基于电容耦合式非接触电导检测的气液两相流参数测量新方法研究
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摘要
气液两相流系统在石油、化工、能源、制药以及电力等工业领域中广泛存在,其流型和相含率的在线测量对两相流系统的运行监控、过程控制及安全保证等具有十分重要的意义。由于两相流动的复杂性和随机性,气液两相流的流型辨识和相含率测量一直以来是科学研究和工业应用领域中长期未能得以很好解决的测量难题。电容耦合式非接触电导检测(Capacitively Coupled Contactless Conductivity Detection,简写为C4D)是一种新型的电导检测技术,有望为气液两相流参数测量提供一条新的解决途径。本学位论文拟研究C4D技术应用于气液两相流流型辨识和相含率测量的可行性,并基于C4D技术,结合现代信息处理技术,提出气液两相流流型辨识和相含率测量的新方法。
本学位论文的主要创新点和贡献如下：
1、基于串联谐振的原理,研发了一种适用于气液两相流参数测量的新型C4D传感器。研究结果表明,所研发的的基于串联谐振的单屏蔽C4D传感器是成功的。基于串联谐振的原理消除了耦合电容对电导测量的不利影响,改善了传统C4D传感器的电导测量范围和分辨率,将C4D的管径适用范围从毛细管尺寸扩展至毫米级尺寸(本文所研制的新型C4D传感器内径尺寸最大已达7.8mm)。研究结果同时表明,所研发的新型C4D传感器在较大管径下的电导率测量精度令人满意。在内径分别为1.8mm、3.3mm、5.5mm、7.8mm四种管径下,电导率测量的最大相对误差均小于3.5%。所研发的新型C4D传感器具有测量范围广,分辨率好,结构简单,稳定性和抗干扰性能佳等优点,为基于C4D的气液两相流参数测量奠定了有效的技术基础。
2、利用C4D传感器获得的电导信号,结合现代信息处理技术,研究了气液两相流流型辨识问题。根据特征提取方法的不同,分别提出并对比了两种流型辨识新方法：1)采用统计分析和傅里叶分析方法对所获得的电导信号进行特征提取,然后将提取的特征输入到由SVM建立的流型分类器中实现流型辨识；2)采用统计分析和小波分析方法对所获得的电导信号进行特征提取,然后将提取的特征输入到由SVM建立的流型分类器中实现流型辨识。研究结果表明,C4D技术应用于气液两相流流型辨识是可行的,所提出的两种流型辨识新方法均是有效的。在内径分别为1.8mm、2.8mm、4.0mm、6.1mm和7.8mm水平管下,层状流、波状流、泡状流、段塞流和环状流的辨识结果令人满意。其中,采用统计分析、小波分析和SVM相结合的流型辨识新方法的辨识准确度更好(采用统计分析、小波分析与SVM相结合的流型辨识准确率均高于91%,采用统计分析、傅里叶分析与SVM相结合的流型辨识准确率均高于87%)。研究结果同时表明,将特征提取和SVM分类技术相结合进行气液两相流流型辨识是有效的。所提取的均值、标准差和能量分布特征可有效反映流型的信息,采用SVM分类技术能够成功实现多种流型的分类问题。
3、利用C4D传感器所获电导信号,并结合SVM回归技术,提出了气液两相流相含率测量新方法。该方法为克服流型对测量结果的影响,需根据气液两相流的不同典型流型分别建立相含率测量模型。在相含率测量过程中,预先利用SVM回归技术建立各典型流型下的测量模型。实际测量时,首先利用所研发的新型C4D传感器获取气液两相流电导信号,然后进行流型辨识并获得当前流型,最后根据流型辨识结果选择相应的相含率测量模型计算相含率值。研究结果表明,C4D技术应用于气液两相流相含率测量是可行的,所提出的相含率测量新方法是有效的。在内径为7.0mm水平管下,层(波)状流、泡状流、段塞流和环状流典型流型下的气相含率测量的最大绝对误差均不高于7.0%。研究结果同时表明,利用特征提取和SVM回归技术建立的气液两相流相含率测量模型是成功的。所提取的气液两相流电导均值和标准差两个特征参数能有效反映相含率的信息,采用SVM回归技术能有效克服电导信号与相含率之间的非线性关系,成功建立了气液两相流的相含率测量模型。
本学位论文的研究证实了C4D技术可为两相流的参数测量提供一种新的解决途径,同时也为C4D这一新技术在工程应用领域的进一步拓展提供了有益的借鉴。
Gas-liquid two-phase flow widely exists in many industries, such as chemical, pharmaceutical, petroleum, energy and power engineering, etc. Online measurement of flow pattern and void fraction are very important for operation monitoring, process controlling and safety assurance of gas-liquid two phase flow system. Due to the complexity of two-phase flow, flow pattern identification and void fraction measurement are two difficult problems which have not been well solved both in scientific research field and industrial application. Capacitively Coupled Contactless Conductivity Detection (C4D) is a new conductivity detection technique, which may provide a promising way for parameter measurement of gas-liquid two-phase flow. This dissertation aims to study the feasibility of applying C4D to flow pattern identification and void fraction measurement of gas-liquid two-phase flow, and then based on C4D and combine with modern information processing technique, to propose new methods for flow pattern identification and void fraction measurement of gas-liquid two-phase flow.
The main innovation points and contributions of this dissertation are listed as follows:
1. Based on series resonance principle, a new C4D sensor which is suitable for parameter measurement of gas-liquid two-phase flow is developed. Research results show that:based on series resonance principle, the new C4D sensor overcome the unfavourable influence of the coupled capacitances on conductivity detection, improves the detection range and resolution, and expands the application of C4D to conductivity detection in millimeter-scale pipes (the maximum inner diameter of the new C4D sensor is7.8mm, while conventional C4D technique are mainly used in capillaries). Research results also show that the conductivity detection accuracies of the new C4D sensor in larger pipes are satisfactory. In four pipes (the inner diameters are1.8mm,3.3mm.5.5mm and7.8mm, respectively), the maximum relative errors of conductivity detection are all less than3.5%. The new C4D sensor has the advantages of wide detection range, high resolution, simple construction, good stability and anti-interference ability, and provides an effective mean for parameter measurement of gas-liquid two-phase flow.
2. Based on the conductivity signals obtained by the new C4D sensor, flow pattern identification of gas-liquid two-phase flow are studied combining with the modern information processing technique. According to different feature extraction methods, two new flow pattern identification methods are proposed and compared:1) use statistical analysis and Fourier analysis methods to extract the features of conductivity signals, then input the extracted features to the flow pattern classifier (which is built by SVM technique) for classification, finally the flow patterns are obtained.2) use statistical analysis and Wavelet analysis methods to extract the features of conductivity signals, then input the extracted features to the flow pattern classifier (which is built by SVM technique) for classification, finally the flow patterns are obtained. Research results show that applying C4D to flow pattern identification of gas-liquid two-phase flow is feasible, and the proposed two flow pattern identification methods are both effective. In five pipes with the inner diameters of1.8mm,2.8mm,4.0mm,6.1mm and7.8mm, respectively, the identification results for five typical flow patterns are all satisfactory. The flow patten identification method based on the combination of statistical analysis, Wavelet analysis and SVM techniques are better (The identification results using the combination of statistical analysis. Wavelet analysis and SVM techniques are all above91%, while the identification results using the combination of statistical analysis, Fourier analysis and SVM techniques are only above87%.). Research results also show that combining the feature extraction and the SVM technique is effective for flow pattern identification. The extracted features can effectively reflect the information of flow pattern, and SVM technique can succeffully implement the multi-classification of flow patterns.
3. Based on the conductivity signals obtained by the new C4D sensor, a new method for void fraction measurement of gas-liquid two-phase flow is proposed combine with the SVM regression technique. This method develops the void fraction measurement model for each typical flow pattern on the basis of SVM regression technique. In practical measurement process, firstly, the conductivity signals of gas-liquid two-phase flow are obtained by the developed C4D sensor, then, the flow pattern of gas-liquid two-phase flow are identified, finally, according to the identification results, the suitable void fraction measurement model is selected to calculate the void fraction value. Research results show that applying C4D to void fraction measurement of gas-liquid two-phase flow is feasible, and the proposed void fraction measurement method is effective. In the pipe with the inner diameter of7.0mm, the void fraction measurement results are satisfactory. For stratified flow (wavy flow), bubble flow, slug flow and annular flow, the maximum measurement errors are all less than7.0%. Research results also show that using the combination of feature extraction and SVM regression technique to develop the void fraction measurement model is successful. The extracted features (the mean value and the standard deviation) of conductivity signals can effectively reflect the information of void fraction, and the SVM regression technique can overcome the nonlinearity between the conductivity signals and the void fraction, and can successfully develop the void fraction measurement models.
本学位论文的主要创新点和贡献如下：
1、基于串联谐振的原理,研发了一种适用于气液两相流参数测量的新型C4D传感器。研究结果表明,所研发的的基于串联谐振的单屏蔽C4D传感器是成功的。基于串联谐振的原理消除了耦合电容对电导测量的不利影响,改善了传统C4D传感器的电导测量范围和分辨率,将C4D的管径适用范围从毛细管尺寸扩展至毫米级尺寸(本文所研制的新型C4D传感器内径尺寸最大已达7.8mm)。研究结果同时表明,所研发的新型C4D传感器在较大管径下的电导率测量精度令人满意。在内径分别为1.8mm、3.3mm、5.5mm、7.8mm四种管径下,电导率测量的最大相对误差均小于3.5%。所研发的新型C4D传感器具有测量范围广,分辨率好,结构简单,稳定性和抗干扰性能佳等优点,为基于C4D的气液两相流参数测量奠定了有效的技术基础。
2、利用C4D传感器获得的电导信号,结合现代信息处理技术,研究了气液两相流流型辨识问题。根据特征提取方法的不同,分别提出并对比了两种流型辨识新方法：1)采用统计分析和傅里叶分析方法对所获得的电导信号进行特征提取,然后将提取的特征输入到由SVM建立的流型分类器中实现流型辨识；2)采用统计分析和小波分析方法对所获得的电导信号进行特征提取,然后将提取的特征输入到由SVM建立的流型分类器中实现流型辨识。研究结果表明,C4D技术应用于气液两相流流型辨识是可行的,所提出的两种流型辨识新方法均是有效的。在内径分别为1.8mm、2.8mm、4.0mm、6.1mm和7.8mm水平管下,层状流、波状流、泡状流、段塞流和环状流的辨识结果令人满意。其中,采用统计分析、小波分析和SVM相结合的流型辨识新方法的辨识准确度更好(采用统计分析、小波分析与SVM相结合的流型辨识准确率均高于91%,采用统计分析、傅里叶分析与SVM相结合的流型辨识准确率均高于87%)。研究结果同时表明,将特征提取和SVM分类技术相结合进行气液两相流流型辨识是有效的。所提取的均值、标准差和能量分布特征可有效反映流型的信息,采用SVM分类技术能够成功实现多种流型的分类问题。
3、利用C4D传感器所获电导信号,并结合SVM回归技术,提出了气液两相流相含率测量新方法。该方法为克服流型对测量结果的影响,需根据气液两相流的不同典型流型分别建立相含率测量模型。在相含率测量过程中,预先利用SVM回归技术建立各典型流型下的测量模型。实际测量时,首先利用所研发的新型C4D传感器获取气液两相流电导信号,然后进行流型辨识并获得当前流型,最后根据流型辨识结果选择相应的相含率测量模型计算相含率值。研究结果表明,C4D技术应用于气液两相流相含率测量是可行的,所提出的相含率测量新方法是有效的。在内径为7.0mm水平管下,层(波)状流、泡状流、段塞流和环状流典型流型下的气相含率测量的最大绝对误差均不高于7.0%。研究结果同时表明,利用特征提取和SVM回归技术建立的气液两相流相含率测量模型是成功的。所提取的气液两相流电导均值和标准差两个特征参数能有效反映相含率的信息,采用SVM回归技术能有效克服电导信号与相含率之间的非线性关系,成功建立了气液两相流的相含率测量模型。
本学位论文的研究证实了C4D技术可为两相流的参数测量提供一种新的解决途径,同时也为C4D这一新技术在工程应用领域的进一步拓展提供了有益的借鉴。
Gas-liquid two-phase flow widely exists in many industries, such as chemical, pharmaceutical, petroleum, energy and power engineering, etc. Online measurement of flow pattern and void fraction are very important for operation monitoring, process controlling and safety assurance of gas-liquid two phase flow system. Due to the complexity of two-phase flow, flow pattern identification and void fraction measurement are two difficult problems which have not been well solved both in scientific research field and industrial application. Capacitively Coupled Contactless Conductivity Detection (C4D) is a new conductivity detection technique, which may provide a promising way for parameter measurement of gas-liquid two-phase flow. This dissertation aims to study the feasibility of applying C4D to flow pattern identification and void fraction measurement of gas-liquid two-phase flow, and then based on C4D and combine with modern information processing technique, to propose new methods for flow pattern identification and void fraction measurement of gas-liquid two-phase flow.
The main innovation points and contributions of this dissertation are listed as follows:
1. Based on series resonance principle, a new C4D sensor which is suitable for parameter measurement of gas-liquid two-phase flow is developed. Research results show that:based on series resonance principle, the new C4D sensor overcome the unfavourable influence of the coupled capacitances on conductivity detection, improves the detection range and resolution, and expands the application of C4D to conductivity detection in millimeter-scale pipes (the maximum inner diameter of the new C4D sensor is7.8mm, while conventional C4D technique are mainly used in capillaries). Research results also show that the conductivity detection accuracies of the new C4D sensor in larger pipes are satisfactory. In four pipes (the inner diameters are1.8mm,3.3mm.5.5mm and7.8mm, respectively), the maximum relative errors of conductivity detection are all less than3.5%. The new C4D sensor has the advantages of wide detection range, high resolution, simple construction, good stability and anti-interference ability, and provides an effective mean for parameter measurement of gas-liquid two-phase flow.
2. Based on the conductivity signals obtained by the new C4D sensor, flow pattern identification of gas-liquid two-phase flow are studied combining with the modern information processing technique. According to different feature extraction methods, two new flow pattern identification methods are proposed and compared:1) use statistical analysis and Fourier analysis methods to extract the features of conductivity signals, then input the extracted features to the flow pattern classifier (which is built by SVM technique) for classification, finally the flow patterns are obtained.2) use statistical analysis and Wavelet analysis methods to extract the features of conductivity signals, then input the extracted features to the flow pattern classifier (which is built by SVM technique) for classification, finally the flow patterns are obtained. Research results show that applying C4D to flow pattern identification of gas-liquid two-phase flow is feasible, and the proposed two flow pattern identification methods are both effective. In five pipes with the inner diameters of1.8mm,2.8mm,4.0mm,6.1mm and7.8mm, respectively, the identification results for five typical flow patterns are all satisfactory. The flow patten identification method based on the combination of statistical analysis, Wavelet analysis and SVM techniques are better (The identification results using the combination of statistical analysis. Wavelet analysis and SVM techniques are all above91%, while the identification results using the combination of statistical analysis, Fourier analysis and SVM techniques are only above87%.). Research results also show that combining the feature extraction and the SVM technique is effective for flow pattern identification. The extracted features can effectively reflect the information of flow pattern, and SVM technique can succeffully implement the multi-classification of flow patterns.
3. Based on the conductivity signals obtained by the new C4D sensor, a new method for void fraction measurement of gas-liquid two-phase flow is proposed combine with the SVM regression technique. This method develops the void fraction measurement model for each typical flow pattern on the basis of SVM regression technique. In practical measurement process, firstly, the conductivity signals of gas-liquid two-phase flow are obtained by the developed C4D sensor, then, the flow pattern of gas-liquid two-phase flow are identified, finally, according to the identification results, the suitable void fraction measurement model is selected to calculate the void fraction value. Research results show that applying C4D to void fraction measurement of gas-liquid two-phase flow is feasible, and the proposed void fraction measurement method is effective. In the pipe with the inner diameter of7.0mm, the void fraction measurement results are satisfactory. For stratified flow (wavy flow), bubble flow, slug flow and annular flow, the maximum measurement errors are all less than7.0%. Research results also show that using the combination of feature extraction and SVM regression technique to develop the void fraction measurement model is successful. The extracted features (the mean value and the standard deviation) of conductivity signals can effectively reflect the information of void fraction, and the SVM regression technique can overcome the nonlinearity between the conductivity signals and the void fraction, and can successfully develop the void fraction measurement models.
引文
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