槽式孔板气液两相流差压信号的时间序列分析
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摘要
基于自行研制的凝析天然气流量计样机和大量的实验数据,本文应用时间序列分析方法对气液两相流的差压波动信号进行处理,分析时序模型参数随气液分相流量、气液两相流流型变化的规律,应用神经网络来映射它们之间的复杂关系,为流量计量算法的开发进行了有益的尝试。
本文的主要工作和结论如下:
(1)提出了一种新的信号处理方法,即应用小波变换模极大值滤波方法对差压测量信号去噪,然后对滤波结果进行经验模式分解获得经验模式函数(IMF),这种信号处理方法能够使具有非平稳特性的差压信号满足时间序列分析的建模条件。
(2)对IMF建立自回归(AR)模型,详细分析了AR模型的各个参数与气液两相流流型以及分相流量之间的关系;AR模型的参数可以有效地区分气液两相流流型,并与气液分相流量的变化密切相关,为应用神经网络映射模型参数与分相流量之间的非线性关系奠定了基础。
(3)以气液分相流量作为神经网络的输出,将IMF的AR模型参数进行多种组合并分别作为输入,采用遗传算法(GA)优化BP神经网络的初始权值,比较了在不同参数组合下GA-BP网络与BP网络的性能;实验结果表明:在同样的输入条件下,GA-BP网络的精度高于BP网络;对于最佳的输入参数组合,在90%的置信概率下,当液相流量大于0.75m3/h时,GA-BP网络预报气液分相流量的相对误差分别低于10%和15%。
Based on a self developed prototype of wet gas meter and experimental data set, a series of works have been carried out. In this thesis, time series analysis techniques were used to process the differential pressure of slotted orifice for gas-liquid two-phase flow. The correlations between the parameters of time series model and gas and liquid flow rates were investigated and mapped by neural network. The main works are listed as follows:
(1) A novel signal processing method was put forward. Firstly, the differential pressure signal was de-noised by wavelet transform using the modulus maximum method; and then de-noised signal was decomposed into a finite number of intrinsic mode functions (IMF) by empirical mode decomposition. The proposed method is a powerful tool for turning the non-stationary original differential pressure into a set of stationary time series, which satisfy the conditions of time series analysis.
(2) Autoregressive (AR) models were built with different IMFs. Correlations among AR model parameters, flow regimes and individual flow rates were analyzed in detail. The model parameters can not only distinguish flow regimes but also have close correlation with the variation of gas and liquid flow rates. The results are fundamentally important for the individual flow rates measurement by neural network.
(3) The neural network was used to map the relationship between the AR model parameters and gas liquid flow rate. The genetic algorithm (GA) was employed to optimize BP network’s initial weights, and then performances of the BP and GA-BP network were compared under different model parameter combinations. The results show that the GA-BP network is more accurate than the BP network with the same inputs. Relative errors of the GA-BP network are within 10% and 15% for gas and liquid flow rates respectively at 90% confidence level when the liquid flow rate is more than 0.75m3/h.
本文的主要工作和结论如下:
(1)提出了一种新的信号处理方法,即应用小波变换模极大值滤波方法对差压测量信号去噪,然后对滤波结果进行经验模式分解获得经验模式函数(IMF),这种信号处理方法能够使具有非平稳特性的差压信号满足时间序列分析的建模条件。
(2)对IMF建立自回归(AR)模型,详细分析了AR模型的各个参数与气液两相流流型以及分相流量之间的关系;AR模型的参数可以有效地区分气液两相流流型,并与气液分相流量的变化密切相关,为应用神经网络映射模型参数与分相流量之间的非线性关系奠定了基础。
(3)以气液分相流量作为神经网络的输出,将IMF的AR模型参数进行多种组合并分别作为输入,采用遗传算法(GA)优化BP神经网络的初始权值,比较了在不同参数组合下GA-BP网络与BP网络的性能;实验结果表明:在同样的输入条件下,GA-BP网络的精度高于BP网络;对于最佳的输入参数组合,在90%的置信概率下,当液相流量大于0.75m3/h时,GA-BP网络预报气液分相流量的相对误差分别低于10%和15%。
Based on a self developed prototype of wet gas meter and experimental data set, a series of works have been carried out. In this thesis, time series analysis techniques were used to process the differential pressure of slotted orifice for gas-liquid two-phase flow. The correlations between the parameters of time series model and gas and liquid flow rates were investigated and mapped by neural network. The main works are listed as follows:
(1) A novel signal processing method was put forward. Firstly, the differential pressure signal was de-noised by wavelet transform using the modulus maximum method; and then de-noised signal was decomposed into a finite number of intrinsic mode functions (IMF) by empirical mode decomposition. The proposed method is a powerful tool for turning the non-stationary original differential pressure into a set of stationary time series, which satisfy the conditions of time series analysis.
(2) Autoregressive (AR) models were built with different IMFs. Correlations among AR model parameters, flow regimes and individual flow rates were analyzed in detail. The model parameters can not only distinguish flow regimes but also have close correlation with the variation of gas and liquid flow rates. The results are fundamentally important for the individual flow rates measurement by neural network.
(3) The neural network was used to map the relationship between the AR model parameters and gas liquid flow rate. The genetic algorithm (GA) was employed to optimize BP network’s initial weights, and then performances of the BP and GA-BP network were compared under different model parameter combinations. The results show that the GA-BP network is more accurate than the BP network with the same inputs. Relative errors of the GA-BP network are within 10% and 15% for gas and liquid flow rates respectively at 90% confidence level when the liquid flow rate is more than 0.75m3/h.
引文
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[38]耿艳峰,冯叔初,郑金吾.槽式孔板的气液两相压降倍率特性[J].化工学报,2006, 57(5):1138-1142
[39] Geng Yanfeng, Zheng Jinwu, Shi Tianming. Study on the Metering Characteristics of a Slotted Orifice for Wet Gas Flow[J]. Flow Measurement and Instrumentation, 2006, 17(2): 123-128
[40]郑金吾,耿艳峰.海上含液天然气流量计开发[J].化工自动化及仪表,2006, 33(3): 74-77
[41] Geng Yanfeng, Zheng Jinwu, Shi Tianming, et al. Wet Gas Meter Development Based on Slotted Orifice Couple and Neural Network Techniques[J]. Chinese Journal of Chemical Engineering, 2007, 15(2):281-285
[42]何利民,郭烈锦,陈学俊.测量水平管道液塞速度和长度的差压波动分析法[J].化工学报,2003,54(2):192-198
[43]邢兰昌,耿艳峰,石岗.槽式孔板的气液两相流测量特性[J].传感技术学报,2006, 19(3):771-775
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[2]曹学文,林宗虎,耿艳峰,等.在线多相流量计测量技术研究[J].中国海上油气(工程),2002,14(2):37-40
[3]耿艳峰,冯叔初,郑金吾,等.凝析天然气计量技术[J].自动化仪表,2005,26(8):1-3
[4]李海青.多相流测试技术现状及趋势[A].多相流检测技术进展[C].北京:石油工业出版社,1996
[5]陈珙,王保良,杨江,等.基于小波分析的气液两相流流型模糊辨识[J].高校化学工程学报,1999,13(4):303-308
[6]杨靖,郭烈锦.气液两相流压差信号数据的分形插值拟合[J].西安交通大学学报, 2002, 36 (9): 921-924
[7]白博峰,郭烈锦,陈学俊.空气水两相流压力波动现象非线性分析[J].工程热物理学报,2001,22(3): 359-362
[8] Sun Bin, Zhang Hongjian, Cheng Lu, et al. Flow Regime Identification of Gas-liquid Two-phase Flow Based on HHT [J]. Chinese Journal of Chemical Engineering, 2006, 14 (1): 24-30
[9] Ding Hao, Huang Zhiyao, Song Zhihuan, et al. Hilbert- Huang Transform Based Signal Analysis for the Characterization of Gas-liquid Two-phase Flow [J]. Flow Measurement and Instrumentation, 2007, 18: 37-46
[10] Ding Hao, Wang Baoliang, Huang, Zhiyao, et al. Application of Wavelet Transform and Higher-order Spectrum to Monitor Gas/liquid Two-phase Flow [A]. Proceedings of the World Congress on Intelligent Control and Automation[C], 2006, 2: 5653-5656
[11] Box G., Jenkins G. M., Reinsel G. C.. Time Series Analysis: Forecasting and Control [M]. Beijing: Posts & Telecommunications Press, 2005
[12] Beg N. A., Toral H. Off-site Calibration of a Two-phase Pattern Recognition Flowmeter [J]. International Journal Multiphase Flow, 1993, 19(6): 999-1012
[13]任杰,朱衡君.时间序列分析和参数识别方法确定两相流流态[J].北方交通大学学报, 1998,22(4):83-86
[14]厉燕新,冀海峰,王保良,等.气液两相流差压波动信号的时间序列分析[J].仪器仪表学报,2003, 24(S4):682-684
[15]孔珑.两相流体力学[M].北京:高等教育出版社,2004
[16]林宗虎,王树众,王栋.气液两相流和沸腾传热[M].西安:西安交通大学出版社,2003:1-2
[17]林宗虎.管路内气液两相流特性及其工程应用[M].西安:西安交通大学出版社,1992
[18]林宗虎,李永光,卢家才,等.气液两相流漩涡脱落特性及工程应用[M].北京:化学工业出版社,2001:43-47
[19] Murdock J. W.. Two-phase Flow Measurement with Orifices[J]. Journal of Basic Engineering, 1962, 84 (4):419-433
[20] James R.. Metering Steam-water Two-phase by Sharp-edged Orifices[J]. Proceeding of the Institution of Mechanical Engineers, 1965,180 (23):549-566
[21] Lin Z. H.. Two-phase Flow Measurements with Sharp-edged Orifice[J]. International Journal of Multiphase Flow, 1992, 8(6):683-693
[22]王文然,佟允宪,仲朔平.利用标准锐边孔板测量汽水两相流的实验研究[J].清华大学学报, 1988,28 (S2):74-82
[23]申国强,林宗虎.应用动态法进行气液两相流的双参数测量[J].计量学报,1993, 14(2):140-145
[24]仲朔平,佟允宪,王文然.利用孔板差压噪声测量汽水两相流[J].清华大学学报,1997, 37(5):15-18
[25]李海青等.两相流参数检测及应用[M].杭州:浙江大学出版社,1991
[26]孙涛,张宏建,胡赤鹰.基于模糊逻辑融合算法的气液两相流流型识别方法[J].仪器仪表学报, 2001,22(S3):293-294
[27] Hewitt G.F.. Development and Testing of the Mixmeter Multiphase Flow Meter. Proc. 13th North Sea Flow Measurement, 1995
[28] Wredit T.. Measurement Optically in Homogeneous Droplets by Phase-Doppler- Anemometry. ISMTMF, 1995:405-412
[29]黄志尧,冀海峰,王保良,等.电容层析成像技术在线测量气固流化床空隙率的研究[J].高校化学工程学报,2002,16(5):490-495
[30] Toral H., Cai S., Akartuna E., et al. Field Tests of the ESMER Multiphase Flowmeter, North Sea Flow Measurement Workshop, 1998
[31] Toral H., Cai S., Steven R.. et al. Characterization of the Turbulence Properties of Wet Gas Flow in a V-Cone Meter with Neural Nets. 22nd North Sea Flow Measurement Workshop, 2004
[32] Morrison G.L., Hall K.R., Holste J.C., et al. Comparison of Orifice and Slotted Plate Flowmeters[J]. Flow Measurement and Instrumentation, 1994, 5(2): 71-77
[33] Morrison G.L., Terracina D., Brewer C., et al. Response of a Slotted Orifice Flow Meter to an Air/Water Mixture[J]. Flow Measurement and Instrumentation, 2001, 12(3): 175-180
[34] Macek M.L.. A Slotted Orifice Plate Used as a Flow Measurement Device[D]. Texas A&M University, College Station, 1993
[35] Geng Yanfeng., Li Yuxing., Feng Shuchu, Study on a New Type of Sensor for Wet Gas Meter[A]. Advances in Multiphase Flow[C]. Hangzhou. 2004: 536-541.
[36]孙淮青,王建中.流量测量节流装置设计手册[M].第二版.北京:化学工业出版社,2005:4-5
[37]耿艳峰,冯叔初,郑金吾,等.基于槽式孔板的凝析天然气计量技术[J].仪器仪表学报,2006,20(8):873-876
[38]耿艳峰,冯叔初,郑金吾.槽式孔板的气液两相压降倍率特性[J].化工学报,2006, 57(5):1138-1142
[39] Geng Yanfeng, Zheng Jinwu, Shi Tianming. Study on the Metering Characteristics of a Slotted Orifice for Wet Gas Flow[J]. Flow Measurement and Instrumentation, 2006, 17(2): 123-128
[40]郑金吾,耿艳峰.海上含液天然气流量计开发[J].化工自动化及仪表,2006, 33(3): 74-77
[41] Geng Yanfeng, Zheng Jinwu, Shi Tianming, et al. Wet Gas Meter Development Based on Slotted Orifice Couple and Neural Network Techniques[J]. Chinese Journal of Chemical Engineering, 2007, 15(2):281-285
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