Abstract：
Instantaneously determining the type and amount of oil product passing through pipelines is one of the most critical operations in the oil, polymer and petrochemical industries. In this research, a detection system is proposed in order to monitor oil pipelines. The system uses a dual-energy gamma source of americium-241 and barium-133, a test pipe, and a NaI detector. This structure is implemented in the Monte Carlo N Particle (MCNP) code. It should be noted that the results of this simulation have been validated with a laboratory structure. In the test pipe, four oil products-ethylene glycol, crude oil, gasoil, and gasoline-were simulated two by two at various volume percentages. After receiving the signal from the detector, the feature extraction operation was started in order to provide suitable inputs for training the neural network. Four time characteristics-variance, fourth order moment, skewness, and kurtosis-were extracted from the received signal and used as the inputs of four Radial Basis Function (RBF) neural networks. The implemented neural networks were able to predict the volume ratio of each product with great accuracy. High accuracy, low cost in implementing the proposed system, and lower computational cost than previous detection methods are among the advantages of this research that increases its applicability in the oil industry. It is worth mentioning that although the presented system in this study is for monitoring of petroleum fluids, it can be easily used for other types of fluids such as polymeric fluids.
摘要：
即时确定通过管道的石油产品的类型和数量是石油中最关键的操作之一，聚合物和石化工业。在这项研究中,为了监测输油管道，提出了一种检测系统。该系统使用双能量伽玛源-241和钡-133，测试管，还有一个NaI探测器.该结构在蒙特卡罗N粒子(MCNP)代码中实现。应该注意的是，该模拟的结果已通过实验室结构进行了验证。在测试管道中，四种油品-乙二醇,原油,汽油,和汽油-以不同的体积百分比两两模拟。收到探测器的信号后,开始特征提取操作，以便为训练神经网络提供合适的输入。四个时间特征-方差，四阶矩,偏斜度,和峰度-从接收信号中提取，并用作四个径向基函数（RBF）神经网络的输入。实现的神经网络能够非常准确地预测每种产品的体积比。精度高,实施拟议系统的成本低，和较低的计算成本比以前的检测方法是本研究的优势之一，增加了其在石油工业的适用性。值得一提的是，尽管本研究中提出的系统用于监测石油流体，它可以很容易地用于其他类型的流体，如聚合物流体。
