&beta;}}_{13}}{{\mathrm{\&sigma;}}_{13}}\right)}^2})$

${\mathrm{\&mu;}}_{{\mathrm{<figure-callout\; id="2"\; label="A"\; filenames="A20031010814200181.png"\; state="\{\{state\}\}">A</figure-callout>}}_2}=\frac{1}{3}(e^{-{\left(\frac{x_1-{\mathrm{\&beta;}}_{21}}{{\mathrm{\&sigma;}}_{21}}\right)}^2}+e^{-{\left(\frac{x_2-{\mathrm{\&beta;}}_{22}}{{\mathrm{\&sigma;}}_{22}}\right)}^2}+e^{-{\left(\frac{x_3-{\mathrm{\&beta;}}_{23}}{{\mathrm{\&sigma;}}_{23}}\right)}^2})$

${\mathrm{\&mu;}}_{{\mathrm{<figure-callout\; id="3"\; label="A"\; filenames="A20031010814200181.png"\; state="\{\{state\}\}">A</figure-callout>}}_3}=\frac{1}{3}(e^{-{{\left(\frac{x_1-{\mathrm{\&beta;}}_{31}}{{\mathrm{\&sigma;}}_{31}}\right)}^2}^{}}+e^{-{\left(\frac{x_2-{\mathrm{\&beta;}}_{32}}{{\mathrm{\&sigma;}}_{32}}\right)}^2}+e^{-{\left(\frac{x_3-{\mathrm{\&beta;}}_{33}}{{\mathrm{\&sigma;}}_{33}}\right)}^2})$

4. differentiate variable according to Fuzzy and obtain degree of membership value μ _A1, μ _A2, μ _A3, and differentiate current which kind of flow pattern of tube section characterization image according to the maximum membership degree criterion.

Fig. 5 shows flow pattern identification block diagram, and the flow process and the step of concrete flow pattern identification are as follows:

(1) gathers capacitance,, have 66 independent measurement capacitances for 12 electrode capacitance chromatographic imaging systems;

(2) image reconstruction obtains each grey scale pixel value of cross section;

(3) calculate three statistical characteristics x according to the reconstructed image grey scale pixel value ₁, x ₂, x ₃

(4) utilize fuzzy pattern recognition method to carry out pattern-recognition and try to achieve three degree of membership value μ _A1, μ _A2, μ _A3

(5) differentiate flow pattern according to the maximum membership degree criterion, if μ _A1Maximum then is a homogeneous flow, if μ _A2Maximum then is a laminar flow.If μ _A3Maximum then is an annular flow.Annular flow, laminar flow just can obtain by an identification.

(6) if identification result is a homogeneous flow, can be full packages, blank pipe or bubble flow according to the current two-phase flow of the further identification of tube section liquid phase content.If the gray scale of image is then thought full packages near 1 (greater than 0.98); If gradation of image is then thought blank pipe near 0 (less than 0.02); If gradation of image between 0.02 and 0.98, is then thought bubble flow.

(7) wave flow can be thought to be made up of the laminar flow of different floor heights, is under the prerequisite of laminar flow differentiating the result therefore, and the fluctuation of analysis image gray scale if fluctuation reaches certain amplitude, is then thought wave flow.

(8) slug flow can be thought to be made up of laminar flow and homogeneous flow, therefore needs just whether the current flow pattern of further identification is slug flow when identification result is laminar flow or homogeneous flow (blank pipe, full packages, bubble flow).Its strategy is to investigate the flow pattern of this identification and the result of preceding identification several times, if laminar flow alternately occurs with homogeneous flow and certain time interval is arranged (because operating mode is different, slug length is different, so the time interval that is provided with decides according to operating mode), think that then current flow pattern is a slug flow, otherwise keep original flow pattern identification result.Be the demonstration of flow pattern identification result at last and return and carry out flow pattern identification next time.

The experiment that flow pattern shows shows that these capacitance tomography system flow pattern speed of displaying 50 frames/more than second, the image of demonstration conforms to the image of reality.Fig. 3 a～e has provided typical flow pattern in one group of horizontal tube that this gas-liquid two-phase flow measuring apparatus shows.The on-line automatic identification of flow pattern experiment shows that for homogeneous flow, laminar flow, wave flow and annular flow equal flow type, the accuracy rate of its identification is higher than 95%, differentiates the used time of flow pattern less than 0.3 second, is higher than 90% for the identification accuracy rate of slug flow; According to the clock in the computing machine, can calculate the time span (time that continuous homogeneous flow is continued) of slug.

Voidage is a quantization parameter, simple backprojection algorithm can not meet the demands, therefore native system is when measuring voidage, adopted combined novel image reconstruction algorithm, the ill-posedness that this algorithm runs into when utilizing the Tikhonov regularization to overcome the inverse problem of finding the solution the image reconstruction model is also obtained the initial pictures gray-scale value, on the basis of this initial pictures, further use algebraic reconstruction technique and carry out iterative modification, obtain high-quality reconstructed image, obtain the voidage of oil-gas two-phase flow by the computed image gray scale.

Fig. 6 shows this combined image reconstruction algorithm.This algorithm carries out in two steps: the first step, image reconstruction field domain are split into 54 pixels (shown in Fig. 4 a), adopt the Tikhonov regularization to find the solution the gray-scale value of initial cross-section image; Second step was split into 216 pixels (shown in Fig. 4 b) with the image reconstruction field domain, was iterative initial value with the gray-scale value of initial pictures, used the ART algorithm and carried out iterative modification and obtain high-quality dielectric distribution image to reconstruct.

In the image reconstruction of the first step, data for projection (measurement capacitance) has 66, with the finite element triangulation tube section is split into 54 pixels, and image reconstruction is to carry out under the complete situation of data.Because being a discomfort, the image reconstruction problem of capacitance chromatography imaging decides inverse problem.Ill-posedness mainly shows as the instability of separating, and promptly the subtle change of measurement data can cause the very great fluctuation process separated.Therefore the method that overcomes the ill-posedness of ill-posed problem is regularization, adopts the Tikhonov regularization, can overcome ill-posed problem in the image reconstruction by structure regularization generalized inverse image reconstruction algorithm, obtains the gray-scale value of initial cross-section image.Introduce regularization parameter λ＞0, finding the solution of image reconstruction model can be summed up as following optimization problem:

Definition auxiliary function J (F):

J(F)＝‖WF-P‖ ²+λ‖F‖ ²→min

By $\frac{\&PartialD;J}{\&PartialD;F}=0,$ Thereby minimization J can get the regular solution of image reconstruction model through deriving, i.e. the gray scale of initial cross-section image vector estimated value

$\hat{F}={(W^{T}W+\mathrm{\&lambda;I})}^{-1}{W}^{T}P$

λ is rule of thumb default, and general value is about 0.1.

Carry out the second step image reconstruction on this basis, tube section is segmented, be split into 216 pixels,, adopt the ART image reconstruction algorithm, to obtain high-quality image with the gray-scale value of 54 pixels initial value as iteration.

The main iterative formula of ART algorithm is:

$f_j^{\&lsqb;i\&rsqb;}=f_j^{\&lsqb;i-1\&rsqb;}+\&lsqb;(p_i-q_i)/\underset{k=1}{\overset{\mathrm{<figure-callout\; id="2"\; label="M\; w\; ik"\; filenames="A20031010814200181.png"\; state="\{\{state\}\}">M</figure-callout>}}{\mathrm{\&Sigma;}}}{w}_{\mathrm{ik}}^{}{}_2^{}\&rsqb;w_{\mathrm{ij}}$

Wherein

$q_i=\underset{k=1}{\overset{M}{\mathrm{\&Sigma;}}}{f}_k^{\&lsqb;i-1\&rsqb;}{w}_{\mathrm{ik}}$

In iterative process, introduce f _j ^[i]The priori of ∈ [0,1], the iteration result is carried out filtering:

Reconstructed image is for further processing, can calculate the voidage of gas-liquid two-phase fluid because the gray-scale value of each pixel of image of rebuilding is corresponding to liquid phase content in this pixel region,, can gets voidage α and be then according to the final image that obtains:

$\mathrm{\&alpha;}=1-\left(\underset{j=1}{\overset{M}{\mathrm{\&Sigma;}}}{f}_i\frac{A_j}{A}\right)\&times;100\%$

In the formula: A _jBe the area of j pixel, A is the area of section of measuring channel.

With diesel oil and air is that medium has carried out the voidage experiments of measuring, and the static demarcating experimental result shows, the measuring error of voidage can be less than 5% (annotate: the dynamic calibrating method of tight rate still at present, can only carry out static demarcating to it).One group of typical voidage measurement result as shown in Figure 7, horizontal ordinate is the voidage setting value among the figure, the voidage value of ordinate for adopting capacitance tomography system to measure.The real-time that the voidage value is measured is also relatively good, measures to obtain the required time of voidage value less than 0.1 second.

The hardware of measuring system is formed as Fig. 8, shown in Figure 9, device has capacitive array sensor 1 successively, capacitance data collecting unit 2 and the computing machine 3 that is used for image reconstruction, data processing and demonstration, said capacitive array sensor has the insulation tube 4 of joint flange 5 as the sensing pipeline section with two ends, sensing pipeline section outboard shafts is to evenly being pasted with copper foil electrode 9 arrays, the sensing pipeline section outside is provided with fixed support 6, radome 7, radial electrode 8, is provided with electric capacity/voltage transformation module 10 on radial electrode 8.

The material that the sensing pipeline section adopts is the long 500～1000mm of polyethylene pipe (PE pipe), and the tubing specification is PE63, SDR11, nominal diameter 25mm～160mm, nominal pressure 1.0～2.0MPa.Shell is a radome, is made up of semi-circular two stainless steel substrates, and respectively there are 2 screw perforates at two ends, can be fixed on the support.Electrode slice is 12, evenly sticks on the outer wall of sensing pipeline section.The material that electrode adopted is a copper sheet, and the electrode subtended angle is 26 °, and electrode slice length is 1.2 times of internal diameter of the pipeline.Be provided with radial electrode between the electrode, electric capacity/voltage transformation module is installed on the radial electrode, has 12 modules, and each electrode pair is answered a module.

Figure 10 shows electric capacity/voltage module to carry out Small Capacitance Measuring Circuit and measures sequential.V _iBe the excitation voltage source that discharges and recharges, amplifier U ₁, capacitor C _fAnd switch S ₁Constitute charge amplifier; Switch S ₂And S ₃, amplifier U ₂And U ₃Constitute two sampling holders (S/H); U ₄Be instrument amplifier.The course of work of circuit was divided into for two steps.The first step is measuring switch S ₁The electric charge injection effect: before circuit is started working, V _iVoltage is high, switch S ₁Closure, two sampling holders all are in sampling pattern.Because S ₁Closure, U ₁Be output as 0V.At t ₁Constantly with S ₁Disconnect, in the ideal case, V ₁To remain 0V, but because switch S ₁The electric charge injection effect, charge Q is arranged _cBe injected into C _f, cause V ₁Be pulled low to V _LAt t ₂Constantly, U ₁Output tend towards stability, with S ₃Disconnection makes sampling holder U ₃Enter the maintenance pattern, then U ₁Output valve by sampling holder U ₃Keep, i.e. U ₃Output V ₃Equal V _LThe 2nd step was to measure the C that driving source causes _xIn the charge variation amount: at t ₃Constantly, driving source V _iProduce saltus step from high to low, hopping amplitude is Δ V, then obtains induced charge to be on potential electrode:

Q＝-ΔV _iC _x

U ₁Be output as:

$V_H=V_L+\frac{Q}{C_f}$

At t ₄Constantly, S ₂Disconnection makes sampling holder U ₂Enter the maintenance pattern, i.e. U ₂Output V ₂Equal V _HWith sampling holder U ₂Output V _HWith sampling holder U ₃Output V _LAs instrument amplifier U ₄Input, then instrument amplifier is output as:

${\mathrm{<figure-callout\; id="4"\; label="V"\; filenames="A20031010814200182.png"\; state="\{\{state\}\}">V</figure-callout>}}_4=V_H-V_L=\frac{-\mathrm{\&Delta;}{V}_i{C}_x}{C_f}$

This value is proportional with measured capacitance, can characterize the size of measured capacitance.

Shown in Figure 11 is the block scheme of capacitance data acquisition module, in a measuring period, at first be that microprocessor sends channel control signals, be used to control that a certain electrode is in foment and remaining electrode is in detected state, its excitation with testing mechanism is: select electrode 1 to be exciting electrode, electrode 2～electrode 12 is a detecting electrode, potential electrode 1 and electrode 2 successively, electrode 1 and electrode 3 ..., the capacitance between electrode 1 and the electrode 12; Select electrode 2 to be exciting electrode then, electrode 3～electrode 12 be a detecting electrode, potential electrode 2 and electrode 3 successively, and electrode 2 and electrode 4 ..., the capacitance between electrode 2 and the electrode 12; By that analogy, the capacitance between last potential electrode 11 and the electrode 12 is finished one-shot measurement.The capacitance that sensor is measured is converted to magnitude of voltage after C/V module (electric capacity/voltage transformation module), the pairing magnitude of voltage of electric capacity (these values obtain in system initialization and are stored in the microprocessor) when cutting blank pipe, obtain characterizing the voltage increment of capacitance change in the pipeline, again after programmable-gain amplifier (its gain is disposed by microprocessor according to different electrode pairs) amplifies signal, give A/D converter and carry out analog to digital conversion, be sent to the computing machine of image reconstruction and flow pattern demonstration again by microprocessor through communication unit.By communication unit, instrument can adopt RS-232 or RS-485 and computing machine to carry out communication.If adopt RS-232 to carry out communication, the serial port of instrument and computing machine need be coupled together, the transmission range under this mode is 15 meters, maximum data transfer rate is 115.2KBps.If adopt RS-485 to carry out communication, require computing machine to be furnished with the RS-232/RS-485 converter, the transmission range under this mode is 1200 meters, maximum data transfer rate is 921.6KBps.

Computing machine based on image reconstruction, data processing and the demonstration of the oil-gas two-phase flow measurement mechanism of 12 electrode capacitance chromatographic imaging systems adopts the PC-104 bus computer, its processor is Pentium MMX300MHz, internal memory 128MByte, the mainboard model is PCM3350, and display adopts 9 inches TFT display screen.

Claims (8)

1. the oil-gas two-phase flow measuring method based on capacitance tomography system is characterized in that: 1) adopt backprojection algorithm to reconstruct oil-gas two-phase flow dielectric distribution image, realize the online demonstration of flow pattern; 2) adopt Fuzzy Pattern Recognition Method to carry out the on-line automatic identification of flow pattern, can discern homogeneous flow, laminar flow, wave flow, annular flow and slug flow equal flow type, and can calculate the time span of slug according to the clock in the computing machine; 3) employing reconstructs the oil-gas two-phase flow cross-sectional image, and calculates the voidage of oil-gas two-phase flow according to the gray-scale value of image based on the combined novel image reconstruction algorithm of Tikhonov regularization theory and algebraic reconstruction technique.

2. a kind of oil-gas two-phase flow measuring method according to claim 1 based on capacitance tomography system, it is characterized in that said employing backprojection algorithm carries out image reconstruction, realize the real-time demonstration of oil-gas two-phase flow flow pattern in the pipeline, display speed 50 frames/more than second.

3. a kind of oil-gas two-phase flow measuring method according to claim 1 based on capacitance tomography system, it is characterized in that said employing Fuzzy Pattern Recognition Method carries out the on-line automatic identification of flow pattern, the membership function that is adopted is a normal state Fuzzy distribution function, discern homogeneous flow, laminar flow, wave flow, annular flow and slug flow equal flow type according to the maximum membership degree criterion, according to the clock in the computing machine, calculate the time span of slug simultaneously.

4. a kind of oil-gas two-phase flow measuring method according to claim 1 based on capacitance tomography system, it is characterized in that the combined novel image reconstruction algorithm of said employing based on Tikhonov regularization and algebraic reconstruction technique, this algorithm carries out in two steps: the first step, the image reconstruction field domain is split into 54 pixels, adopts the Tikhonov regularization to find the solution the original image intensity profile; Second step, the image reconstruction field domain is split into 216 pixels, is iterative initial value with the original image intensity profile, uses algebraic reconstruction technique and carries out iterative modification, reconstruct the oil-gas two-phase flow cross-sectional image, calculate the voidage of oil-gas two-phase flow according to the gray-scale value of image.

5. oil-gas two-phase flow measurement mechanism based on capacitance tomography system, it is characterized in that it has capacitive array sensor (1) successively, capacitance data collecting unit (2) and be used for image reconstruction, the computing machine of data processing and demonstration (3), said capacitive array sensor has the insulation tube (4) of joint flange (5) as the sensing pipeline section with two ends, sensing pipeline section outboard shafts is to evenly being pasted with copper foil electrode (9) array, the sensing pipeline section outside is provided with fixed support (6), radome (7), radial electrode (8) is provided with electric capacity/voltage transformation module (10) on radial electrode (8).

6. a kind of oil-gas two-phase flow measurement mechanism according to claim 5 based on capacitance tomography system, it is characterized in that said sensing pipeline section adopts polyethylene pipe, polyethylene pipe length is 500～1000mm, nominal diameter 25mm～160mm, nominal pressure 1.0～2.0MPa.

7. a kind of oil-gas two-phase flow measurement mechanism based on capacitance tomography system according to claim 5 is characterized in that said copper foil electrode is 12, and material is a copper sheet, and the electrode subtended angle is 26 °, and electrode slice length is 1.2 times of internal diameter of the pipeline.

8. a kind of oil-gas two-phase flow measurement mechanism based on capacitance tomography system according to claim 5 is characterized in that said capacitance data collecting unit adopts RS-232 or RS-485 to carry out communication.

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