基于BP神经网络的原油持水率检测仪
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摘要
针对传统电磁波传感器输出信号与持水率难以线性化的缺点,综合单片机和BP神经网络的优点,研制出基于MSP430F5529单片机和BP神经网络的原油持水率检测仪。通过MSP430F5529检测电磁波传感器输出相位差信号,经低通滤波和A/D转换后得到与持水率相对应的电压值,将实时温度和电压值送BP神经网络进行训练。该仪器首次将单片机与人工神经网络相结合用于原油持水率检测,具有响应时间短、精确度高、功耗低等优点,能方便实时地检测出原油持水率,具有很强的实用性。
Integrating the shortcoming that the output signal of the traditional electromagnetic sensor is difficult to linearize with the water-hold and the advantages of microcontroller and BP Neural Network,the crude water-hold detector of crude oil based on MSP430 F5529 MCU and BP Neural Network was developed.By detecting the phase shifted signals outputting from electromagnetic wave sensor from MSP430 F5529 and transforming the low-pass filter and AD into the voltage values which correspond to the water-hold,the real-time temperature and voltage values were sent to BP Neural Network to be trained.The instrument combines the microcontroller and artificial neural networks to detect the water-hold ofcrude oil for the first time,which has the advantages of short-response time,high accuracy and low-power consumption.Moreover,the water-hold of crude oil can be easily detected in real time by it,which is very practical.
Integrating the shortcoming that the output signal of the traditional electromagnetic sensor is difficult to linearize with the water-hold and the advantages of microcontroller and BP Neural Network,the crude water-hold detector of crude oil based on MSP430 F5529 MCU and BP Neural Network was developed.By detecting the phase shifted signals outputting from electromagnetic wave sensor from MSP430 F5529 and transforming the low-pass filter and AD into the voltage values which correspond to the water-hold,the real-time temperature and voltage values were sent to BP Neural Network to be trained.The instrument combines the microcontroller and artificial neural networks to detect the water-hold ofcrude oil for the first time,which has the advantages of short-response time,high accuracy and low-power consumption.Moreover,the water-hold of crude oil can be easily detected in real time by it,which is very practical.
引文
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[11]张冬至,夏伯锴,曾蕾蕾,等.基于多传感器技术的原油含水率预测模型研究[J].化工自动化及仪表,2006,33(04):61-63.
[12]曾蕾蕾,夏伯锴,刘彬.应用BP神经网络预测原油含水率问题的研究[J].中国测试技术,2006,32(4):25-27.
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[14]吴良海.基于粒子群优化RBF神经网络原油含水率预测[J].计算机仿真,2010,27(5):261-263.
[15]郑晓兰.应用BP神经网络预测原油含水率的研究[D].大庆:大庆石油学院,2010.
[2]王进旗,强锡富,于英华.基于相位法原油含水率仪的实验研究[J].计量学报,2004,25(4):366-368.
[3]曹学文,梁法春,林宗虎.神经网络在含水率预测中的应用[J].计量技术,2003,1(1):3-5.
[4]刘再斌,吴锡令.阵列电磁传感器测量气水层状流持水率实验研究[J].测井技术,2011,35(4):303-307.
[5]任磊,陈祥光,刘春涛.原油储罐油水界面测量方法的研究及应用[J].仪器仪表学报,2008,29(10):2186-2191.
[6]李高峰,付石,何帆,等.油水界面自动监测技术研究与应用[J].自动化与仪表,2012(6):21-25.
[7]余厚全,魏勇,汤天知,等.基于同轴传输线电磁波检测油水介质介电常数的理论分析[J].测井技术,2012,36(4):361-364.
[8]刘铁男,段玉波,刘志德,等.带优选聚类算法的RBF网络辨识器及应用[J].控制与决策,2003,18(2):233-236.
[9]林熙.基于RBF网络的模糊if-then规则快速提取[J].系统工程理论方法应用,2001,10(2):145-149.
[10]王倩.BP神经网络在脱气井产出剖面测井解释中的应用[J].测井技术,2016,40(2):234-238.
[11]张冬至,夏伯锴,曾蕾蕾,等.基于多传感器技术的原油含水率预测模型研究[J].化工自动化及仪表,2006,33(04):61-63.
[12]曾蕾蕾,夏伯锴,刘彬.应用BP神经网络预测原油含水率问题的研究[J].中国测试技术,2006,32(4):25-27.
[13]MONTAZER G A,SABZEVARI R,GHORBANI F.Threephase strategy for the OSD learning method in RBF neural net-works[J].Neurocomputing,2009,72(7-9):1797-1802.
[14]吴良海.基于粒子群优化RBF神经网络原油含水率预测[J].计算机仿真,2010,27(5):261-263.
[15]郑晓兰.应用BP神经网络预测原油含水率的研究[D].大庆:大庆石油学院,2010.